text:
Nicholas A. Gowen
U.S. Court of Appeals for the Seventh Circuit
U.S. District Court for the Northern District of Illinois (trial bar)
U.S. District Court for the Central District of Illinois
U.S. District Court for the Southern District of Illinois
U.S. District Court for the Southern District of Indiana
University of Illinois at Chicago, B.A., 2000
with honors; Phi Beta Kappa
University of Illinois College of Law, J.D., 2003
Nicholas Gowen is a trial attorney who represents Fortune 500 companies and middle market companies in commercial business disputes in courts and arbitration forums around the country. Nicholas serves as lead trial and appellate counsel in matters, including shareholder disputes, real estate litigation, trade secrets, employment matters, and other business disputes, representing global companies and entrepreneurs. Nicholas approaches litigation with an eye toward results; understanding that advancing his clients' litigation strategies should fit within their overall business interests.
Nicholas has been honored as one of Chicago's "Top 40 under 40 Game Changers" by Ariel Investments and The Urban Business Roundtable and selected by Crain's Custom Media as one of Chicago's Notable Minority Lawyers.
Nicholas received his B.A. in political science with honors from the University of Illinois at Chicago in 2000, where he was admitted to Phi Beta Kappa, and his J.D.,cum laude, from the University of Illinois College of Law in 2003.
Business and Commercial Litigation
Lead trial counsel representing a U.S. manufacturing company in an international arbitration defeating a Chinese metal packaging conglomerate in a $10 million contract dispute
Lead counsel representing global automotive supplier in a substantial contract dispute with a global customer that led to favorable settlement
Recovered millions of dollars on behalf of a U.S. based international investment firm against competitor for breach of an asset purchase agreement.
Lead trial and appellate counsel successfully defending a Chinese national in a breach of contract dispute related to the client's facilitating the issuance of an EB-5 Immigrant Investment Visa.
Recovered several million dollars on behalf of a computer software design company in breach of contract against a professional services provider
Trade Secret and Employment Litigation
Lead counsel representing closely-held company in prosecuting multiple successful actions to enforce restrictive covenants in employment agreements against former sales representatives
Successful prosecution of trade secret claims on behalf of electrical component manufacturer against two larger competitors. Negotiated favorable multimillion dollar settlement prior to empaneling jury
Lead counsel in multiple representations of corporations and (former) executives in disputes involving trade secrets, restrictive covenants in employment agreements, and non-solicitation agreements, in industries including: manufacturing, international customs brokerage, retail, architectural design, finance, technology, engineering, and sporting goods
Lead counsel representing closely-held company in internal investigation of sexual harassment and discrimination claims brought by employees against senior executives
Lead counsel in successful defense of private equity firm in multi million dollar contract dispute with a national bank
Lead counsel in successful defense of a commercial real estate investor in several multi million dollar disputes with a regional bank
Defeated claims brought by Brazilian investment fund alleging breaches of contractual and fiduciary duties and conversion against multiple investor clients
Recovered more than $25 million in fraud suit on behalf of a biopharmaceutical company against one of the largest U.S. financial institutions
Shareholder and D&O Litigation
Lead counsel defending chairman of a private equity firm and portfolio company in a derivative action brought by minority shareholders and executives of the portfolio company
Represented multinational corporation in Delaware Chancery Court on claims that defendants defrauded client relating to a $20 million sale of a manufacturing business. Case settled favorably for client
Lead Counsel in multiple representations defending private companies, majority shareholders, and officers in litigation involving ownership rights, distributing, and fiduciary duties in industries including real estate services, manufacturing, software development, hospitality, logistics, and commodities trading.
Real Estate / Construction Litigation
Lead trial counsel defeating TRO filed against real estate developer client by commercial tenant that sought to delay a $100 million redevelopment of a regional shopping center
Lead trial counsel securing arbitration victory and damages for construction-industry client in breach of contract dispute against one of the nation's largest construction companies
Lead trial counsel for municipality and stadium lessor in private arbitration defeating NFL team in claims involving the team's contractual rights
Defended construction industry client in a $10 million breach of contract dispute against residential building owner leading to favorable low-dollar settlement
Intellectual Property/ Unfair Competition Litigation
Lead counsel in successful defense of major research university in patent infringement litigation related to use of electronic equipment
Represented former executive in a trademark infringement action against the software company he founded
Obtained judgment on behalf of a Fortune 500 automotive supplier against a distributor for unauthorized use of trademarks
Represented entertainer in copyright infringement dispute with music producer over use of vocal arrangements
Represented technology company in false advertising claims against competitor based on misleading comparisons with products sold by our client
Successful representation of an American Civil Rights era-icon in right of publicity litigation
Lead counsel defeating breach of nine-figure fiduciary duty and malpractice claims against representing boutique law firm asserted by former client arising out of a failed partnership and patent licensing dispute
Defended AmLaw 25 firm in malpractice action arising out of the prosecution of patents for a medical device
Defeated tortious interference claims asserted by competitor against AmLaw 25 firm and its partners
Ariel Investments and The Urban Business Roundtable, honored as one of Chicago's Top 40 under 40 Game Changers
Crain's Custom Media, honored as one of Chicago's Notable Minority Lawyers for 2018
Illinois Super Lawyers, recognized as a Rising Star, 2008-2019
Chicago's Notable Minority Executives in Accounting, Consulting and Law for 2020
Chicago State University, Board of Trustees, Chairman (previously Vice-Chairman), (2017-2019)
Project H.O.O.D. Advisory Committee, Board of Directors (2012 – Present)
Illinois Equal Justice Foundation, Board of Directors (2018-Present)
Chicago Foundation for Education, Board of Directors (2019-2020)
University of Illinois at Chicago, Visiting Instructor, Political Science, (2005-2017)
Chicago Bar Association Judicial Evaluation Committee, Investigative Division, member
National Bar Association
o Commercial Law Section
o Federal Civil Practice Committee, Past Vice Chair/Chair
National Employment Law Council
American Arbitration Association, Commercial Litigation Panel Arbitrator
Partner Nick Gowen Named to Chicago's Notable Minority Executives in Accounting, Consulting and Law for 2020
Atty's Defeat Of Patent Portfolio Malpractice Claims Upheld
By Lauraann Wood
Law 360, August 20, 2020
Gowen Named Among "Chicago's Notable Minority Lawyers"
When A Key Employee Resigns, Joins Your Competitor And You Have Nothing In Place To Stop Them
Firm Attorneys Attend "Strike Out Cancer" Fundraiser
Employers Must Be Mindful Before Punishing Employees for Their Social Media Usage
Firm's Nick Gowen Appointed to Chicago State University's Board of Trustees by Gov. Bruce Rauner
Firm's Race Judicata Team
Project H.O.O.D. Annual Gala
Firm Welcomes Nicholas A. Gowen and Joseph Hylak-Reinholtz
Chicago Family Business Council, August 28, 2017
Attorneys need to be on guard in new cyber world
Chicago Lawyer Magazine, September 2015
Social media dos and don'ts
Without guidelines, employers can get into trouble in a hurry
Chicago Lawyer Magazine, June 2015
Getting the best of both worlds
Third-party litigation funding while protecting confidentiality
Chicago Lawyer Magazine, March 2015
Protecting Trade Secrets in the Cloud
The National Law Review, October 20, 2014
Recovering Damages for Trademark Infringement
Illinois Bar Journal, March 2013
Crafting Workable Social Media Policies for Corporate Clients
National Bar Association: Young Lawyers Division Newsletter, Summer 2012
Warning: Cloud Could Bring Storm
CPA Insider, February 27, 2012
Arbitration Conversation Episode 25: Nicholas Gowen, Partner at Burke, Warren, MacKay & Serritella
Territory Story - I Can't Breathe featuring Partner Nick Gowen
Territory Story - Australia-Based Podcast Features Partner Nick Gowen


text:
Q: How to detect users on an iPhone with "Private Browsing" enabled? My jquerymobile App requires the use of localStorage and sessionstorage e.t.c, I've been giving a prompt to users without cookie support and telling them to enable cookies, but if a user has private browsing enable, this create cookie test I'm doing doesn't work and they just get a still erroneous screen, does anyone know how I could test if the user has private browsing enabled?
Thanks

A: I don't have an Iphone to test this on, but in the desktop Safari browser (in private mode) running the below function does catch the error and handles it as expected.
function storageEnabled() {
    try {
        localStorage.setItem("__test", "data");
    } catch (e) {
        if (/QUOTA_?EXCEEDED/i.test(e.name)) {
            return false;
        }
    }
    return true;
}

if (!storageEnabled()) alert('localStorage not enabled');

Jsfiddle: http://jsfiddle.net/B9eZ5/

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}

Traditional newspapers have been in decline in recent years in terms of readership and revenue; in comparison, digital online news have been steadily increasing according to both metrics.\footnote{http://stateofthemedia.org/2012/overview-4/key-findings/}

Recent surveys have shown that about half of the population of the US gets their news online, and about one third goes online every day for news.\footnote{\url{http://www.people-press.org/2012/09/27/section-2-online-and-digital-news-2/}}

The study of patterns of consumption of online news has attracted considerable attention from the research community for over a decade. This research started with the analysis of access patterns to websites, and has expanded to include topics such as new engagement metrics, personalized news recommendations and summaries, etc. (see Section~\ref{sec:relwork} for an overview).

One line of research looks at consumption and interaction patterns as a single time series and attempts several prediction tasks on it. For example, predicting total comments from early comments~\cite{lee_2010_popularity,tatar_2001_predicting}, total visits from early visits~\cite{kim_2011_temperature}, etc. More recent works incorporate attributes from each specific article (e.g. topic, source, etc.) into the prediction~\cite{bandari2012pulse}.

We adopt a novel approach, in which we integrate different types of interactions of users with an online news article including visits, social media reactions, and search/referrals.
We evaluate our methods on data from Al Jazeera English, a large international news network, deeply characterizing different classes of articles, and predicting their total number of page views and their effective shelf-life (the effective shelf-life of an article is the time span during which it receives most of its visits).

The characterization and prediction of user behavior around news articles is valuable for a news organization, as it allows them (i) to gain a better understanding of how people consume different types of news online; (ii) to deliver more relevant and engaging content in a proactive manner; and (iii) to improve the allocation of resources to developing stories over their life cycle.

\mpara{Our contributions.}
In this paper we present a qualitative and quantitative analysis of the life cycle of online news stories. Our main contributions are the following:
\begin{itemize}
 \item We find that social media reactions can contribute substantially to the understanding of visitation patterns in online news.
 \item We characterize two fundamental classes of news stories: breaking news and in-depth articles, and describe the differences in users' behavior around them.
 \item We describe classes of short-term audience response profiles to news articles in terms of visits and social media reactions (decreasing, steady, increasing, and rebounding).
 \item We improve significantly the accuracy and timeliness of predictive models of total visits and shelf-life of articles, by incorporating social media reactions.
\end{itemize}

The remainder of this paper is organized as follows. Section~\ref{sec:relwork} provides an overview of previous works related to ours. Section~\ref{sec:framework} introduces our data collection and defines the concepts and variables we use. The main results of our paper are presented as descriptive and predictive analysis in the two following sections: Section~\ref{sec:classification} describes user behavior with respect to different classes of articles, and Section~\ref{sec:prediction} demonstrates the importance of incorporating social media information into the predictive modeling of visits. The last section concludes the paper.

\section{Related work}\label{sec:relwork}

One of the earliest published studies of user behavior in online news was conducted by Aikat~\shortcite{aikat_1998_news}, who studied the web sites of two large newspapers from November 1995 to May 1997. This work describes many of the patterns still seen in news sites today: visits occur mostly during weekdays and working hours; readers ``skim'' pages for information so dwell times tend to be short, and there are clear traffic ``bursts'' that can be attributed to specific news developments.

With the advent in recent years of what can be considered as new forms of journalism (blogs) and new propagation mechanisms for news (micro-blogs and online social networking sites), the volume of research publications in this area has increased considerably. In this section we overview a few previous works closely related to ours, but our coverage is by no means complete.

\spara{Behavioral-driven article classification.} Previous works including~\cite{crane_2008_response,lehmann_2012_dynamical} that have studied online activities around online resources (e.g. visiting, voting, sharing, etc.), have consistently identified broad classes of temporal patterns. These classes can be generally characterized, first, by the presence or absence of a clear ``peak'' of activity; and second, by the amount of activity before and after the peak.

Crane and Sornette~\shortcite{crane_2008_response} describe classes of visitation patterns to online videos, and present models that are consistent with propagation phenomena in social networks.
Lehmann et al.~\shortcite{lehmann_2012_dynamical} extend these classes by observing that for Twitter ``hashtags'' (user-defined topics) the distributions of activity in different periods (before/during/after) induce distinct clusters of activity that can be interpreted considering the semantics of each hashtag. Romero et al.~\shortcite{romero_2011_differences} describe how manually-assigned classes of hashtags are related to different shapes of the {\em exposure curve}: the probability that a user will propagate some information (``retweet'' in the case of Twitter) after being exposed to the information by a certain number of her neighbors.

Yang and Leskovec~\shortcite{yang_2011_patterns} describe six classes of temporal shapes of attention. Attention is measured in terms of the number of appearances of a given phrase (of a variation of it) corresponding to an event. The patterns describe the distribution of attention over time, as well as the ordering in which different types of media (professional blogs, news agencies, etc.) ``break'' the story.

In general, previous works have established that the evolution of the popularity of different on-line items depends on their class. Figueiredo et al.~\shortcite{figueiredo_2011_tube} describe how YouTube videos that are posted to a ``top'' page on the website, and videos that are making use of professionally produced content, are different from randomly-chosen videos in terms of their visit patterns.

Recently, researchers at URL shortening service Bit.ly~\cite{bitly_2012_halflife} described how an article's half-life (see definition in Section~\ref{sec:framework}) is affected by topics, extending a previous observation than in general there are some topics that are more time-sensitive than others~\cite{gharan_2010_memes}. For instance, business-related articles have on average a longer half-life, while articles related to politics/celebrities/entertainment have an intermediate one. Sports-related articles have in comparison a shorter half-life. Previously, Bit.ly researchers~\cite{bitly_2011_halflife} have shown that this half-life is also affected by the social media platform where the link is first posted (e.g. links  on Facebook were longer-lived than links on Twitter)

\begin{table}[t]
\caption{Selected references on predictive modeling of user behavior, sorted by publication year.}
\hskip-0.5em
\scriptsize\begin{tabular}{p{0.79in}p{0.40in}p{1.75in}}\hline
Reference & Collection & Input / Output \\\hline\hline
Tatar et al. \shortcite{tatar_2001_predicting} & 20 \newline Minutes & {\em input}: publication hour, number of comments after a short time, section; {\em output}: total number of comments \\\hline
Brody, Harnad, and Carr \shortcite{brody_2006_citations} & arXiv \mbox{pre-prints} & {\em input}: short-term article downloads; {\em output}: long-term article citations\\\hline
Lee, Moon, and Salamatian \shortcite{lee_2010_popularity} & DPReviews / Myspace & {\em input}: time to first-comment, inter comment arrival stats; {\em output}: time to last comment\\\hline
Lerman and Hogg \shortcite{lerman_2010_news} & Digg & {\em input}: visits; {\em output}: parameters of models that consider examination and promotion patterns\\\hline
Kim, Kim, and Cho \shortcite{kim_2011_temperature} & Blogs & {\em input}: clicks on first 30 minutes; {\em output}: clicks until end of lifetime\\\hline
Yu, Chen, and Kwok \shortcite{yu_2011_predicting} & Facebook pages & {\em input}: content and media type; {\em output}: number of FB likes/shares of each post\\\hline
Lakkaraju and Ajmera \shortcite{lakkaraju_2011_attention} & Facebook pages & {\em input}: text- and other characteristics of the posting and the page; {\em output}: number of FB likes/shares of each post\\\hline
Szabo and Huberman \shortcite{szabo2012predicting} & YouTube and Digg & {\em input}: views (Y), votes (D) in first 10d (Y), 2h (D); {\em output}: total number of views/votes \\\hline
Bandari, Asur, and Huberman \shortcite{bandari2012pulse} & News \newline aggregator & {\em input}: text analysis incl. topics, named entities, subjectivity, etc., source popularity; {\em output}: tweet count\\\hline
Ruan et al. \shortcite{ruan_2012_prediction} & Tweets & {\em input}: topics, past tweets, content features, user features, etc.; {\em output}: tweet count for a given topic\\\hline
Pinto, Almeida, and Gon\c{c}alves \shortcite{pinto_2013_predicting} & YouTube & {\em input}: time series of views in first 7 days; {\em output}: number of views after 30 days \\\hline
Ahmed et al. \shortcite{ahmed_2013_predicting} & YouTube, Digg, Vimeo & {\em input}: views (Y, V)  and votes (D) over time; {\em output}: predict future popularity
\\\hline
\end{tabular}
\label{tbl:relwork-pred}
\end{table}

\smallskip
We deepen and complement previous works on behavioral-driven characterization of online content, by describing the life-cycle of online news articles considering their visitation patterns as well as their social media reactions.

\spara{Prediction of users' activity.} The prediction of the volume of user activities with respect to on-line content items has attracted a considerable amount of research. This is attested by a number of papers, some of which are outlined in Table~\ref{tbl:relwork-pred}. Another active topic that is closely related, but different, is that of predicting real-world variables such as sales or profits using social media signals (e.g. \cite{gruhl_2005_predictive} and many others).

Over the years, the models used to predict user behavior in social media have increased in complexity. For instance, Bandari et al.~\shortcite{bandari2012pulse} and Ruan et al.~\shortcite{ruan_2012_prediction} incorporate into their models features extracted from the content of the articles, such as topics. Yin et al.~\shortcite{yin_2012_straw} study voting behavior over on-line contents and describe a model that considers that users are divided into two populations: a group that follows the majority opinion, and a group that does not. Myers et al.~\shortcite{myers_2012_external} study models that describe user activity in terms of information propagations, including the presence of external influences, e.g. traditional media sources that can reach vast audiences, such as television networks. Huang et al.~\shortcite{huang_2012_predicting} consider an online model of social activities that evolves over time as more information becomes available.

\smallskip
In contrast with previous works, we focus on the dynamic relation between social media reactions and visits over time, and show that both are useful to understand the differences among classes of articles and to predict future visit patterns.

\spara{Analysis of news visits and social media responses.}
Dezso et al.~\shortcite{dezso_2006_dynamics} analyze the visits to a large news portal in Hungary. One aspect they study which is closely related to our work is the half-life of articles, which is shown to be distributed according to a power-law across a broad range, with a mean of 36 hours. Agarwal et al.~\shortcite{agarwal_2012_multi} study the actions users perform after reading an article, which include printing, commenting, rating, and sharing through e-mail or social media. Their focus is on performing personalized recommendations, but they also uncover that article topics have an effect on the probability of each action, with a division between articles users read privately and articles they share publicly: ``Users tend to share articles that earn them social prestige and credit but they do not mind clicking and reading some salacious news occasionally in private.''

Social media reactions to traditional news media can vary not only in volume but also qualitatively. Hu et al.~\shortcite{hu_2011_event} record tweets during the broadcast of a speech of the US President. They observe that many tweets refer to the speech in general, except for certain topics which are discussed in more detail.

Finally, social media optimization company SocialFlow describes in a whitepaper~\cite{lotan_2011_news} a comparative study of social media responses to several large media outlets: Al Jazeera, BBC News, CNN, The Economist, Fox News and The New York Times. Among other findings, they note that the probability that a user clicks on a tweet is higher for The Economist ($\approx 19\%$) than for Fox News ($\approx 16\%$), Al Jazeera ($\approx 11\%$) or The New York Times ($\approx 4\%$). However, followers of Al Jazeera are almost twice as likely to retweet article links than followers of the other channels.

\smallskip
In contrast with previous works, we consider jointly traffic to the website and social media reactions, as both constitute acts in which users engage with the news content. Additionally, we quantify the richness of Twitter messages over time measuring entropy and counting unique tweets, and show that these variables are key to more accurate predictions of future visits.

\section{Context and dataset}\label{sec:framework}

In this section we provide some context to our research and describe the dataset that will be used on the remainder of the paper.

\subsection{Traditional news and social media} \label{sec:framework-aje}

Our dataset is provided by Al Jazeera English,\footnote{\url{http://www.aljazeera.com/}} a well-established news organization that reaches hundreds of millions of viewers through its TV channel.
Their website
is divided into five major sections: News, In-Depth, Programmes, Sports, and Weather -- plus a collection of blogs, which is outside the scope of this study. Approximately 40 editors/producers work on the areas of News, In-Depth, and Programmes.

The editors of Al Jazeera English maintain Facebook and Twitter\footnote{Currently Facebook and Twitter are the two most frequent sources of social media referrals to Al Jazeera. Reddit appears in a third place but only through few articles having extremely high visibility.} accounts (we call them ``corporate accounts'' in the rest of the paper) and use them actively to announce their content.
This seems to be a standard practice adopted by all major media organizations in recent years.
Each account
({\tt facebook.com/aljazeera} and {\tt @AJEnglish})
has over 1.5 million followers as of May 2013. Using these accounts, articles in the News section are shared immediately after being posted online. Articles on the In-Depth and Programmes sections are shared throughout the day with the goal of maximizing audience reach across multiple time zones.

The corporate social media accounts re-share articles at different times of the day, sometimes up to 4 times, on a schedule determined by editors' judgment and designed to increase user engagement. Close attention is paid to the wording of the items posted in social media, including aspects such as their length and the use of hashtags in the case of Twitter. Editors use a variety of online tools to obtain low-latency analytics of traffic and social media, and to decide which hashtags and keywords to use in their postings.

More than half of the visitors to the Al Jazeera English website are from the USA, the United Kingdom, or Canada. According to an online survey taken by Al Jazeera English in 2011 ($n=4,500$), 18\% of respondents said they used Twitter, 42\% Facebook, and 12\% both.

Social media interactions and traffic to the website can complement or substitute each other. Most frequently, they complement each other: people click on the shared content and visit the website. Sometimes, the social media share can be a substitute for a visit to the article, such as when a video can be viewed directly on the social media site, or when the social media content itself delivers enough information to satisfy users without requiring them to click through to the full article.

For instance, the news ``Pakistan's Malala now able to stand in UK'' (19 Oct 2012) generated an unusually large number of shares on Facebook, but comparatively little traffic on the website. At the time, the student-activist was being treated from nearly-fatal wounds received ten days before, and it is likely that users who were following the story just wanted to express their relief or satisfaction at her recovery.

In summary, for Al Jazeera and for most large news organizations, social media is important both because it attracts more visitors to their website than any other external referrer, as well as because it provides more platforms in which to have an audience. Hence, many news organizations adopt an active role in social media in order to increase this positive effect.

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.9\columnwidth]{fig/sum_series}
\caption{Overall visits to articles. Our study considers all articles posted from Oct. 8th to Oct 29th, 2012 (the graph extends until Nov. 6th).}
\label{fig:overall}
\end{figure}

\subsection{Data collection} \label{sec:data-collection}

We focus on a period of three weeks between October 8th, 2012, and October 29th, 2012. The choice of this period is not random: it was a relatively stable period of traffic, only exhibiting a relatively minor peak on October 29th due to Hurricane Sandy. Figure~\ref{fig:overall} depicts the frequency of visits to all the articles in our dataset during the observation period.

The data collection is done via a ``beacon'' embedded in all article pages; this produces events that are processed using Apache S4,\footnote{\url{http://incubator.apache.org/s4/}} a high-performance system for online processing, which is used to collect and aggregate the visits with a 1-minute granularity. For efficiency reasons, only articles obtaining at least 5 visits in a 10-hour window are monitored. The collected data is stored using a Cassandra\footnote{\url{http://cassandra.apache.org/}} NoSQL database.

Our system also collects messages from Facebook (using the Facebook Query Language API) and Twitter (using their Search API). Both platforms have strict limitations on polling frequencies, which impose a trade-off between the number of articles we can monitor and the frequency with which we monitor them. To obtain more accurate results for popular articles, and after experimenting with different settings, we decided to poll social media reactions for articles that are within the list of the 30 most visited articles during each five-minute data collection window. We remark that this list varies considerably over time.

We selected a uniform random sample of articles whose first visit was recorded during the observation period, and kept only those accumulating at least 100 visits during their first week after publication.
A total of 606 articles was included; this covers over 3.6 million visits and at least 235,000 social media reactions.
Table~\ref{tbl:overall} presents some summary statistics on this dataset.

\begin{table}[t]
\caption{Summary statistics of our dataset.}
\label{tbl:overall}
\centering
\scriptsize\begin{tabularx}{1.0\linewidth}{X|rr}\hline
 & Total & Article avg. \\\hline\hline
 Number of articles     & 606   & -\\
 Visits after 1 hour    & 260 K & 430   \\
 Visits after 1 day     & 2.5 M & 4,273 \\
 Visits after 7 days    & 3.6 M & 5,971 \\ \hline
 Facebook shares        & 155 K & 256 \\
 Tweets                 & 80 K  & 133 \\
 Tweet entropy                   & & 5.6 bits \\
 Fraction of unique tweets       & & 19.9 \%  \\
 Fraction of corporate retweets & & 36.8 \%  \\ \hline
 \end{tabularx}
\end{table}

\subsection{Metrics}\label{subsec:variables}

For each article we collected a number of metrics regarding user visits and social media reactions. First, we observed at a granularity of one minute the number of visits (page views) to each article, and the URL of the previous page seen by the users before reaching an article (referral). We bucketed the latter into four classes:

\begin{compactitem}
\item internal links, mostly from the home page of the website: these are the majority of the traffic sources and comprise 70\% of the visits;
\item external links from other sources including social media sites, news aggregators, and others: 14\%;
\item direct links, which have an empty referral and correspond mostly\footnote{\url{http://www.theatlantic.com/technology/archive/2012/10/dark-social-we-have-the-whole-history-of-the-web-wrong/263523/}} to people sharing news through instant messaging, e-mail, or other non-web application: 11\%; and
\item search referrals, basically links from organic search results: 5\%.
\end{compactitem}

We remark that this distribution of referrals corresponds to the articles in our sample, which do not include the homepage of the website, section index pages, or older articles. If we take those into account, the numbers are different, e.g. the search referrals account for 30\% of the visits.

We also collected periodically the number of times an article has been shared on Facebook, and the content of any Twitter message containing the URL of the article, or a variant of the URL produced by a URL shortening service. We used this data to compute the following variables:

\begin{compactitem}
 \item Number of Facebook shares per minute (interpolated).
 \item Number of tweets per minute.
 \item Number of unique tweets per minute. A tweet is deemed {\em unique} if its edit distance with all previous tweets pointing to the same article (after discarding shortened URLs and ``retweet'' prefixes) is more than 10 characters.
 \item Tweet vocabulary entropy. To compute this, at any given point in time we create a document by concatenating all the tweets received up to that time. Then, we compute the entropy of the distribution of terms in that document.
 \item Number of corporate retweets per minute. A tweet is a ``corporate retweet'' if it includes
 ``RT @AJEnglish'' or ``RT @AJELive''
 in its text. A tweet can be both corporate retweet and unique, as users are free to edit the retweet before posting it.
 \item Number of followers, friends (followees) and statuses of each of the users posting a tweet.
\end{compactitem}

\begin{table}[t]
\caption{Top 10 most frequent words (stemmed and lowercased) in article titles in the ``News'' and ``In-Depth'' sections. Words that appear in both lists are italicized.}
\label{tbl:titles}
\small\centering\begin{tabular}{lcc|lcc}\hline
\multicolumn{3}{c|}{Top words (News, $n=322$)} & \multicolumn{3}{c}{Top words (In-Depth, $n=139$)} \\\hline\hline
Word	&	News	&	In-Depth	&	Word	&	News	&	In-Depth \\\hline
{\em us}	&	34	&	17	&	{\em us}	&	34	&	17	\\
kill	&	21	&	1	&	pictur	&	0	&	10	\\
attack	&	19	&	0	&	obama	&	6	&	6	\\
{\em syria}	&	15	&	4	&	interact	&	0	&	6	\\
dead	&	15	&	1	&	america	&	0	&	6	\\
protest	&	13	&	0	&	muslim	&	0	&	5	\\
rebel	&	12	&	2	&	{\em syrian}	&	11	&	4	\\
vote	&	11	&	3	&	{\em syria}	&	15	&	4	\\
{\em syrian}	&	11	&	4	&	presid	&	4	&	4	\\
pakistan	&	10	&	2	&	polici	&	2	&	4	\\
\end{tabular}
\end{table}

\section{Behavioral-Driven Classes}\label{sec:classification}

In this section we describe classes of articles according to patterns of user behavior.

\subsection{News vs In-Depth}

We observe that articles in the two larger sections of the Al Jazeera English website trigger distinct user behavior patterns: visits and social media reactions on articles in the News section (322 articles in our sample) are different from the ones on articles in the In-Depth section (139 articles).

\iffalse
We depict in Figure~\ref{fig:tagclouds} the most frequent terms in the titles of articles in these two sections -- as in e.g. Lehmann et al.~\shortcite{lehmann_2012_dynamical}.
Articles in the News section are dominated by current events, such as the conflict in Syria for this period of time, while articles in the In-Depth section are dominated by photos and analyses of some topics.

\begin{figure}[t]
\centering\includegraphics[width=.8\columnwidth]{fig/tagcloud_news}
\includegraphics[width=.8\columnwidth]{fig/tagcloud_indepth}
\caption{Most frequent words in article titles in the ``News'' (top) and ``In-Depth'' (bottom) sections.}
\label{fig:tagclouds}
\end{figure}
\fi

\spara{Titles.}
Table~\ref{tbl:titles} includes the most frequent words in titles of articles in these two sections, after converting to lowercase and applying Porter's stemmer.\footnote{\url{http://snowball.tartarus.org/algorithms/porter/stemmer.html}}
While in our sample the US and Syria appear prominently in both sections, articles in the News section include several violent acts, while articles in the In-Depth section are dominated by photos and political analysis. A chi-squared test comparing the entire distributions shows $p<10^{-13}$, rejecting the hypothesis that they are equal.

\iffalse
\begin{figure}[t]
\centering\includegraphics[angle=-90,width=\columnwidth]{fig/views_shares-20121017135037325310}

\includegraphics[angle=-90,width=\columnwidth]{fig/views_shares-www_aljazeera_com_indepth_opinion_2012_10_201210993632838545}
\caption{Visits per minute (left y-axis) as well as Tweets and Facebook shares per minute (right y-axis) for the first 12 hours. Top: typical profile for a News item: ``Pirates abduct ship's crew off Nigerian coast'' (Oct 17th, 2012). Bottom: an In-Depth item: ``US corn ethanol fuels food crisis in developing countries'' (Oct 9th, 2012).}
\label{fig:series-news}
\end{figure}

Figure~\ref{fig:series-news}~(top) depicts the typical series for some of the variables in an article in the News section. Time is expressed in {\em hours-equivalent}, which are hours corrected by the seasonality (day-night, weekday-weekend) of traffic on the website, as in~\cite{szabo2012predicting}.
We can see that initially there are a number of visits and activity on Twitter and Facebook, that decays rapidly after a short time. This is often the pattern in news media as observed e.g. by~\cite{dezso_2006_dynamics,lotan_2012_aurora} and others. After three or four hours, almost all the visits can be explained by ``internal traffic'', i.e. visitors arriving from the homepage of the site. When the news article is displaced from the homepage by more recent items, after about nine hours in this example, its traffic slows down considerably.

The profile of In-Depth articles can be much more complex. Figure~\ref{fig:series-news}~(bottom) depicts one such example, where a sustained level of visits is observed during several hours.
\fi

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.8\columnwidth]{dat/average_profile/avg_news}

\includegraphics[angle=-90,width=.8\columnwidth]{dat/average_profile/avg_indepth}
\caption{Visits per minute (left y-axis) as well as Tweets and Facebook shares per minute (right y-axis) for the first 12 hours. For visits, the shaded area covers 50\% of the data (quantiles 0.25 to 0.75). Top: average  for a News item. Bottom: average  for an In-Depth item.}
\label{fig:series-news}
\end{figure}

\spara{Visits.}
Figure~\ref{fig:series-news}~(top) depicts the average time series of some variables for articles in the News section. Time is expressed in {\em hours-equivalent}, which are hours corrected by the seasonality (day-night, weekday-weekend) of traffic on the website, as in~\cite{szabo2012predicting}.
Initially there are a number of visits and activity on Twitter and Facebook, that decays rapidly after a short time. This is often the pattern in news media as observed e.g. by~\cite{dezso_2006_dynamics,lotan_2012_aurora} and others. After a few hours, a large amount of visits can be explained by ``internal traffic'', i.e. visitors arriving from the homepage of the site. For most articles, once the news article is displaced from the homepage by more recent items, its traffic slows down considerably.

The profile of visits to In-Depth articles can be more complex. Figure~\ref{fig:series-news}~(bottom) depicts the average series for these articles. We can observe that a sustained level of visits is observed during several hours, as the contents of these articles are not as time-sensitive as those of the News section. We remark that in both cases (News and In-Depth) there is considerable variability from one article to another.

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.72\columnwidth]{fig/scatter_v1h_v7d}
\caption{Visits in the first hour versus visits on the first week for articles in the two largest categories. A simple function that assumes that visits after seven days are a multiple of visits after one hour has been included, by performing a least-squares fit in the central portion of each distribution.}
\label{fig:scatter_v1h_v7d}
\end{figure}

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.72\columnwidth]{fig/scatter_facebook_twitter}
\caption{Differences in the distribution of Facebook vs Twitter shares. On average the ratio of Facebook shares to tweets is 1.9:1 (1.6:1 for News, 2.7:1 for In-Depth). The result of a least-squares fit in the central portion of each distribution is included.}
\label{fig:scatter_facebook_twitter}
\end{figure}

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.72\columnwidth]{fig/hist_tweets_unique_fraction}
\caption{Differences in the distribution of the fraction of unique tweets. In both cases, Twitter activity is dominated by re-tweets or repetitions of the same tweets, but In-Depth articles attract more unique tweets.}
\label{fig:hist_tweets_unique_fraction}
\end{figure}

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.72\columnwidth]{fig/hist_tweets_corporate_retweets_fraction}
\caption{Differences in the distribution of fraction of corporate retweets. In-Depth articles have a larger share of re-tweets from the @AJEnglish and @AJELive accounts.}
\label{fig:hist_tweets_corporate_retweets_fraction}
\end{figure}

News items compared to In-Depth items have a more intense first hour, as can be seen in Figure~\ref{fig:scatter_v1h_v7d}. For News, visits in one hour are roughly $1/12$th of the visits in the first week, while for In-Depth they are on average around $1/29$th. The two groups are similar to ``promoted'' (homepage) and ``not-promoted'' stories in Digg as observed in~\cite{szabo2012predicting}.

This difference in behavior can to some extent be explained by the design of the website. News articles are displayed more prominently on the home page, with the most salient location being typically used by a news item; however, In-Depth articles are also visible across the website, including a prominent slot on the top right corner of every page. Additionally to the differences in social media sharing that we discuss next, we observe that long-lived News articles (in terms of effective shelf-life as defined in Section~\ref{sec:shelf-life}) tend to include analysis that would actually make them fit for the In-Depth section. Indeed, the top-3 longer lived News articles in our observation period are ``Profile: Malala Yousafzai'' (Oct 10th, 2012), ``Syrian rebels in uneasy alliances'' (Oct 25th, 2012), and ``Malala is the daughter of Pakistan'' (Oct 13th, 2012); their contents, while motivated by specific news events such as the Syrian conflict and the shooting of a school girl, do not describe the events but rather the context in which they are taking place.

\spara{Social media.}
On average the ratio of Facebook shares to tweets per article is 1.9:1, which is to some extent consistent with the survey described in Section~\ref{sec:framework-aje} that indicated that there were twice as many website visitors using Facebook as there were Twitter users. Additionally, In-Depth articles are shared more on Facebook given the same level of activity on Twitter, as shown in Figure~\ref{fig:scatter_facebook_twitter}. On average News articles have 1.6 Facebook shares per tweet, while In-Depth articles have~2.7.

As shown in Figure~\ref{fig:hist_tweets_unique_fraction} there is also a difference in the number of unique tweets. On average, 17\% of the tweets about News articles are unique, versus 25\% of the tweets about In-Depth articles. This means that a majority of users do not change the content of the tweets when clicking on the ``tweet'' button next to the articles, or when retweeting from another Twitter user.

There is also a difference in the number of corporate retweets, as shown in Figure~\ref{fig:hist_tweets_corporate_retweets_fraction}. On average, 27\% of tweets about News articles are corporate retweets, compared to 44\% of tweets about In-Depth articles. This means that for In-Depth articles a larger share of Twitter activity can be attributed to users who are followers of @AJEnglish or @AJELive, and thus are probably more engaged with these Twitter accounts.

Anecdotally, we know that editors spend more time crafting tweets to promote In-Depth articles than News articles, given that the former are not as time sensitive as the latter. In the case of News, the headline is often posted without modifications to Twitter, which may produce a comparatively less appealing tweet.

\subsection{Analysis of news articles} \label{sec:qualitative_news}

We observe that News articles have attention profiles  that are quite predictable, while In-Depth and other article categories show significantly more variability.
We focus on the first 12 hours-equivalent after publication of each article in the News section, and observe the time series of data from all sources including internal links, external links, search engines, and social media (similarly to the time series shown in Figure~\ref{fig:series-news}, but for each individual article instead of as an average). We then classify articles into several classes based on visit patterns that are apparent from these observations, starting with the largest class (``{\em decreasing}'') and following with the other classes. The classification is done by the authors seeking consensus and discussing borderline cases.

At a high level, the classes of articles in our News sample can be roughly described by an ``80:10:10 rule''. The traffic to $\sim$80\% of the articles decreases monotonically during the first 12 hours, the traffic to $\sim$10\% does not decrease, and the traffic to the remaining $\sim$10\% decreases first, but then rebounds. Articles on each  are listed in Appendix~\ref{app:examples}.

Next we provide a brief description of each class and examples of stories appearing in each of them. We remark that in this work we do not attempt to provide a comprehensive content-based typology for news articles within each attention profile.

\mpara{Decreasing (78\%).}
The largest article class represents about 78\% of the sample set. Articles in this class demonstrate an initial spike in visits following article publication, followed by a rather consistent drop in the number of visits, either immediately (244 articles), or after a short delay (7 articles).

Delayed onset traffic decreases have been observed before, such as in~\cite{lotan_2012_aurora} with respect to the shooting in Aurora, Colorado, in 2012.
This attention pattern can often be attributed to breaking news that resonates with readers located in a time zone that is off-peak when the article is first posted, such as when that portion of the audience is mostly asleep. A story about Hurricane Sandy's movement up the East Coast of the United States, for example, sees an initially sharp visit growth that begins to decline as the East Coast retires for the evening.

The predominance of this class of article indicates that while news itself occurs, and can even be covered, at a constant rate, in most cases readers will only be interested on a news article for a brief period of time after its publication.

\mpara{Steady or Increasing (12\%)}
Roughly 9\% of the sample's articles retain relatively constant visitor rates during their first 12 hours. Compared to news categories with very short shelf-lives, such as sports news, these articles are remarkably consistent. In this subset of news articles, dramatic news and emotional stories appear to garner Facebook shares and, often as a result, extended shelf-lives.

In the U.S., multiple articles on Obama and Romney's sharp-tongued presidential debate drive consistent Facebook and Twitter responses for a relatively long period of time following the articles' publication. A poll on racism in the US has similar staying power and Facebook sharing. In Central Asia, the Taliban attack on Pakistani schoolgirl Malala appears in a number of these articles, where consistent Facebook sharing buoys the article traffic beyond average shelf-life. In Europe, furor over a seismology scandal is posted to Facebook, while in the Middle East, atrocities in the war in Syria and violence between Israel and Hamas also generate hours of steady traffic. Africa sees a new prime minister in Libya, the police shooting of 34 striking miners in South Africa, and a bomb attack on a church in Nigeria, all of which see sustained traffic thanks in part to significant Facebook and Twitter sharing many hours after their initial publication.

Stories in this group were mostly developing stories and many of them had regular updates. One such example is the story about Malala for which Al Jazeera sent a correspondent to the Swat Valley. Being a complex region to cover, a series of news articles and feature stories were written. In addition, Al Jazeera reached out to the Reddit community for a Q\&A session which topped the ``Ask me anything (AmA)'' section\footnote{\url{http://www.reddit.com/r/IAmA/comments/11p6q3/i_am_asad_hashim_journalist_for_the_al_jazeera/}}.
section.

A relatively small number of articles (3\% of our sample) buck the usual trend and see increased page traffic as time passes after their publication, rather than a decline. To the extent that these articles can be generalized, they resemble the class of articles detailed above. Some of these articles were also updated with supporting content. For at least half of them, web producers added video packages after publication, which may explain to some extent the increase in visits.

\mpara{Rebounding (10\%).}
About 10\% of the articles in our sample initially exhibit a decline in visits/minute, until a point where such decline is reversed. This ``rebound'' occurs either because of internal or external links.

In the case of internal traffic, the traffic patterns behind these rebounding articles sometimes reflect the common newsroom practice of linking to previous coverage in more recent articles. This practice provides additional background context to readers just arriving at the story, but also helps news organizations extract additional value from articles that are otherwise statistically becoming valueless. Stories that required a significant investment of resources to produce are also promoted more heavily than regular articles.  We can see that in these cases, these internal links do indeed deliver readers to articles whose shelf-lives have nearly expired, when measured by homepage and social media traffic.

The articles that rebound as a result of external traffic are beneficiaries of attention directed from outside of the news organization (e.g. a social networking site, the website of another news network, etc.). Typically each observed burst in external web traffic can be tracked to a single source. Breaking stories can also gain visits as ongoing developments drive significant additional interest. This phenomenon is evidenced, for instance, by three rebounding articles tracking Hurricane Sandy's descent upon the United States.

\bigskip
In general, we see that when News articles cover topics that stray from ``hard news'', the article's attention profile reflects the increased variability seen in the In-Depth pieces. For example, some articles ostensibly cover specific actualities, but also bridge into long-standing issues: in the U.S., ``Immigrant family in pursuit of the American Dream'' and ``Living the modern American Dream'' stoke passions around immigration.

The sometimes blurry line between reporting on immediate actualities and longer-term trends like immigration is an area of tension in journalism, one identified by Galtung and Ruge when they asked ``how do 'events' become 'news'''?~\cite{galtung_1965_foreign}.

\section{Improving Traffic Predictions\\Using Social Media Data}\label{sec:prediction}

An increased amount of social media reactions is often correlated with more traffic to online articles. This is particularly marked in the case of non-decreasing and rebounding News articles, as well as In-Depth articles whose visitation patterns are more varied and less predictable than regular (decreasing) News articles. In this section, we combine social media reactions with early visitation measures to provide improved predictions of (i) the volume of visits to an article after 7 days from its publication and (ii) the effective shelf-life of articles, i.e. the time during which they will receive most of their visits. We begin by fitting models to our sample data, and then explore the practicality of this approach for new data.

\subsection{Modeling visiting volume}

Our first goal is to determine to what extent social media reactions can improve the prediction of the overall popularity (total number of visits) of an article. The dependent variable that we want to describe with our models is the total number of visits after 7 days ($v7d$). We use a straightforward approach to answer this question---linear regression models. We include the following variables (described in Section~\ref{subsec:variables}) as observed at the time at which the prediction is performed: number of visits ($v$), number of visits from link referrals ($vr$) and from ``direct'' traffic from e-mail/IM ($vd$), shares on Facebook ($f$), Twitter ($t$), mean number of followers of people sharing on Twitter ($\textrm{\em{foll}}$), entropy of tweets ($ent$), number and fraction of unique tweets ($uni$, $unip$) and fraction of corporate retweets ($cp$).
We use a linear regression model that includes all first-order effects as well as second order interactions. We included second-order interactions because of the interdependency of the variables (e.g. an article with more visits is more likely to have more social media reactions):

\begingroup
\setlength{\thinmuskip}{0mu}
\setlength{\medmuskip}{-1mu}
\setlength{\thickmuskip}{-1mu}
\noindent
$lm(vis7d \sim v)$

\noindent
$lm(vis7d \sim (v+vr+vd+f+t+\textrm{\em{foll}}+ent+uni+unip+cp)^2)$.
\endgroup

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.9\columnwidth]{fig/accuracy_pred_visits}
\caption{Proportion of explained variance ($r^2$) for the prediction of total volume of visits, for News and In-depth articles.}
\label{fig:accuracy_pred_visits}
\end{figure}

\begin{table}[t]
\caption{Modeling visiting volume after 7 days: Significance levels for regression models after 20 minutes.}
\label{tbl:significance}
\setlength{\tabcolsep}{0.5em}
\hskip-0.5em
\small\begin{tabularx}{1.02\linewidth}{Xrlrl}\hline
Variable & \multicolumn{2}{c}{In-depth} & \multicolumn{2}{c}{News} \\\hline\hline
Facebook shares & 0.0349 & * & 0.0204 & *  \\
Twitter tweets & 0.0026 & ** & $<$0.0001 & ***  \\
Twitter entropy & $<$0.0001 & *** & 0.0003 & ***  \\
Twitter avg. followers & $<$0.0001 & *** &  &  \\
Volume of unique tweets & & & $<$0.0001 & ***  \\
Unique tweets \% &  & & $<$0.0001 & ***  \\
Corporate retweets \% & 0.0092 & ** &  &  \\
\hline
\end{tabularx}
\end{table}

The distribution of visits to articles is log-normal distributed in our data, consistently with previous works~\cite{wu_2007_novelty,bandari2012pulse}. We log-transform ${log (x+1)}$ the visits as well as the volume of social media reactions. For t=5, 10, 15, \dots we calculate the proportion of the explained variance of these two linear models. The result is shown in Figure~\ref{fig:accuracy_pred_visits}.

It takes about 3 hours to be able to explain $>0.6$ of the variance for In-Depth articles, and the additional variables are profitable from the first minutes. After 10-20 minutes we observe the largest difference in our regression models (+0.5 in terms of $r^2$).

We take a closer look at the model variables after 20 minutes to identify the sources of this improvement. For this purpose we stepwise fit the model variables by AIC (Akaike information criterion) as implemented in {\tt stats.step} in R. Table~\ref{tbl:significance} shows the reliability of the Social media variables to serve as good predictor for the volume of visits after 7 days.

The fraction of traffic from different sources does not appear to be a reliable predictor when all variables are used for the model; when we reduce the model to exclusively these two variables, the traffic from e-mail/IM is a more reliable predictor than the traffic from external links.

Social media variables, particularly the number of tweets and the entropy of the vocabulary used in them, seem to be reliable predictors for both In-Depth and News articles. The number of followers of people posting an article on Twitter together with the fraction of corporate retweets seem to be particularly important for In-Depth articles. A possible interpretation is that the response to these articles has a larger component driven by influential accounts and the actions of Al Jazeera editors. In contrast, the number and fraction of unique tweets can be used for the prediction of traffic to News articles. Consequently, a rich online discussion around a breaking within its first minutes is a signal of potentially high and sustained user interest.

\begin{figure}[t]
\centering\includegraphics[angle=-90,width=.9\columnwidth]{fig/hist_shelflife}
\caption{Distribution of effective shelf-life.}
\label{fig:hist_shelflife}
\end{figure}

\begin{table}[t]
\caption{Modeling effective shelf-life: Significance levels for regression models after 20 minutes.}
\label{tbl:significance2}
\small
\setlength{\tabcolsep}{0.5em}
\hskip-0.5em
\begin{tabularx}{1.02\linewidth}{Xrlrl}\hline
Variable & \multicolumn{2}{c}{In-depth} & \multicolumn{2}{c}{News} \\
\hline\hline
Visits $R^2$ & 0.0005 & & 0.0921 & \\
Social media $R^2$ & 0.4457 & & 0.2193 & \\
Social media $R^2$ adjusted & 0.2274 & & 0.1505 & \\
\hline
Twitter tweets & 0.0138 & * & 0.0061 & ** \\
Twitter entropy & 0.0027 & ** & 0.0024 & ** \\
Twitter avg. followers &  &  & 0.0001 & *** \\
Volume of unique tweets & 0.0026 & ** &  &  \\
Unique tweets \% & 0.0190 & * & 0.0445 & * \\
Corporate retweets & 0.0001 & *** &  &  \\
Traffic from e-mail/IM & 0.0482 & * &  &  \\
\hline
\end{tabularx}
\end{table}

\subsection{Modeling shelf-life} \label{sec:shelf-life}

We define the {\em effective shelf-life} $\tau_{\ell}$ of an article as the time passed between its first visit and the time at which it has received a fraction $\ell$ of the visits it will ever receive. In this work we set $\ell=0.90$, but similar values (e.g. $0.85$, or $0.95$) yield similar results to the ones presented here. When $\ell=0.50$ this is equivalent to half-life~\cite{bitly_2011_halflife,bitly_2012_halflife}.

Given that our observation period is finite, we use a seven-day observation period as a proxy for the total number of visits the articles will ever receive, as for basically all the articles in our sample, there is little activity after 3 or 4 days. This is consistent with the experience of Al Jazeera editors and with observations in previous works (e.g.~\cite{yang_2011_patterns}). We remark however that there are rare cases where an article is ``re-born'' after weeks, for instance when it provides background information for a new development.

The distribution of the shelf-life for both classes is depicted in Figure~\ref{fig:hist_shelflife}. As observed in the qualitative analysis, the average shelf-life of In-Depth articles, 2 days and 9 hours, is longer than the one of News articles, 1 day and 16 hours. Their average half-lives are respectively 20 hours and 8 hours (both are shorter than the 36 hours observed by
\cite{dezso_2006_dynamics}).

\iffalse
\begin{figure}
\includegraphics[angle=-90,width=\columnwidth]{fig/scatter_visits_shelflife}
\caption{Scatter plot showing the absence of correlation between shelf-life and total visits.}
\label{fig:scatter_visits_shelflife}
\end{figure}
\fi

We observe that the effective shelf-life of all articles is independent from their total number of visits after 7 days (Pearson's correlation $r = -0.03$). This will lead to low accuracy when predicting based solely on visits. For the predictive task the linear regression model setup is analogous to the one used to model visiting volume; this time the dependent variable is $\tau_{90}$. Our focus is again on the variables after 20 minutes.  Running the first regression model (only visits) for this time period reveals differences for News and In-Depth stories (Table~\ref{tbl:significance2}). For News stories, at least 9.2\% of effective shelf-life variance can be described, while visits show no predictive information for In-Depth stories. Including social media variables changes this picture dramatically. Especially for In-Depth stories, a significant part of the variance can now be described. Stepwise fitting of the social media models shows that the number of Facebook shares and the traffic from external links are no reliable predictors for effective shelf-life. In contrast, all Tweet variables reach significant levels. For In-Depth articles corporate retweets and traffic from e-mail/IM also serve as reliable predictors.

In a nutshell, using social media variables to model effective shelf-life of stories can increase the accuracy of early prediction significantly. This is a very promising result for future research given that we describe the effective shelf-life pattern with data from one single time point without the use of time series or other elaborate models.

\subsection{Online predictions} \label{sec:system}

\begin{figure}[t]
\includegraphics[width=\columnwidth]{fig/screenshot-fast-edited.png}
\caption{Screenshot of http://fast.qcri.org/ depicting predictions for four articles. Green bars indicate number of pageviews so far, gray bars indicate predictions. Exact numbers are business-sensitive so they are omitted.}
\label{fig:fast-screenshot}
\end{figure}

A live system implementing these ideas on data from the Al Jazeera English website is available online.\footnote{\url{http://fast.qcri.org/}} This allows us to further test the effectiveness of our methods in an online setting, in addition to the off-line tests we have described so far. Figure~\ref{fig:fast-screenshot} shows a screenshot of this application.

The system collects data for all articles irrespective of their section, and produces predictions for all articles in the News section using one set of models, and for all the remaining articles (In-Depth, Videos, Programmes, etc.) using another set. In each model set there are models that are executed 1 hour, 6 hours, 12 hours and 24 hours after an article is published. The target variable in this live system is page-views after 3 days. Every 24 hours, it re-trains the models by adding the articles that have passed the 3-day deadline to the training set. After an initial warm up period of 3 weeks, we monitored all 350 article URLs published during a period of 1 week in July 2013 and kept all the predictions done by the system.

First, we evaluated the coverage of our system, which as explained in Section~\ref{sec:data-collection} is designed to focus on the top 30 most visited URLs in every 5-minute period. In practice, we produce predictions within 6 hours for 194 (55\%) of the articles seen. Taking as comparison Google Analytics, which is also used by this website,\footnote{\url{http://analytics.google.com/}} we observe that this covers 65\% of the page-views to Al Jazeera English articles. We remark that this partial coverage is not an intrinsic aspect of the system, but a limitation of using public (instead of paid) access to Twitter's API.

Second, we evaluated the quality of the predictions. In order to do so, we store the predictions done by the different models. Conceptually, each of the 350 articles is in the testing set, and the training set is composed of all the articles published in the period of 1 to 4 weeks before its publication day. Figure~\ref{fig:pred_sys} compares the actual number of page-views with predictions done 1 hour and 6 hours after an article is published. The quality of the predictions after one hour is similar to off-line tests ($r^2=0.72$) even when we are mixing articles from other sections; 134 articles (38\%) not in News or In-Depth -- but we also remark the predictive horizon of the live system is shorter (3 days vs 7 days). Predictions after 6 hours have $r^2=0.85$. The location of the best trade-off between timeliness and accuracy in the range of 1 to 6 hours is an important problem, which requires understanding how editors react to the predictions and use them in practice.

\begin{figure}[t]
\hskip-1em\includegraphics[angle=-90,width=.5\columnwidth]{system/pred_1h}~~\includegraphics[angle=-90,width=.5\columnwidth]{system/pred_6h}
\caption{Predictions of visits after 3 days using the online system across all articles. Left: predictions 1 hour after publication. Right: predictions 6 hours after publication.}
\label{fig:pred_sys}
\end{figure}

\section{Conclusions}

\mpara{Main findings.}
By adopting an integrated view of users' behavior, we have
observed that there are two classes of articles that generate qualitatively and quantitatively different responses from readers. News articles describing breaking news events tend to decay in attention shortly after they are published and thus have a shorter shelf-life. These articles also have more repetitive social media reactions, as most users simply repeat the news headlines without commenting on them. In-Depth items portraying or analyzing a topic tend to exhibit a longer shelf-life and a richer social media response, including more content-rich tweets in terms of vocabulary entropy and fraction of unique tweets, and more shares on Facebook for the same level of tweets.

By going deeper into the first few hours after publication of News articles, we found three distinctive response patterns in a roughly 80:10:10 proportion: decreasing traffic, steady or increasing traffic, and rebounding traffic. We found that there can be multiple causes for non-decreasing traffic, including the addition of new content to articles, social media reactions, and other types of referrals.

We have shown that social media signals can improve by a large margin the accuracy of predictions of future visits, as well as the accuracy of predictions of article shelf-life. In particular for In-Depth articles which exhibit more complex visit patterns over time, we have found that incorporating measures of the quantity and variety of social media reactions can lead to substantial gains in terms of prediction accuracy.

\mpara{Practical significance.}
From the perspective of a news provider, while no automatic system can replace editorial judgment, understanding and predicting the life cycle of stories has three main benefits:

\begin{itemize}
 \item In the case of News stories, knowing how the audience is interacting with an article is not just ``nice to have'', but increasingly a critical component in delivering timely and relevant content to an ever growing online audience.
 \item For In-Depth stories, which operate on a slower news cycle, knowing when to allocate additional time and resources can significantly improve the news planning process. This is particularly useful for an emerging class of news programmes that combine live online discussions with more traditional TV coverage.
 \item To a web producer, an article with a longer shelf-life means judicious time can be spent preparing backgrounder pieces which are valuable in providing context to a story. From a reach perspective, articles with steady or increasing levels of traffic translate into higher user engagement.
\end{itemize}

Our work depends on having access to a large repository of social media reactions. As more people get into social media (e.g. Twitter), this line of work will become more relevant and will be able to produce even higher quality predictions.

\mpara{Limitations and future work.}
We combine findings from computer science, journalism, and media studies. The research presented here is more difficult to execute than the traditional single-discipline study, but we expect interdisciplinary work on this area to become increasingly common as our media and technology continue to converge.

Our data gathering system collects only aggregate information and does not attempt to link actions across sessions or users across platforms; this prevents us from separating post-read from pre-read sharing, an important distinction explored in~\cite{agarwal_2012_multi}. Another limitation of our work is that we used data from a single website, and we are in the process of gathering data from other sources in order to strengthen our claims. We also used a manual process for categorization of the article classes (decreasing/steady/increasing/rebounding), and we did not attempt a comprehensive content-based classification of articles inside each class.

In this work, we used linear models and did not attempt anything more sophisticated. We do not claim that our models are the more accurate that can be built using this data, but used them to demonstrate in a clear way the importance of social media signals for the predictive tasks we undertake. Better models are definitively possible, and may yield even larger gains in accuracy when incorporating social media signals.

We also used a data-driven approach in which shelf-life is derived from observations. Alternatively, shelf-life can be derived by fitting a visitation curve produced by a parametrized model~\cite{wu_2007_novelty}. This may lead to an improvement in the prediction accuracy.

\mpara{Reproducibility.}
The data sample used for this study, including feature vectors and the categorization of articles done during the qualitative analysis, is available for research purposes upon request. A~live demo is available at \url{http://fast.qcri.org/}

\mpara{Acknowledgments.}
The authors wish to thank Al Jazeera English for the data used for this study, Kiran Garimella from QCRI for his work in the live system, Janette Lehmann from Yahoo! Research for her valuable help and comments on an early version of this manuscript; Michael K. Martin and Ju-Sung Lee from Carnegie Mellon University for insightful discussions on  regression models; and Edward Schiappa for his feedback on the methodology for the analysis of article classes.

\mpara{Key references:} \cite{brody_2006_citations, szabo2012predicting}

redpajama_set_name: RedPajamaArXiv


[Taipei, Taiwan] We are happy to announce details for the APAC open qualifiers are now available. First qualifier will happen on 28th of June, while the second qualifier will be ran on 5th of July. These will be a 128 man open tournaments with BO3's leading up to a BO5 in the semi's and finals.
APAC is China, Japan, Hong Kong ONLY!
The APAC qualifers DO NOT include Taiwan, Taiwanese qualifiers will be held by MSI. More information on Taiwanese qualifiers will be released towards the end of July.
For the StarCraft 2 Tournament, there will be six qualifiers for the following regions: Europe, USA, APAC, South Korea, SEA, Taiwan and Australia. These qualifers are, depending on the region, split into a Pre qualifer and a Main qualifer where up to 64 players can take part. Finally the best 16 players will compete in Taiwan in the Grand Finals. The Prizepool for the StarCraft 2 tournament is $28,000.


text:
Q: Base class for windows service My new project has a design in which there are number windows services for performing different tasks. I have been given a task to create base class from which all of the windows service will inherit. This base class will perform common functions like creating instances of other windows services by iterating through the config file (may be like Activator.CreateInstance), do event logging on onStart, onStop etc. and may contain some more functionality.
Before I start developing stuff, wondering if there is any pattern already in place or someone has good understanding of how to implement this kind of functionality.
Any help appreciated.

I forgot to mention I am using .Net 2.0 (C#), no option for wcf

A: Check "A simple windows service to host WCF or WF services", I really like it.

A: Look in your main method , when you create windows service and you will see that the routine is not call onstart and onstop, you can create each service by createinstance and give it as a paramater to ServiceBase.Run, in your case every service will not appear as a separate service, ServiceController is a great class to start and stop services too, use it if you need


text:
Name: Alfred Niederhoff
Rank: Leutnant
Units: Jasta 11, 20
Born: 02 January 1895
Place of Birth: Dortmund
Killed In Action: 28 July 1917
Place of Death: Becelaere
After scoring two victories with Jasta 20, Niederhoff was reassigned to Jasta 11 under Manfred von Richthofen on 30 April 1917. On 20 July 1917, he scored his sixth victory, shooting down a Nieuport 23 flown by Australian ace Alfred Shepherd of 29 Squadron. Eight days later, Niederhoff's Albatros D.V was shot down by members of Shepherd's squadron.
#6 Alfred Shepherd
1 11 Mar 1917 Jasta 20 Sopwith 1½ Strutter Roye
2 25 Mar 1917 Jasta 20 Sopwith 1½ Strutter St. Quentin-Le Catelet
3 05 Jun 1917 1130 Jasta 11 Sopwith 1½ Strutter S of Ypres
4 07 Jul 1917 1110 Jasta 11 Sopwith Triplane Bousbecque
5 17 Jul 1917 1315 Jasta 11 Nieuport 23 Nordschoote
6 20 Jul 1917 2110 Jasta 11 Nieuport 23 (B1504) Zonnebeke
7 22 Jul 1917 1130 Jasta 11 Sopwith 1½ Strutter SE of Zonnebeke

redpajama_set_name: RedPajamaCommonCrawl


text:
David George Osborne
December 16, 1936 – August 14, 2020
David, "Dave", was born December 16, 1936 in Vancouver, BC. He passed away on Friday, August 14, 2020 at the West Coast General Hospital in Port Alberni.
Dave is survived by his wife, Gabriele Osborne; children, Lesley (Michael) and Tom (Darcie); stepdaughters, Sabrina (Terry) Lamport and Moya (Luke) Kelly; grandchildren, David and Sarah Osborne, Teagan and Damon Lamport; sisters, Marilyn McPherson and Diane Miller. Dave was predeceased by his mother, Margaret Muir and father, William, "Bill" Osborne.
While Dave was born in Vancouver, he spent the majority of his childhood growing up in Port Alberni where he also spent most of his adult life. Dave loved Port Alberni. He was a second generation shipwright and worked with his father at the Osborne Shipyards in Vancouver.
He worked with his father for a time before moving back to Port Alberni to work with wood, steel, and aluminum boats at Alberni Engineering and then at Kamma & Blake Industries.
Dave was a very active man and had a sport or hobby for every season. Whether it was skiing, curling, hiking, telemarking, windsurfing, tennis, hockey – which he started doing at the age of 60, sailing, paddle boarding, or pickleball, he enjoyed it all.
Dave's adventures lead him to his first wife Shirley through racing cars and boards and then sailing. His love for Mount Arrowsmith was a major part of his life. He built cabins and a rope tow with his Valley friends and then later was a ski patroller.
A proud moment in Dave's life was his mountain top wedding with Gabriele by the top ski lodge.
Dave taught all the children in his life and those around him a love of the outdoors. He really did enjoy these many special times and places in his life.
David was an avid reader and loved mysteries with strong detectives. He made his own wine and bread and was not afraid to throw a little flour around if the occasion demanded it!
Dave was a strong family man too. In his quiet way, he nurtured the children he helped raise and allowed them to grow up to be strong adults without the need to provide advice. He let them learn on their own while providing support along the way.
Dave enjoyed his time with his kids at their concerts and games and then later his grandchildren's choral concerts or hockey games in persona and watching games online.
Dave loved his time with family and friends on Hornby too. He spent many summers there where he worked on projects like the summer kitchen or just quietly sitting in a sunspot by the side of the road with the people he loved.
Dave will be truly missed by all that knew and loved him – his family and friends. A gathering to honour his memory will be held at a later date.
In lieu of flowers, those wishing to honour his memory may do so by making a donation in his name to either the BC Cancer Society, 2410 Lee Avenue, Victoria, BC, V8R 6V5 or the West Coast General Hospital Foundation,. 3949 Alberni Highway, Port Alberni, BC, V9Y 4S1.
"We will see you on the hill!"
To send a condolence to the family please visit www.yatesmemorial.ca
Yates Memorial Services
"Lives Cherished and Celebrated"
Yates Memorial ServicesObituary


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Happy New Year, everybody! WIshing you a peaceful and prosperous 2018.
Best actor of all time ?
Hi guys, I have spare invites to TVChaosUK, which is a great site for all British TV shows. I'd like an invite to Broadcast the Net or Pass the Popcorn. Thanks!
Hi. I'm Marguerite. I'm from the United States. I like art, cats and playing Fallout 4. I'm in my junior year at university. I'm an art major. Here's some lines from my favorite poem by James Elroy Flecker. HASSAN: Sweet to ride forth at evening from the wells, When shadows pass gigantic on the sand, And softly through the silence beat the bells Along the Golden Road to Samarkand. ISHAK: We travel not for trafficking alone; By hotter winds our fiery hearts are fanned: For lust of knowing what should not be known We take the Golden Road to Samarkand.


Comment: All pages and the cover are intact, but the dust cover may be missing. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable. Has remainder mark and shelf wear.
The Startup Playbook is the go-to guide for any first-time entrepreneur interested in creating and building a new startup. By founders for founders.
Start reading Stress Test: Reflections on Financial Crises on your Kindle in under a minute.
I don't know why I decided to buy this book. I never particularly liked Geithner or his policies, and I've already read several good books about the financial meltdown of 2007-2008. However, I did end up picking it up and reading it, and I'm glad I did.
To my surprise, Geithner comes across as very likable. Of course, likability is not a critical characteristic for a great Treasury Secretary. However, it's a necessary and maybe even a critical characteristic if you're going to read a 500-page book by a Treasury Secretary; had I found him obnoxious, I doubt I would have finished this (and you can see my review of "Duty" by Robert Gates for proof of that). Geithner comes across as extremely intelligent but not at all a "peacock," to use one of his words. He readily admits that he screwed up in many ways, and he makes no bones about his failings. At the same time, he takes us through the panics (his own and that of the economy) and his approach to his responsibilities, and he seems to be, well, human.
The book is also not even remotely snarky. He is candid in expressing his views of a number of other key players in dealing with the meltdown, and he's not always complimentary. However, his views are policy-based rather than personal, and he doesn't shy away from giving credit where credit is due to people he's not fond of from a policy point of view.
I also learned a lot about the financial crisis that I didn't learn from the other books I've read. Much of that new knowledge is the "inside story" that Geithner tells, of course, but it's an interesting and important story, and I found him more or less persuasive.
My comment about the sausage-making is the only negative. In order to tell the inside story he feels he must tell (and I can't really blame him for wanting to tell it), he gives lots and lots of detail about various financial institutions' failures and rescues and many details of how the policies (i.e., the sausage) was made. IMHO, these lengthy discussions could have been edited a bit more judiciously, and doing so would have made the book more readable (not that it's unreadable - it's just long and rather too detailed). However, while I've deducted one star to reflect this view, I believe this is a worthy book and, in fact, a worthy man.
This book is user friendly, well written and well thought out. It shows that Obama chose his Secretary of the Treasury very wisely. The detail is amazing, but not in the least boring. It reads like a novel. Geithner was the exact right man for the job, already skilled and savvy in the knowledge of how to go about rescuing nations from economic crisis. This is well worth the read and impressive. Geithner emerges as a very likable and admirable human being, who just happened to be in the right time and place to save us from Depression 2.0.
The Great Recession and financial panic of 2007-2009 has a lot of myths and stories surrounding it concerning what the government did or didn't do, what it should've done and shouldn't have done, and the motives of those working behind the scenes. Former Secretary of the Treasury Timothy Geithner is one of those lighting rod figures from the crisis who, because of his relative anonymity prior to the crisis, is often misunderstood or caricatured. In this book, Sec. Geithner not only dispels the myths about his own background, but also provides a unique insight and perspective on the panic from one of the only two people in high government who was there from beginning to end (Fed chairman Ben Bernanke being the other one). But his story, and his experiences with financial panics, doesn't start in 2007, but rather starts with his upbringing in a family that was involved in international development and finances from when he was very young, always moving from one country to another and spending very little time in America. From there, it moves on to his time as a civil servant in the Treasury department's international wing, dealing with the crises brought on the by the Asian financial panics of the 1990s and eventual rising to be Assistant Secretary of the Treasury in charge of the international branch. From those times Mr. Geithner picks up a lot of invaluable experience in financial panics, what makes them better, and what makes them worse. From there he makes the surprising leap to head of the Federal Reserve Bank of New York, the most powerful of the regional Federal Reserve Boards in the country. And that is where the main story begins as Mr. Geithner describes the events that led to the panic, the actions he took as FRBNY chair and then Treasury Secretary, why they took them, and why they were necessary even if they were both unpopular and counterintuitive. The book reaches its climax with the stress tests backed by promises of capital injections for those banks the tests deemed were in need of them. Once the results of the test are released in the spring of 2009 a certain measure of calm enters the markets and the last chapters feel like a denouement as he deals with the Eurozone crisis and the toxic politics of Washington following the ascendancy of the Tea Party in 2010. What is surprising is how in the first half of the book Mr. Geithner seems to undersell himself, not out of a sense of humility, but because he genuinely seemed to believe that there were other people better or more knowledgeable than him. From my perspective, it seems like a no-brainer that Pres. Obama tapped him to be Treasury Secretary, even if he was a terrible salesman (a flaw that he readily, even jokingly, admits to being). What is also great about this book is how much value it has beyond being another account of the financial crisis. He does a great job of explaining why the unpopular decisions the Fed, the FDIC, and the Treasury took were necessary to save the economy, even if he falls into the trap of using too much of the jargon of Wall St. to explain it. I believe this book has value not only as an account of this financial crisis, but as a guide to how to handle future financial, indeed any, crisis in the future. As Mr. Geithner is fond of saying, a plan beats no plan. I highly recommend this book to anyone interested in the true story behind the financial crisis and the Great Recession of 2007-2009.
Overall I really liked this book. While Tim Geithner is not an writer and his style is definitely not of a thriller, the portrait of the crisis from his view definitely contributes to the overall understanding of the financial crisis of 2008.
This book should not be taken as "stand-alone" but read in the context of all the other ones out there about the crisis itself. It helps one to form their opinions in a clear and more fair way in my opinion.
Finally, this book also help show that the work of the Treasury, Federal Reserve, and other institutions that prevented the crisis relied on countless individuals who never get recognized for their hard work and countless hours helping make this country better. My admiration and gratitude only got bigger after reading this account.


This topic contains 5 replies, has 5 voices, and was last updated by Qwandree 4 months, 2 weeks ago.
I am on the fence of buying a Vitpilen 701. Unfortunaltely I have not had the opportunity to take one out for a test ride ( I only sat on one at the dealership), but I have previously ridden the Duke 690R and 690Smc-r, the latter I absolutely loved. I love the looks of the Vitpilen, I love the fact that it has quickshifter and abs, tc and such. I am just wondering if anyone could tell me if the engine is retuned compared to say the supermoto, or how capable the Vitpilen is as a hooligan-bike? Is it mostly top-end tuned or does it have that wonderful punchy in-your-face mid-range?
The 690Smc-r is basically the same as the 701 Supermoto, so if you like the hooligan supermoto thing then get one of them, they are fantastic bikes and unbelievably fun.
The Vitpilen 701 is slightly more refined with more tech as you mention but the main difference is the chassis and riding position. Engine is very similar with bags on low down grunty torque and it does retain some of the supermoto flickability.
FYI- KTM's 2019 SMC & Enduro R now have a quick shifter and dynamic corning ABS.
I definitely wouldn't call it a hooligan bike but it's super fun. The low weight does tons to make up for lack of power.


The Miku Supporter Photo Shoot in Tokyo is this weekend, but before we get to the live webcast schedule there's a bit of new information we need to cover.
First is regarding the Session 4 deadline. As Session 4 will be held on a yagatabune boat, the entry deadline will be on (Sat) Sept. 24 at 12pm JST.
There will be no same day tickets, so please take note! Sessions 1-3 however will have same-day tickets and have no deadline. Also, the costume for the yugatabune will be jinbei. It may be fall already, but we want you all to savor this one last taste of summer.
Also, autograph sessions will be held during Sessions 1 and 4. There will be no autograph sessions during Sessions 2 and 3.
If you haven't already registered, please reserve your slot at the site below!
Sessions 1 & 4 will be held outdoors, so we'll have our live webcasts during Sessions 2 & 3. In 2 30 min segments, the Racing Miku Supporters will have 15 min each, so please tune in from start to finish!
Racing Miku Supporters Photo Shoot in Tokyo: Registration Open!
Announcing the 2016 Nagoya Photo Contest Winners!
GSR Live Webcast is Today! Looking Back at Suzuka!
Thai Race Live Viewing Tickets – On Sale Tomorrow!
Thai Race Live Viewing Tickets – On Sale Now!
Round 5: Fuji – Photo Conest Winners!
Thai Pack-Up Report from Shun!
All-New Car-Wrap Stickers from Noraimo!


You will find details regarding the litigation settlement here.
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text:
package controllers

import (
	"github.com/JREAMLU/core/global"
	io "github.com/JREAMLU/core/inout"
	"github.com/JREAMLU/jkernel/base/services"
)

// IPController ip struct
type IPController struct {
	global.BaseController
}

// Info ip info controller
func (r *IPController) Info() {
	data, jctx := io.InputParams(r.Ctx)

	service := services.NewIP()
	httpStatus, shorten := service.IPsInfo(jctx, data)

	r.Ctx.Output.SetStatus(httpStatus)
	r.Data["json"] = shorten
	r.ServeJSON()
}

redpajama_set_name: RedPajamaGithub


Fukushima Coverup: Will There Be Accountability for What Happened During The Nuclear Disaster?
A scandal is rocking Japan as the President of TEPCO, the Tokyo Electric Power Company, admits that the corporation engaged in a coverup during the devastating Fukushima nuclear disaster. So far, there has been no real accountability for this historic disaster. Becker is joined by Kevin Kamps of Beyond Nuclear to talk about the coverup.


text:
Hybrid immunity offers 'strongest and longest protection against severe Covid'
However, all sources of protection against reinfection wane 'within months'
A man receives a Covid-19 vaccine dose in Hoboken, New Jersey. EPA
Gillian Duncan
Hybrid immunity offers the greatest protection against severe disease from Covid — and previous infection provides higher protection than vaccination alone, a review has found.
However, all sources of protection waned "within months" against reinfection, the analysis found.
The term hybrid is used to describe the immunity provided by a combination of infection and vaccination.
Researchers writing in The Lancet Infectious Diseases said the Omicron variant resulted in many individuals with hybrid immunity due to high levels of infection following vaccination.
They wanted to know how strong the level of protection in people with hybrid immunity was, compared to those who had been infected without being vaccinated.
What is the 'Kraken' Covid variant XBB.1.5 spreading globally?
"There are gaps in the literature on the magnitude and duration of protection conferred by previous infection, both among individuals who have not been vaccinated (ie, the effectiveness of previous infection alone) and among individuals who have been vaccinated (ie, the effectiveness of hybrid immunity)," said the paper, which has been published in The Lancet Infectious Diseases.
Previous reviews had estimated the durability of protection conferred by previous infection.
But none had looked into the durability of protection conferred by hybrid immunity or compared the durability of the different types of protection, said the researchers.
So they set about comparing data from 26 studies.
The analysis found people with previous infection alone, and those with previous vaccination only had "rapidly waning protection" against infection with the Omicron variant.
"Previous infection was found to provide higher protection against reinfection and more sustained protection against hospital admission or severe disease than vaccination alone," said the study.
However, people with hybrid immunity had the most robust and longest protection of all, "emphasising the importance of providing vaccination to previously infected individuals," they said.
The analysis shows protection against admission to hospital remains high 12 months after developing hybrid immunity.
Anyone who developed hybrid immunity in the period had a 95 per cent lower chance of suffering a serious case of the disease, according to the paper.
Those who were infected a year before, but were not vaccinated, had a 75 per cent lower chance of developing a severe disease and requiring admission to hospital.
Protection against reinfection was lower, at 42 per cent for those with hybrid immunity, compared to 25 per cent for those who had been infected before but were not vaccinated.
The researchers said protection provided by previous infection "should not detract from the need for vaccination" because infection-induced protection against reinfection wanes rapidly, and vaccination increases durability.
"Furthermore, there are serious risks associated with infection," said the paper.
"These include the risks of hospital admission, ICU admission and mechanical ventilation, and death. They also include the risk of developing post-Covid-19 complications.
"Additionally, those who recover from severe Covid-19 have a higher risk of cardiovascular complications, neurological complications, dementia, diabetes, and chronic respiratory problems.
"Vaccination is therefore a safe intervention to avert severe disease outcomes and to reduce post-Covid complications."
A recent large-scale global analysis found pregnant women with Covid are seven times more likely to die and face a substantially higher risk of suffering from serious complications.
Expectant mothers infected by Covid-19 are more than three times more likely to be admitted to intensive care and 15 times more likely to require a ventilator to help them breathe.
They face a 23 times higher risk of developing pneumonia and are five times more likely to suffer from blood clots.
The study also suggests having Covid during pregnancy also increases the risk that the baby will need to be admitted to intensive care, said the study.
Researchers said the risks underscore the need for pregnant women to be vaccinated against the virus.


lxqt or file mgr problem?
Sadly i dumped lxqt after several problems moving files between windows; also moving and editing files on usb thumb drives. Now i am having the same problems with xfce. Do they both use the Thunar file manager by default? And could that be the issue, rather than lxqt? How can i switch to pacmanfm? Thanks!
LXQT is the successor of Lxde (formed after merging Razor-QT and LXde) and both are using PCMan file-manager. Xfce is using Thunar as its default file-manager.
It looks like your problem is file-manager independent. Do you encounter this issue with all of your USB thumb drives or just with a specific thumb drive?
All thumb drives are affected, as well as the known Thunar issue of transferring between windows on the same drive. Does Openbox use Thunar by chance? Somehow it seems i had both file managers on the Openbox/LxQt setup?
@billwx you use pcmanfm-qt or pcmanfm ?
Thunar is a part of Xfce, not of Openbox.
I think to test the whole situation the best is to format at least 1 thumb drive in a terminal and test afterwards if you still have any issues with it. If you can read/write after formatting the thumb drive you could format all thumb drives.
Reading your post more carefully i recognize your problem since i had similar experience with some of my thumb drive. After formatting them in a terminal the problem was gone.


Already boasting the likes of Chad le Clos and Chad Ho as recent world champion swimmers, Westville are firmly entrenched at the very top of school swimming and very few schools are in a position to give them too much of a challenge at this stage.
It is probably an understatement to say that WBHS won the D&D at Kings Park last week in convincing style, because in fact the A team won every race on the programme, racking up the maximum of 260 points on offer. To the best of our knowledge, this is a feat that has never been achieved before. In the process they set six new records in the U15, U17 and Open age-groups. Clifton were in 2nd position on 203, followed by Kearsney on 201 and Glenwood A on 165. The B team were also impressive, giving the DHS A team a close run for 5th position, and finishing ahead of Northwood A, Glenwood B and DHS B.
Swimming captain Marno Langeveldt was delighted with the commitment and fine performances of every one of his swimmers, who were inspired by an enthusiastic group of more than 500 Westville boys. After success in the National Alan Burt event, the WBHS Interprovincial gala and the D&D, they now look forward to the KZN Top 8 event next week.


Salem Public Schools » Families » Student Enrollment » Parent Feedback - We Want to Hear from You!
Whether you have newly registered for Kindergarten or have been a long-time Salem Public Schools family, we want to hear from you! Please complete one of the below feedback forms to share your insights and experiences with us. The information you provide will help us learn what's working and identify areas for improvement. Please contact the PIC with any questions at pic@salemk12.org.