text:
\section{Text Processing \& Classification}
\label{sec:classify}

In this section we present the details of our tweet classification (using ML)
and sentiment analysis (using existing NLP tools).

\subsection{Topic Classification}
\label{sec:topic}

One of the major challenges during cleaning the dataset and removing spam was
ensuring topic relevance. Our expectation was that this would not be an issue:
as in much previous work, our study is looking at all sentiment expressed
towards the brands, as long as the tweet matched the parameters of the tweet
selection as explained in Section~\ref{sec:data}. However, whilst sampling tweets for spammers, a general problem surfaced. We found that a keyword-based approach
tends to be too broad to accurately identify tweets referring to a particular brand,
\emph{O2} (a UK mobile telecommunications provider and network). Our parameters
for collecting tweets for this brand were to match tweets containing
\texttt{O2WhatWould\-YouDo} and \texttt{O2} (the hashtag being promoted was
\texttt{\#O2What\-WouldYouDo} and \texttt{@O2} is the official brand Twitter
handle). Over the 10 day period, 90,000 tweets were collected that matched
these keywords. However, examining a random sample of 200 tweets from this
dataset showed that over 70\% were not referring to the O2 Network brand; many
were referring to the \textit{``O2 Academy''} (a chain of concert venues), the
\textit{``O2 Arena''} (a dome-shaped monstrosity in London), or other senses of
\textit{`O2'} such as oxygen. We also noticed that Twitter users have recently
established a new way of using the letter sequence \textit{`O2'} as a
replacement for the letters \textit{`to'}: e.g. ``\texttt{@CokeWave\_Thang What
  Picture You Want Me O2 Put As My BackGround}'', ``\texttt{what im\\goin o2 do
  o2day}''. Experiments with boolean combinations of \texttt{O2} with other
keywords were not successful. A major challenge therefore becomes to filter out
non-brand-related tweets automatically: the problem is not trivial, given the
variability and unpredictability of language, vocabulary and spelling on
Twitter, and the short length of tweets (up to 140 characters); and manual
removal of approximately 70\% of large datasets is prohibitively
labour-intensive.

We therefore approached this as a text classification problem and investigated
various supervised machine learning approaches using the Weka toolkit
\cite{Hall.etal09}. First, we performed a pilot study over a 200-tweet
development set to determine a suitable feature representation and
classification method; the data was manually labelled as O2-related or otherwise
to give a binary decision problem. We tested a variety of classifiers including
Naive Bayes, Naive Bayes Multinomial, ID3, IBK and J48 decision trees; features
were based on the tweet text using a standard bag-of-words representation (see
e.g. \cite{Manning.Schutze99}) with various scaling methods,\footnote{We used
  Weka's \texttt{StringToWordVector} filter for text feature extraction and
  scaling.} with the addition of user ID and date of tweet. Given the small size
of the dataset, we restricted the feature space to be based on the most common
100 words. We also tested using a simple manual keyword-based filter to remove some
common negative instances (using keywords \textit{arena, academy}, etc) before
training (see ``manually filtered'' results in the figures). Tests were
performed using ten-fold cross-validation in order to simulate performance on
unseen data. Best performance (overall accuracy) was obtained using only
bag-of-words text features, with stopwords removed and a TF-IDF weighting, after
manual filtering. The best performing classifiers in cross-validation were J48
and Naive Bayes (NB), with 71\% and 91\% accuracy respectively. We then compared
their performance on a held-out test set: the NB model outperformed the J48
model with 84\% accuracy compared to 71\% for J48, with training and prediction
also noticeably faster for NB (the tree structure of the J48 model made it very
slow with larger training sets).

\begin{figure}[ht!]
  \centering
  \includegraphics[width=0.5\textwidth]{learning-curve}
  \vspace{-0.5cm}
  \caption{NB accuracy with increasing training data.}\label{fig:learning-curve}
  \vspace{-0.25cm}
\end{figure}

To determine a suitable training set size, we then varied the training set while
testing performance on a held-out test dataset of 30 manually labelled
tweets. Increasing training set size improved performance (see
Figure~\ref{fig:learning-curve}): we tested up to a 2,000-tweet training set;
while the curve suggests performance may improve beyond this point, the accuracy
on the held-out test set is approaching that on the training set so large
improvements are unlikely. The NB classifier trained on 2,000 tweets was
therefore used for the experiments below. Figure~\ref{fig:NBres} shows results
when tested on a larger, unseen, randomly selected test set of 100 tweets; the
version with manual filtering achieves 78\% accuracy, 77\% recall and 66\%
precision. Figure~\ref{fig:NBres2} gives details of the per-class predictions:
without manual filtering, false positives are more common than false negatives
(i.e. too much irrelevant data is slipping through); levels are much closer with
filtering.

\begin{figure}[t!!]
\centering
  \includegraphics[width=3.3in,height=2.5in]{nbtestresults2}
 \caption{Classification results using Naive Bayes.\label{fig:NBres}}
\end{figure}

\begin{figure}[t!!]
\centering
  \includegraphics[width=3.3in, height=2.5in]{nbtestresults}
 \caption{Classification details per class using Naive Bayes.\label{fig:NBres2}}
\end{figure}

\subsection{Sentiment Analysis}
\label{sec:sentiment}

Having identified tweets with relevant content, we now required a method for
sentiment analysis -- determining the positive or negative stance of the writer.
As discussed in Section~\ref{sec:related} above, many methods for sentiment
detection exist, with the major distinction being between lexicon-based and
machine learning-based approaches. We examined existing tools for Twitter
sentiment analysis using both of these approaches in order to determine the most
suitable for our data.

As a baseline lexicon-based tool we used the freely available Data Science
Toolkit\footnote{\url{http://www.datasciencetoolkit.org/}}. The sentiment
analyser is based on a sentiment lexicon \cite{Nielsen11}; we therefore
anticipate its coverage to be low but take it to be representative of
commonly-used lexicon-based approaches.

For a more robust tool for comparison, we examined two alternatives.  As a
hybrid lexicon/machine-learning tool we chose
SentiStrength~\cite{Thelwall:2012}. This method uses a predetermined list of
words commonly associated with negative or positive sentiment, which are given
an empirically determined weight (learned from data); new texts are classified
by summing the weights of the words they
contain. Thelwall~{\it et al.}~\cite{Thelwall:2012} report accuracy on Twitter data of
63.7\% for positive sentiment and 67.8\% for negative when predicting ratings on
a 1-5 scale, and accuracies near 95\% when predicting a simple binary
positive/negative label. However, even though their word lists and weightings
are determined for OSN data (including Twitter), this approach may suffer when
faced with social text with new words, unexpected spellings and
context-dependent language and meaning (see~\cite{6215730}).

For a purely ML-based option we used Chatterbox's Sentimental
API,\footnote{\url{http://mashape.com/sentimental/sentiment-analysis-for-social-media}}
based on statistical machine learning over large, distantly labelled
datasets~\cite{Purver.Battersby12}. This data-based approach means it might be
expected to handle slang, errorful or abbreviated text better. Purver \&
Battersby \cite{Purver.Battersby12} report accuracies approaching 80\% using a
similar technique on smaller datasets; Chatterbox report 83.4\% accuracy in an
independent study.\footnote{See
  \url{http://content.chatterbox.co/Sentiment\%20Analysis\%20Case\%20Study\%20-\%20Chatterbox\%20and\%20IDL.pdf}.}

Before applying the sentiment analysis tool, and in order to compare the two
approaches, a few hundred random tweets were selected from the database and were
read and manually labelled for positive or negative sentiment, and both tools
were tested on the resulting set. Results showed accuracy below 50\% for the
lexicon-based Data Science Toolkit, 63\% for the hybrid SentiStrength approach,
and 84\% for the ML-based Chatterbox approach. Error analysis showed one
significant source of the latter difference to be sentiment expressed in
hashtags (e.g. the negative \texttt{\#shambles}), which were detected better by
the ML-based approach, presumaby due to their absence from SentiStrength's
predetermined lexicon. We therefore use Chatterbox as the ``robust'' tool in our
experiments below, and compare to the Data Science Toolkit as a purely
lexicon-based baseline.

\section{Conclusions \& Future directions}
\label{sec:conclusion}

In this paper we present a measurement-driven study of the effects of promoted
tweets and trends on Twitter on the engagement level of users, using a number of
ML and NLP techniques in order to detect relevant tweets and their
sentiments. Our results indicate that use of accurate methods for sentiment
analysis, and robust filtering for topical content, is crucial. Given this, we
then see that promoted tweets and trends differ considerably in the form of
engagement they produce and the overall sentiment associated with them. We found
that promoted trends lead to higher engagement volumes than promoted tweets.
However, although promoted tweets obtain less engagement than promoted trends,
their engagement forms are often more brand inclusive (more direct mentions);
and while engagement volumes
drop for both forms of promoted items after the first day, this effect is
less pronounced for promoted tweets.
We also found that although the volume of tweets is highest in promoted trends,
they do not lead to the same level of positive sentiment that promoted tweets
do. Hence advertisers should carefully assess the trade-offs between high level
of engagement, drop-off rate, direct mentions, and positive user sentiment.

In the next stage of this study we will investigate the effect of individuals'
influence on the take-up of promoted tweets and trends by their social graph. We
will investigate new data at finer granularity (hourly) for events that are
time-sensitive, such as major concert ticket sales. This is our first attempt at understanding this space. The advertising campaigns have very different structure and we need to understand these in details. Promoted trends typically stay on the trends list for a day, and promoted tweets are selectively shown to a subset of users for a period of time selected by the advertiser. Without accounting for such nuances, broad statements on the impact of the two forms of advertising are not conclusive. However in this paper we focussed on insights in using sentiment analysis methods and accurate data labelling. We believe our findings could provide a new insight for social network marketing and advertisements
strategies, in addition to comparing different methods of classifying and filtering relevant content.

\section{Data collection}
\label{sec:data}

We set up a crawler to use the Twitter Streaming API\footnote{\url{https://dev.twitter.com/docs/streaming-apis}} to collect the tweets of interest and all associated metadata (e.g., ID, username, user's social graph), with details stored in a MySQL database. In this section we briefly describe our dataset and data collection strategy.

\subsection*{Identifying promoted brands}
Twitter distinguishes promoted tweets and trends by the use of a \emph{Promoted} tag. We collected tweets from 11 brands with an active advertising campaign during our study period, across different industry domains, ranging from entertainment to health-care. For each promoted item, the brand names was used to crawl Twitter for tweet data posted in English for a 10 day period. If the promoted item also included a hashtag, the hashtag was also included in the parameters of the crawl's GET function. This included all tweets that contained keywords such as  \texttt{@BrandName}, \texttt{\#BrandName}, \texttt{BrandName}, \texttt{\#PromotedHashtag} and other brand related terms. These parameter values were selected to keep the dataset both relevant to brand-related tweets, and also manageable for searching purposes. Followers and following information was also tracked on a daily basis for each brand.

\begin{table}[t]
\centering
\begin{tabular}{ |l|l|l| }
\hline
\textbf{Industry}&\textbf{Promotion type}&\textbf{Brand} \\ \hline
\multirow{3}{*}{Electronics}&Promoted tweet&International CES \\
 &Promoted tweet&SONY \\
 &\textit{Promoted trend}&Nintendo UK \\
  \hline
\multirow{1}{*}{Travel}&Promoted tweet&Marriot \\
  \hline
\multirow{1}{*}{Entertainment}&Promoted tweet&BBC One \\
  \hline
\multirow{1}{*}{Automobile}&\textit{Promoted trend}&Vauxhall \\
  \hline
\multirow{1}{*}{Heath Care}&Promoted tweet&Paints like Me \\
  \hline
\multirow{3}{*}{Retail}&\textit{Promoted trend}&ASOS \\
 &\textit{Promoted trend}&PespiMax \\
 &Promoted tweet&JRebel \\
  \hline
\multirow{1}{*}{Telecomms}&\textit{Promoted trend}&O2 Network \\
  \hline
 \end{tabular}
        \vspace{-0.2cm}
\caption{Industry sectors and sample brands}
        \vspace{-0.5cm}
\label{tab:PPer}
\end{table}

Details of the selected brands and their promoted type are provided in Table~\ref{tab:PPer}. Given that we were interested in promoted items for branding purposes, a range of different brands from different industries were selected. The aim was to include both major, and small brands when selecting promoted items. In addition, a major brand and a small brand enable a comparison of sentiment while weakly controlling for follower count.

\subsection*{Dataset}

We identified different industries' promoted items for 10 day periods between $17^{th}$ December 2012 and $7^{th}$ January 2013. We used non-parallel crawling periods in order to avoid the query limits set by the Twitter API. In total, around 180,000 individual tweets were collected by crawling Twitter continuously, excluding December $21^{st}$ 2012 when there was a 6 hour outage in the crawler API. The crawler collected tweets from around 120,000 different Twitter users engaged in spreading the promoted tweets and trends. Tweets across all topics and with no geographical limits were gathered, as long as they featured the brand's name/hashtag. When a brand contained more than one directly relevant hashtag, e.g., \texttt{\#Coke} and \texttt{\#CocaCola}, we included all the relevant hashtags.

Twitter users do often repeat their tweets to benefit from repeated exposure. However, in order to remove noise and bias in analysis caused by spam tweets, we removed users who had posted the exact same tweet more than 20 times during our measurement periods, along with their tweets. Twitter users, tweets and tweet timestamps were also cross-analysed to check for spamming accounts. In one case a single user was removed for adding over 8,000 spam tweets to the database. After manual inspection of many tweets and accounts, we are confident that nearly all spam has been removed from our dataset.

\section{Introduction}
\label{sec:intro}

Online Social Networks (OSNs) such as Facebook, Twitter, and YouTube have emerged as highly engaging marketing and influence tools, increasingly used by advertisers to promote brand awareness and catalyze word-of-mouth marketing. Researchers have also long recognised the effectiveness of OSNs as a rich source for understanding the spread of information about the real world~\cite{youarewhattweet}. For example, Asur~{\it et al.}~\cite{DBLP:journals/corr/abs-1003-5699} analyzed Twitter messages (\emph{tweets}) to predict box-office ratings for newly released movies. Their findings shows that OSNs can be used to make quantitative predictions that outperform those of markets forecasts, by focusing on the \textit{sentiment} expressed in the tweets.
Brands also now recognise the potential of OSNs for gathering market intelligence and insight. In 2012, Twitter announced that 79\% of people follow brands to get exclusive content.\footnote{\url{http://advertising.twitter.com/2012/05/twitter4brands-event-in-nyc.html}} This provides the opportunity for brands to participate in real-time conversations to listen to and engage users, respond to complaints and feedback, drive consumer action and broadcast content. Understanding the real engagement of the end users with the brands and their OSN presence has given rise to a number of data analytics, sentiment analysis and social media optimisation startups and academic research projects. However, the techniques required pose a number of challenges and pitfalls often ignored by researchers and analysts, and adopting a particular method naively can lead to problems. Significant progress in Natural Language Processing (NLP) and Machine Learning (ML) has produced models for topic modelling designed for social media \cite{Ritter.etal10}, and high accuracies in sentiment detection (e.g. \cite{Socher.etal13}), even with the possibility of detecting sarcasm \cite{Liebrecht.etal13}; but care still needs to be taken while using and relying on the relevant tools and techniques straight out of the box~\cite{Pollyanna@COSN13}.

In this study we present a focused case study by examining the content and volume of users' brand engagement on OSNs to determine the effect of choice of promotion channel on a brand's influence.\footnote{In this work, engagement is defined as adoption of the content by e.g., replying to a tweet, mentioning the brand name, or including the hashtag in a tweet. We are not able to measure external engagement such as sharing content on other OSNs, or clicking on the links in the tweet.} We do this by analysing the engagement level  of Twitter users, their adoption of brand hashtags, and the sentiment they express, to determine the similarities and differences between two separate advertising strategies on this network: \textit{promoted tweets}, and \textit{promoted trends}. We pose a number of questions regarding brands and advertising on OSNs: How does the sentiment for a promotion strategy spread over time? What are the engagement levels for each day of promotion? What is the engagement level (e.g. \emph{retweets} and \emph{mentions}) for promoted brands and how do these affect the sentiments expressed towards a brand?

In order to answer these questions, we use Twitter's Streaming API service to
collect engaged users' profiles and tweets in regards to promoted influences
(tweets and trends) over a busy 10 day shopping period for a selection of brands
across different industries. We observe the need to accurately filter the
resulting tweets for topical relevance, and compare simple keyword-based methods
with a discrimative Machine Learning (ML) approach. We then classify the tweets
by sentiment (positive, negative or neutral), and again compare a range of
existing methods and tools.  We then use this data to establish the driving
factors behind the success of promoted influences and differences between
advertising strategies.  For both tasks, the choice of classification method
makes a significant difference, highlighting the care that must be taken
when choosing techniques for this kind of analysis.

The rest of the paper is organized as follows: In Section~\ref{sec:related} we present the some recent related studies. In Section~\ref{sec:data} we describe our case study, dataset and its characteristics. In Section~\ref{sec:classify} we briefly discuss our sentiment analysis and text classification methodology and the challenges which only become apparent upon thorough manual inspection of the data. Section~\ref{sec:results} presents our results and the insights gained from our analysis. We conclude the paper and present potential future directions in sentiment and content analysis in Section~\ref{sec:conclusion}.

\section*{Acknowledgments}
The authors thank Chatterbox\footnote{\url{http://chatterbox.co}} for providing unlimited access to the Sentimental API.

\vspace{0.3cm}
 \bibliographystyle{abbrv}

\section{Related Work}
\label{sec:related}

\subsection*{Influence on OSNs}

Our primary interest in this work is in understanding the factors which govern
the effectiveness and influence of campaigns on OSNs. Several recent studies
have examined individuals' influence on OSNs~\cite{Cha10measuringuser}, and the
effectiveness of online advertising~\cite{Chan:2011:MLV:2047529.2047535,
  blake2013consumer}, but little attention has been paid to identifying the
driving factors behind a brand's influence on their social audience (although it
has been noted that brand names are more important online for some
categories~\cite{Degeratu200055}).  Cheung {\it et al.}~\cite{cheung2010effectiveness}
examined the way information spreads differently within social networks as
opposed to word-of-mouth (WOM) broadcasting, by focusing on electronic
word-of-mouth (eWOM), showing comprehensiveness and relevance to be the key
influences of information adoption. The closest work to ours in understanding
brands on Twitter is the study by Jansen~{\it et al.}~\cite{ASI:ASI21149}, who found
that 20\% of tweets that mentioned a brand expressed a sentiment or opinion
concerning that company, product or service. Here, we examine and compare such
mentions and sentiments across different promotion strategies available to
brands on Twitter, thus specifically investigating advertising effectiveness (see
Section~\ref{sec:data}).\footnote{Data availability limits us to effects within
  OSNs; we cannot determine effects on actual clicks or sales.}

In a study on the spread of hashtags within Twitter,
Romero~{\it et al.}~\cite{Romero:2011:DMI:1963405.1963503} used over 3 billion tweets
2009-2010 to analyze sources of variation in how the most widely used hashtags
spread within its user population. Their results suggested that the mechanism
that controls the spread of hashtags related to sports or politics tends to be
more persistent than average; repeated exposure to users who use these hashtags
affects the probability that a person will eventually use the hashtag more
positively than average. However, they only examined hashtags that succeeded in
reaching a large number of users. In regards to the focus of promoted influences
within Twitter, this raises the question; what distinguishes a promoted item
that spreads widely, possibly with positive sentiment, from one that fails to
attract attention or is associated with mainly negative sentiment? Our study
aims to answer this by examining the sentiment and spread of tweets in relation
to brands' promoted items.

\subsection*{Analysis Methods}

Sentiment analysis has been approached across many domains, including products,
movie reviews and newspaper articles as well as social media (see e.g
\cite{Pang.Lee08} for a comprehensive overview). Typically, the methods employed
depend either on existing language resources (e.g. sentiment dictionaries or
ontologies) or on machine learning from annotated datasets. The former can
provide deep insight, but are somewhat inflexible in the face of the
non-standard and rapidly changing language used on OSNs, for which few suitable
linguistic resources currently exist. The latter are more scalable and can be
trained on relevant data (e.g.~\cite{mejova2012crossing}), but generally depend
on large amounts of manual annotation (expensive and often problematic in terms
of accuracy) and in some cases the existence of grammatical resources for the
language and text domain in question (e.g.~\cite{Socher.etal13}). However, some
approaches leverage the existence of implicit labelling in the datasets
available (\emph{distant supervision}), to avoid the necessity for manual
annotation: for example, user ratings provided with movie or product
reviews~\cite{Pang:2002:TUS:1118693.1118704,Chan:2011:MLV:2047529.2047535}); or
author conventions such as emoticons and hashtags on
OSNs~\cite{Go.etal09,Pak.Paroubek10,Purver.Battersby12}). Hybrid approaches also
exist, e.g. the use of predefined sentiment dictionaries with weights learned
from data (e.g.~\cite{Thelwall:2012}).

Identifying the topic of text has also received much attention in NLP research,
with methods ranging from the use of existing topic resources or ontologies
(e.g.~\cite{malo2010semantic}) to unsupervised models for discovery of topics
(e.g.~\cite{Ritter.etal10}). The use of machine learning to detect the relevance
(or otherwise) of text to a known topic also has a long history, perhaps most
well-known in the form of Na\"{i}ve Bayes filtering for spam filtering
\cite{Sahami.etal98}.

However, research into OSN behaviour or influence sometimes ignores the spread
of sophisticated methods available. Sentiment analysis is often performed based
on defined dictionaries (e.g.~\cite{Tumasjan.etal10}), and topic identification
is often ignored, with datasets filtered purely on keywords or simple Boolean
queries.  Recently, Goncalves~{\it et al.}~\cite{Pollyanna@COSN13} examined the
difference in performance across various sentiment analysis approaches on online
text, finding significant variations. The effect of these variations in a
specific analysis problem is less clear, though: how much does the variation in
sophistication (and accuracy) of these methods actually matter?
\cite{Quercia.etal11b} compared statistical and lexicon-based methods and found
significant differences at the level of individual messages, although a
correlation at the level of their intended analysis (user profiles). Here, we
investigate the effect when considering individual advertising campaigns
(promoted items). For text relevance, we compare the use of keywords to Na\"ive
Bayes classification via Weka \cite{Hall.etal09}. For sentiment analysis, we
examine three existing and freely available tools: the widely-used Data Science
Toolkit's
\texttt{text2sentiment}\footnote{\url{http://www.datasciencetoolkit.org/}} based
on a sentiment lexicon \cite{Nielsen11}; the lexicon-based but
data-driven hybrid SentiStrength \cite{Thelwall:2012}; and a statistical
machine-learning-based approach, Chatterbox's
Sentimental\footnote{\url{http://sentimental.co/}}
(see~\cite{Purver.Battersby12}).

\section{Results}
\label{sec:results}

\begin{figure}[t!!]
\centering
  \includegraphics[width=3.3in, height=2.5in]{distpromotweets}
 \caption{Distribution of promoted tweets volumes over time.\label{fig:disttweets}}
\end{figure}

\begin{figure}[t!!]
\centering
  \includegraphics[width=3.3in,height=2.5in]{distpromotrends}
 \caption{Distribution of promoted trends volumes over time.\label{fig:dists}}
\end{figure}

\subsection*{Response volume over time}

To examine the spread of engagement for each promoted item over the 10 day
period, we analysed the volume of unique tweets each day in response to each
promoted item, then averaged the results across all
brands. Figures~\ref{fig:disttweets} and~\ref{fig:dists} display the distribution of this volume in
response to \emph{promoted tweets}~(\ref{fig:disttweets}) and \emph{promoted
  trends}~(\ref{fig:dists}) per brand.
On average, promoted trends led to much higher response volumes. However, the
highest percentages of \emph{mentions} within responses were from promoted
tweets, where an average of 18\% of tweets each day included an `@' mention to
the brand; promoted trends had an average of only 15\% mentions per day. This indicates that for a brand to successfully
engage users in the content of the promoted item, a promoted tweet is better for
this purpose. For example, out of the O2 Network's $\sim$30,000 tweets, 7,965 included an `@'mention to the brand (25\%).

Results confirmed that the greatest percentage of engagement for a brand's
promoted item takes place on the first day of promotion.
On average, 24\% of engagements around the
promoted item take place on the first day. The effect is most
pronounced for \emph{promoted trends}, with 34\% of engagement on average on the
first day of promotion, after which the engagement falls dramatically by an
average of 25\% to 9\% by day two and continues to fall thereafter, even if the item is promoted for several days.  For
\emph{promoted tweets}, the effect is less pronounced: 19\% of the engagement
takes place on the first day of promotion, with engagement decreasing by 8\% by
the second day of promotion. However, it does not continue on a steady decline
thereafter, but it rises and falls over the next 8 days, although never again
reaching the peak of the first day of promotion. This could be due to the fact
that a promoted tweet is usually promoted for several days on Twitter where it
occasionally appears at the top of different user's timeline were users are
repeatedly exposed to the item. This finding can be said to conform to
Romero~{\it et al.}'s theory of \textit{repeated
  exposure}~\cite{Romero:2011:DMI:1963405.1963503}.\footnote{Also see
  \small{http://advertising.twitter.com/2013/03/Nielsen-Brand-Effect-for-Twitter-How-Promoted-Tweets-impact-brand-metrics.html}} They found that repeated exposure to a hashtag within Twitter had a significant marginal effect on the probability of adoption of that hashtag.

In general, though, these results show that adoption of a promoted item is not
a slow gradual shift over several days (as might be assumed) but rather an
immediate incline when exposure to the item is new to users.

\subsection*{Effects on user sentiment}

The sentiment breakdown for each promoted brand item can be observed in
Figures~\ref{fig:brandsentimentml} and ~\ref{fig:brandsentimentkey}, with
Figure~\ref{fig:brandsentimentml} showing the results obtained using our chosen
machine learning method and Figure~\ref{fig:brandsentimentkey} those obtained
using a keyword-based method (see section~\ref{sec:classify} above). We observe
that in most cases, the percentage of positive sentiment was higher than that of
negative and neutral for promoted items. Notable exceptions are the results for
two brands, NiveaUK and O2, where neutral and/or negative levels outweigh
positive; the ASOS brand also shows little difference between negative and
positive levels. However, comparison of the figures that would have been gained
using a keyword-based approach (Figure~\ref{fig:brandsentimentkey}) shows
misleading results in precisely these interesting cases: apparent positive
levels are higher than negative in all cases. Neutral cases also appear much
more common; this is due to the low coverage of the keyword lexicon causing
large numbers of results with apparently zero sentiment. Use of the more
accurate tool (as objectively assessed -- see section~\ref{sec:classify})
therefore does appear crucial.

\begin{figure}[h!]
  \includegraphics[width=\columnwidth]{brands-chatterbox}
  \vspace{-0.5cm}
  \caption{Sentiment analysis by brand - machine learning}\label{fig:brandsentimentml}
\end{figure}

\begin{figure}[h!]
  \includegraphics[width=\columnwidth]{brands-dst}
  \vspace{-0.5cm}
  \caption{Sentiment analysis by brand - keywords}\label{fig:brandsentimentkey}
\end{figure}

On average, across all brands (promoted tweets and trends), the average
percentage of tweets and retweets\footnote{We assume that retweeting users share
  the same sentiment as the original tweet.}  which contained a positive
sentiment is 50\%, that which contained a negative sentiment is 12\%, and 38\%
of tweets had a neutral tone.

Figures~\ref{fig:positivesentiment} and~\ref{fig:negativesentiment} then show
the distribution of positive and negative sentiments in this response traffic
over time. On average, positive sentiment outweighs negative sentiment; on the
first day, 49\% of the tweets were positive. In general, \emph{promoted tweets}
lead to more positive sentiment and less negative sentiment than \emph{promoted
  trends}.

\begin{figure}[t!]
    \centering
    \includegraphics[width=3.3in, height=2.5in]{positivesentiment}
    \caption{Positive sentiment distribution over time}
     \label{fig:positivesentiment}
\end{figure}

\begin{figure}[t!]
	\centering
    \includegraphics[width=3.3in, height=2.5in]{negativesentiment}
    \caption{Negative sentiment  distribution over time.}
 \label{fig:negativesentiment}
\end{figure}

In total, 47\% of tweets relating to a \emph{promoted tweet} are positive in
sentiment. Day one received the highest percentage of positive sentiment tweets
(58\%); positive sentiment then continues to dominate over the 10 day period,
never falling below 36\% of the tweets.  Examining \emph{promoted trends}, we
found that, on average, only 37\% of tweets relating to a promoted trend
contained a positive sentiment. On the first day of promotion, 26\% of tweets
expressed a negative sentiment, 32\% expressed a positive sentiment and 42\%
expressed no sentiment at all. This shows that Twitter users do not tweet as
positively about a promoted trend as they would about a promoted tweet. Instead,
a large proportion of tweets relating to a promoted trend contained no emotional
words, or if they did, the positive and negative sentiments balanced each other
out. They generally contained just the promoted hashtag or generally had an
objective, matter-of-fact tone (e.g., - ``\texttt{Get 3G where I
live... \#O2WhatWouldYouDo}'').

Taken together with the analysis of engagement volume, these results show that
when an item is promoted, the brand and the item get adopted immediately and
regarded quite positively by the engaged users. Twitter users welcome the
promoted item on Twitter, which has a positive effect on the tweets
expressed. The engagement level reduces to an average of 10\% of the total
tweets on day two, when the item is no longer being promoted, or is no longer
seen as ``new and interesting''. However, on average, the positive sentiment
expressed still outperforms that of negative sentiment and neutral sentiment
each day.

\subsection*{Effect of hashtags on engagement and sentiment}

\begin{figure}[t!!]
\centering
  \includegraphics[width=3.3in, height=2.5in]{asus}
 \caption{Hashtag related engagements for ASOS.\label{fig:hashtag-asos}}
\end{figure}

\begin{figure}[t!!]
\centering
  \includegraphics[width=3.3in, height=2.5in]{vauxhall}
 \caption{Hashtag related engagements for Vauxhall.\label{fig:hashtag-vaux}}
\end{figure}

We then performed two example case studies, using the ASOS and Vauxhall brands,
to examine the use of hashtags within promoted items. Figures~\ref{fig:hashtag-asos} and~\ref{fig:hashtag-vaux} show the results. ASOS promoted a trend, \#AsosSale, on the $19^{th}$ and $20^{th}$ of
December to highlight their Boxing Day sale on the $26^{th}$ of December (day 8
of data collection).  Although the \textit{promoted} hashtag was virtually discarded by
day two of data collection, we found that user engagement (use of hashtag, mentions and tweets) for the forthcoming sale
continued. This trend is also apparent in Vauxhall's tweet volumes for their
sale which stated on the $27^{th}$ of December (day one of promotion), and ended
the day after our 10 day data collection period. The engagement for Vauxhall
remained at a consistent level throughout the event (see Figures~\ref{fig:dists} and \ref{fig:hashtag-vaux}), despite the rapid drop-off in use of the promoted hashtag.


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text:
Practical support for people struggling with funeral costs
How we can help>
Funeral poverty>
Down to Earth>
Raising money toward funeral costs
Money from the deceased person's estate
Arranging a funeral can be daunting at a time when you're feeling vulnerable.
It may be that you've overlooked possible sources of funding to help towards the costs, and that may include money from the estate of the person who has died.
The first priority for any money left by the deceased person is to help pay towards the costs of their funeral.
You don't need to wait for probate or the will (if there is one) to be read.
In order to release money from a bank account, you can take a copy of the death certificate and a copy of the funeral bill to the bank. Many banks will release the money directly to the funeral director (if you are using one).
Is there an insurance policy?
Is there a work related pension?
Is there any money in any of the deceased person's bank accounts?
Potential support from job related organisations?
Next - Government support
Charles' story
Charles took responsibility for arranging his cousin's funeral but was not eligible for any state benefits.
However, when sorting through paperwork, details of two insurance policies were found. A Down to Earth advisor contacted the insurance provider and helped Charles complete the necessary paperwork. On New Year's Eve he received a cheque from the insurance company with sufficient funds to pay for the service he wanted.
Contact Down to Earth
Email: downtoearth@qsa.org.uk
Please note, we are only able to provide 1:1 support when a funeral has not yet taken place. If a funeral has already happened please use our guide.
Planning an affordable and meaningful funeral
10 steps to an affordable funeral
Planning for the cost of a funeral
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Le Batard and the shipping container discuss Chris putting raisins in his picadillo and Dwyane Wade's final home game.
On today's show, can Adam Schefter pee while holding three cell phones?
Le Batard, Stugotz and Stan Van Gundy are joined by NBA Hall of Famer, Gary Payton and Brandone Francis from the Texas Tech basketball team.
Stan Van Gundy gives us his NBA nuggets and Marty Smith tells one of the worst stories ever told on the show.
Le Batard, Stugotz and Stan Van Gundy debate what five movies they'd bring if they were stranded on an island and we take more calls on celebrity bathroom encounters.
Le Batard and the shipping container discuss the emotions going into Dwyane Wade's final games and David Samson joins the show.
On today's show, do you miss the flabby 7 footer in the NBA?
Le Batard and Stugotz are joined in studio by Michael Smith. We discuss Game of Thrones and the Golden State Warriors.
Le Batard and Stugotz discuss Russell Wilson wanting a new contract and Allyson tells the story of when Stugotz fired her.
Le Batard and the shipping container discuss the Miami Heat and Miami Marlins. Also, Mike is excited about UM spring practices.
On today's show, do you think of Bill Belichick as hefty?
We're joined by Ron Magill and Greg Cote hosts the final segment of the show.
Le Batard, Stugotz and Greg Cote discuss the Cleveland Browns upcoming season and Greg has his Back In My Day.
Le Batard, Stugotz and Greg Cote discuss Stugotz's appearance on Around the Horn and Jay Bilas joins the show.
It's a wheel of topics kind of Tuesday! We discuss the Miami Heat, Quentin Tarantino movies and Randy Scott has a Hack In My Day.
On today's show, did Jason Witten lie to us?
Le Batard and Stugotz discuss Odell Beckham Jr. showing up to Browns offseason workouts and Stugotz has his Weekend Observations.
Le Batard and Stugotz discuss Greg Schiano stepping down as Patriots defensive coordinator and we have Funniest Thing From The Sports Weekend.
Le Batard and Stugotz discuss Duke losing to Michigan State and Tony Reali joins the show to discuss Stugotz potentially being on Around the Horn.
Stugotz and the shipping container discuss Mike wanting to get a tattoo and Stugotz appearing on Around The Horn.
On today's show, will you eat a sandwich in the bathroom?
Le Batard and Stugotz are joined in studio by Hank Azaria. He shares an interesting fact about The Simpsons and we open up the club!
Le Batard and Stugotz are joined in studio by Hank Azaria and Tim Kurkjian joins the show.
Le Batard, Stugotz and Hank Azaria discuss the proper way to eat a pineapple and Hank does a bunch of his impressions.
Amin Elhassan and the shipping container discuss Miami Marlins Opening Day and the Miami Heat beating the Dallas Mavericks. Also, can you microwave Styrofoam?
On today's show, do the Utah Jazz have the credentials to hit you with a, "not on our watch"?
Le Batard, Stugotz and Amin Elhassan discuss Devin Booker's recent dominance and Action Bronson joins the show.
Le Batard, Stugotz and Amin Elhassan discuss the Jordan Bell story and Amin challenges Chris to a karaoke-off.
Le Batard and Stugotz discuss Jordan Bell getting in trouble with the Warriors and we're joined by Bear Grylls.
Le Batard and the shipping container are fired up about Opening Day! Chris and Billy go to Marlins Park to interview Lewis Brinson and David Samson joins the show.
On today's show, do you know why Joe Jackson was shoeless?
Le Batard and Stugotz discuss potential rule changes to basketball and we see who can make the best swish sound.
Le Batard and Stugotz discuss the toughest baseball players ever. Who wins a fight in their prime, Kyle Farnsworth or Nolan Ryan?
Le Batard, Stugotz and Amin Elhassan discuss Stugotz's fantasy baseball team and how he is incapable of being honest.
Le Batard, Stugotz and the shipping container discuss Billy's fantasy baseball team and Chris Bosh's jersey getting retired.
Sarah talks with Greg Cote about the series of mishaps that have led to his second wave of fame, why he should not have become friends with Dan Le Batard, the time he unwittingly wrote a story that was completely false, his favorite "Back in My Day"�, and his infamous cough.
On today's show, is there anything in life better than a good, hard, crisp piece of French bread dipped in olive oil?
We're joined by Zoo Miami's Ron Magill and Greg Cote has his Back In My Day.
Le Batard, Stugotz and Greg Cote are joined by Brian Windhorst and NBA Hall of Famer, Bob McAdoo.
Le Batard, Stugotz and Greg Cote discuss a turkey vulture crashing through the window of Stephen A. Smith's office. We're joined by Trey Wingo who is on the scene.
Le Batard, Greg Cote and the shipping container celebrate Chris Bosh's career with the Miami Heat.
The whole show is sad over Gronk's retirement. Stugotz also gave us his Weekend Observations and Stan shared his NBA Nuggets.
Stan Van Gundy shared his NBA Nuggets, the guys discussed the funniest thing from the weekend and Stugotz updated the polls.
Stugotz shared his Weekend Observations, the guys discussed Robert Kraft's apology and we updated our March Sadness tournament.
Stan Van Gundy joined Dan and Stugotz to honor Gronk, discuss Tom Izzo and break down UCF vs. Duke.
The Shipping Container plays a round of "What's Good?" Amin and Stan Van Gundy also joined the show to discuss basketball.
On today's show, is LeBron James a compiler?
Stugotz makes fun of Chris for planning a vacation Masters weekend and we open up the club!
Stugotz and Greg Cote are joined by Tim Kurkjian and we update our March Sadness tournament.
It is Wake and Take on a Friday! Stugotz and Greg Cote discuss the NCAA tournament and we try to find valuable Cheetos.
Le Batard, Greg Cote and the shipping container discuss Spring Break on South Beach and Inter Miami playing in Fort Lauderdale.
On today's show, who's Rick Pitino rooting for, his son or the school he's suing?
Stugotz and Izzy Gutierrez come up with a good way to avoid tanking in the NBA and Billy discusses the music that will play at his wedding.
Stugotz and Izzy Gutierrez inexplicably start the show talking about tennis and Daniel Baldwin joins the show.
Stugotz, Izzy Gutierrez and the shipping container discuss the Heat beating the Spurs last night and Chris goes through his show notes.
On today's show, do you throw the yellow Starburst off to the side?
Le Batard, Stugotz and Stan Van Gundy discuss corruption in college sports and we update our March Sadness tournament.
Le Batard, Stugotz and Stan Van Gundy are joined in studio by Mardy Fish and we discuss Zion Williamson's future in the NBA.
Le Batard and the shipping container talk airplane etiquette with Stan Van Gundy and we're joined by David Samson.
Le Batard, Stugotz and Greg Cote discuss everything going on with the New York Giants and Chris makes one of the best/worst jokes ever told on the show.
Le Batard, Stugotz and Greg Cote discuss Mike Trout signing the biggest contract in sports history and Marty Smith joins us to make a big announcement.
Le Batard, Stugotz and Greg Cote discuss Mike Trout signing an extension with the Anaheim Angels. Jeff Passan joins us to discuss.
Greg Cote and the shipping container discuss the Dolphins moving on from Ryan Tannehill and Greg says he's thinking about getting a tattoo.
On today's show, if Gardner-Webb had Zion Williamson would they be a Final Four favorite?
Le Batard and Stugotz are joined in studio by former NBA player, John Amaechi. We play one of our favorite games, Stump The Maech.
Stugotz has his Weekend Observations and we discuss everything that has happened with the Pittsburgh Steelers.
Stugotz proves he can fake his way through the college basketball tournament and we unveil our March Sadness tournament.
Izzy Gutierrez and the shipping container discuss Ryan Tannehill's tenure with the Miami Dolphins and the Miami Heat season coming to an end.
On today's show, can Warren Buffett make you disappear?
Stugotz, Izzy Gutierrez and the shipping container discuss the Syracuse player tripping Zion Williamson and we open up the club!
Stugotz, Izzy Gutierrez and the shipping container discuss Odell Beckham Jr. signing with the Cleveland Browns and Zion Williamson's dominance.
It is Wake and Take on a Friday! We discuss Zion Williamson's future at Duke and whether Tiger Woods will win a major this year.
Izzy Gutierrez and the shipping container discuss some of life's simple pleasures and what's going on with all the construction in Miami.
On today's show, is John Mayer a dish?
Stugotz and Greg Cote are joined by Mike Greenberg and we imagine what literal coaching trees would look like.
Stugotz and Greg Cote discuss Zion Williamson's future with Duke basketball and Stugotz's budding friendship with George R. R. Martin.
Stugotz and Greg Cote discuss the Golden State Warriors recent struggles and whether or not we should feel good for Browns fans.
Greg Cote and the shipping container discuss the Miami Dolphins game plan for next season and Chris says crawfish is overrated.
On today's show, who is going to be more annoying today, Mike Ryan or Stugotz?
Le Batard, Stugotz and Stan Van Gundy debate how good the Browns will be this season and we're joined in studio by former NFL player and current magician, Jon Dorenbos.
Le Batard, Stugotz and Stan Van Gundy are joined by Stugotz's wife, Abby, and the director of the Richard Pryor documentary, Jesse James Miller.
Le Batard, Stugotz and Stan Van Gundy discuss the latest NFL free agency news. Mike and Stugotz are obnoxiously excited about who their teams signed.
Le Batard, Stan Van Gundy and the shipping container discuss Odell Beckham Jr. signing with the Browns and the Miami Heat potentially making the playoffs.
On today's show, is Bill Belichick going to turn Blake Bortles into Joe Montana.
Le Batard, Stugotz and Greg Cote are joined by Zoo Miami's Ron Magill and XFL commissioner, Oliver Luck.
Greg Cote has his Back In My Day and we marvel at the absurdity of the latest college sports scandal involving Aunt Becky from Full House.
Le Batard, Stugotz and Greg Cote discuss NFL free agency and Steve Kerr's comments about Draymond Green.
Le Batard, Greg Cote and the shipping container discuss the Miami Dolphins, Billy going to Marlins Spring Training, and Greg's career as a college soccer goalie.
Sarah talks with Dan Le Batard about his childhood in Miami, his reluctant climb in sports media, the moment he lost interest in writing about sports, the pressure of conducting the sloppy orchestra that is his radio show, his relationship with Stugotz, whether or not he's concerned about burning out, and the time he pooped in a parking lot.
On today's show, are bats capable of being cute?
Le Batard and Stugotz are joined in studio by Sarah Spain and we have Funniest Thing From The Sports Weekend.
Stugotz has his Weekend Observations and we're joined by Bill Barnwell.
Le Batard and Stugotz discuss Antonio Brown getting traded to the Raiders and we play sound of Jeff Van Gundy saying the Lakers should consider trading LeBron James.
Le Batard and the shipping container discuss the Miami Heat, baked potatoes and we do a Life Hack Attack.
On today's show, what kind of person are you, close your eyes and feel something or open your eyes and dance?
Le Batard and the shipping container are joined by Gary Sinise and we open up the club.
Le Batard, Greg Cote and the shipping container discuss Ronda Rousey potentially leaving the WWE and Wright Thompson joins the show.
Le Batard, Greg Cote and the shipping container discuss the Michael Jackson documentary, Leaving Neverland, and Antonio Brown potentially getting traded.
Le Batard, Greg Cote and the shipping container discuss the Miami Dolphins and we debate whether it's ok to look up a set list before attending a concert.
On today's show, are you surprised Stan Van Gundy isn't a sweatpants guy?
Le Batard, Stugotz and Stan Van Gundy discuss the Golden State Warriors recent struggles and Vincent D'Onofrio joins the show.
Le Batard, Stugotz and Stan Van Gundy share funny stories from when they were umpires and Stan gives us his NBA nuggets.
Le Batard and Stan Van Gundy discuss whether people at ESPN like our show and we have a surprise for Dan.
Le Batard and the shipping container discuss what it's like working in South Beach and we're joined by former Miami Hurricane, Joaquin Gonzalez.
Le Batard and Izzy Gutierrez debate whether the Golden State Warriors are vulnerable after their recent struggles and we throw tomatoes at Chris Cote's face.
Le Batard and Izzy Gutierrez discuss the Michael Jackson documentary, Leaving Neverland, and Kevin Arnovitz joins the show.
Le Batard, Izzy and the shipping container discover Bob Costas' middle name and realize that we need to come up with a middle name for Izzy.
Le Batard, Izzy Gutierrez and the shipping container discuss the Miami Marlins and bread. Also, David Samson joins the show.
On today's show, do you still consider Pluto a planet?
Le Batard and Greg Cote are joined by Ron Magill and John Turturro.
Greg Cote has his Back In My Day and we're joined by Derek Murphy, a marathon investigator.
Le Batard and Greg Cote discuss a minor league baseball team wanting to have Greg Cote Day and Antonio Brown getting paid to send messages to fans.
Le Batard, Greg Cote and the shipping container discuss the cruise Greg was on this past weekend and the Miami Heat barely beating the Atlanta Hawks.
On today's show, do you do sing-alongs in the car?
Le Batard and Amin Elhassan are joined by Tom Haberstroh and we discuss Guillermo's bathroom habits.
We have Funniest Thing From The Sports Weekend and Amin Elhassan is in full Stugotz mode today.
Le Batard and Amin Elhassan discuss the Lakers potentially not making the playoffs and we play interesting sound from the Sloan Analytics conference.
Le Batard, Amin Elhassan and the shipping container discuss the Michael Jackson documentary, Leaving Neverland, and the most unnecessary hall of fames.
Chris Bosh tells us how tall he is, fake Stugotz has a Sports Whisperer and we open up the club!
Le Batard and Stan Van Gundy are joined by Brian Kenny and Stan gives us his NBA nuggets.
Le Batard, Stan Van Gundy and the shipping container discuss Bryce Harper signing with the Phillies and we celebrate Stugotz's incompetence.
Le Batard and the shipping container debate where Dwyane Wade ranks all time and we play one of our favorite games, What's Good?!
On today's show, when Stugotz uses "in fact", is it ever a fact?
Le Batard and Stugotz discuss everything going on with the Boston Celtics and we clear the computer of all the Jim Zorn sounds we have.
Le Batard and Stugotz are joined by Bill Simmons and we recap the season finale of The Masked Singer.
Le Batard, Stugotz and the shipping container debate what kind of French fries are the best and Tim Kurkjian joins the show.
Le Batard and the shipping container discuss Dwyane Wade's game winner against the Golden State Warriors and David Samson joins the show.
On today's show, would you chase goats or run from goats?
Le Batard and Stugotz are joined by Ron Magill and we find out who the best NBA small forwards were in the 1980's.
Le Batard and Stugotz discuss Dan running errands with Amin Elhassan and Stugotz gets sent to the therapy couch.


text:
LET'S WELCOME CHRISTI SNOW AUTHOR OF: TASTY LOVE
AUTHOR OF:
Rad-Reader: What made you want to tell this story?
Christi: When Sarah and I created the Aspenridge world, several stories were there from the very beginning. Although we didn't know the specifics of it at the time, we knew that Spencer and Val were one of those couples who deserved their 'second chance at love'. Both of them were in the original trilogy (the Naked Brews series).
Rad-Reader: How do you go about writing one book together with another author?
Christi: Honestly, it's easier than it sounds. Writing typically is a very lonely affair. It is so rewarding and gratifying to work with someone else, to have someone else to bounce ideas off of and to have sitting there cheering on when you get a scene 'just right'.
Personally, I think the key to writing together successfully is pre-planning. Sarah and I have a huge pre-writing process. We establish huge character profiles along with detailed photos of our characters and locations so we both have the same picture in our head when we write a scene. Then each scene/book is meticulously outlined/plotted before writing so we know exactly where we're going.
Rad-Reader: How did you come up with this book idea?
Christi: Honestly, I'm not sure exactly where the story came from...our brainstorming sessions tend to take on a life of their own.
We knew the conflict between Spence and Val went back to their abrupt breakup and the relationship that resulted between Taylor and Val. To have Taylor eventually die and be the cause of Spencer's mother's death was something that neither of them had ever dealt with...thus their conflict.
They had to be put in a position where they couldn't ignore what had happened anymore...thus the Safety Corridor campaign...where they had to confront their loved ones dying while at the same time being thrown into a close situation like having to create Public Service Announcements together about being left behind when someone makes the mistake of driving carelessly.
Rad-Reader: How long did it take you to write this book?
Christi: Sarah and I have our writing process down. We write in alternating points of view and each writes a scene a day (I write until noon, she writes the rest of the day and sends me her scene sometime in the PM so I'm ready to write again early the next day). As a result, we can write an entire first draft in 21 days (approximately 40 scenes/book).
Rad-Reader: I have seen there are two books in this series but how do you work it with your writing partner? As far as writing who write the first half, second half, etc.…?
Christi: Like I said before, we write together, alternating scenes. Each chapter in the book alternates point of view. In this book, Sarah wrote Val and I wrote Spence...but we both heavily edit the entire book so the books don't seem like they're written from two different people.
We break up all the other publishing tasks between us, depending on where we're at in our own publishing schedule and real life.
For the pre-planning, I write all the character bios and am in charge of finding character and location photos. We get together and plan a vague outline and then Sarah goes and creates our "beat sheets" or outlines.
Rad-Reader: Can you give us some backstory on Val and Spencer that the blurb/synopsis doesn't give us?
Christi: Val and Spence were high school sweethearts. She was the sexy cheerleader that everyone liked while Spencer was the smart awkward geek. He had no idea she even knew who he was until she approached him. They didn't date very long...only a couple of months, but those were intense, real months with real emotions.
But Spencer was destined for bigger things and spent spring break visiting Berkley where he planned to attend to eventually go to med school. A year younger than him, Val wasn't handling the idea of him moving to California and leaving Colorado very well. She made a mistake and went to a party the week he was gone which resulted in her making an unholy alliance with Taylor and having to break up with Spencer.
Rad-Reader: How did you develop their characters?
Christi: I swear we don't have to develop characters. They just arrive on the scene fully formed. We just have to figure out why they are the way that they are. Like I said, both Val and Spence were in the original series, so we had a pretty good idea who they were already.
Rad-Reader: Was Val mad at Spencer when she texted and broke up with him?
Christi: No, she was heartbroken.
Rad-Reader: I am getting the bad feeling Taylor may have been abusing Val. I don't know why it's just by the conversation Val has with his mom.
Christi: There are different layers of abuse. Taylor was at his heart a manipulator and Val was a prime target for him.
Rad-Reader: What does Em-Dash mean?
Christi: LOL...it's probably more of writing reference than a bookstore reference...and we're authors who happen to love our em-dashes.
In this book, Em-Dash is the name of the bookstore, which I adore (yes, I come up with all the names...hehe). But when you're reading a book, the information placed within Em-Dashed tends to deepen the story. With Em-Dash being at the heart of our village of Aspenridge all our characters float through there at some point...thus adding to their stories.
Rad-Reader: Spencer is being an ass to Val. Why doesn't he just ask her? What happened back then when she broke up with him? Yet, he wants to be mean. Why?
Christi: Spencer always thought Val was too good for him (remember, he may be a hot cop/detective now, but when he was in love with Val, he wasn't that guy...he was a teenager with tons of self-doubt and confidence issues). Beyond the fact that he fell in love with her, Val tried to show him that he could trust her to like him because of who he was...and then she betrayed that in the worst way...by breaking his heart and dumping him for another guy.
The thing is that he's never gotten over her, so he's mean to her now because he NEEDS her to keep her distance. He knows if she ever gets too close that he isn't strong enough to resist her. So he's mean...and it's heartbreaking (yes, to the author as well as the reader...)
Rad-Reader: Gavin must have felt bad when he saw that he hurt her feelings? Since the next day, they asked her to help with the lights and he helped with her heater, Right?
Christi: All the Murdock brothers are amazing guys. They are also zealously protective of women.
Because she broke Spencer's heart, they have a really hard time with Val. Seriously, everyone in town loves her. She just has that kind of personality. But the brothers' loyalty to Spencer means that they can't like her. They aren't actively mean to her, but they aren't going to go out of their way to help her. But at the same time, they know that Spencer never got over her so when they see a possibility to fix things between the two of them, they help the process along a little bit.
Rad-Reader: What does Spencer's dad think about her being there and about how she hurt Spencer?
Christi: I think dad more than anyone in the book was acting behind the scenes knowing that Val and Spencer needed to either work things out or get closure so that they can move on. Remember, dad could have put any one of his sons into this position close to Val and he chose Spencer (although he acted like the producers made that call). I think he's silently hoping they fix things between them.
Rad-Reader: Why has Spencer never asked why Val broke up by text and why she broke up with him at all?
Christi: Plain and simple...pride and hurt feelings. She broke him and to ask that question would reveal to her just how much it had all hurt him. He can't allow that.
Rad-Reader: When Val's secret leaks out to the town Spencer was on the way to the prison to see about Warner. Why did Val keep repeating the words, "I thought I knew you?" Why did that stand out to her?
Christi: I think a lot of it was that out of everyone, Spencer DID know her better than anyone ever did.
But she'd been living a lie...for years. To have him reject her for that stole the connection that she felt like they'd had beyond all the lies.
Rad-Reader: Didn't her parents get worried when they didn't hear from Val after two days?
Christi: She was on her way to her brother's house and her mother knew that Val was upset. Radio silence while she licked her wounds wouldn't have been inconceivable in those circumstances.
Rad-Reader: What took the boys coming around to at least accepting Val and not be Rude?
Christi: Once Spencer started softening toward her, they followed suit. Their number one loyalty was to Spence and they'd stand by his decisions. He softened, so they did, too.
Rad-Reader: If your book was made into a movie who would you want to play…
Val: Bianca Andrade (Boca Rosa w/red hair)
Spencer: Ryan Paevey
Travis: Ian Nelson
Mick:
Gavin: Liam Hemsworth
Miller: Bill Skarsgard
Warner: Valter Skarsgard
Noah: Ryan Murphy
https://www.famousbirthdays.com/people/ryan-murphy-swimmer.html
Christi: Okay, not so secret fact about me. I write approximately 6-10 books/year and read on average another 350 more. As a result, I really don't watch much TV or movies and don't know the actors. So, instead, I'll send you my photo collages of the main characters so you can see them as Sarah and I do.
Rad-Reader: When did you know you wanted to be a writer?
Christi: Isn't it every reader's secret hope to someday be a writer? As long as I can remember, I wanted to write a book. But it wasn't until 2011 that I actually sat down with the goal of really and truly writing a whole book. That was my first release, Operation: Endgame.
Rad-Reader: What would you tell a young writer just starting out?
Christi: Stick with it.
My daughter is 11 years old and she's an aspiring author (and honestly, a naturally talented one), but as yet hasn't managed to finish a manuscript although she writes on something every single day.
But that's what I tell her...stick with it, finish a book, then start another one. Write through the hard parts because we all have them in every single book. That moment or weeks of moments when the book seems like it is utter shit. That shit is fixable if you write it, but it's not if it's still stuck in your head.
Rad-Reader: When do you do your best writing am or pm?
Christi: I'm an AM writer...who works SO MUCH BETTER under deadline...LOL.
Rad-Reader: What is your favorite food?
Christi: pizza!
Rad-Reader: What is your favorite music?
Christi: Sadly, top 40. I love popular radio, but my 11 yr. is good with that. LOL
Rad-Reader: What is your favorite genre to read?
Christi: Contemporary romance
Rad-Reader: What are three things you can't leave home without?
Christi:
1.Kindle
2. back up book in case my Kindle dies
3. a journal in case I get hit with writing inspiration.
Rad-Reader: Are you in a writing group that you bounce ideas off of?
Christi: I used to be in a local group which was key to me having the confidence to move forward, but sadly, it's pretty much fallen apart. Now I have a couple of online writing buddies who keep me accountable and will let me work through writing issues if I need some help.
Rad-Reader: How long did it take to get your first book published?
Christi: I wrote Operation: Endgame the fall of 2011. I published it less than a year later after working through the manuscript at least 100 times.
Rad-Reader: Did you do it the traditional way or did you self-publish?
Christi: self-published
Rad-Reader: Where can our reader buy your books? Links
Amazon author page..
https://www.amazon.com/Christi-Snow/e/B008E71IJI?ref=sr_ntt_srch_lnk_1&qid=1550074021&sr=8-1
There are links to all my books and the universal links on my website...
http://christi-snow.blogspot.com/p/when-mission-ends-series.html
Rad-Reader: Where can our reader find you on the Web? Links
http://christi-snow.blogspot.com/
https://www.facebook.com/christisnowauthor
https://www.instagram.com/christisnowauthor/
https://www.goodreads.com/author/show/5849322.Christi_Snow
Bookbub:
https://www.bookbub.com/profile/christi-snow
https://twitter.com/ChristiSnow
snowchris_01@yahoo.com
Christi: Thank you so much for hosting me. This was a lot of fun!
Christi: Once again it was great having you with us. Your book was a pleasure to read. It had great twists and lots of conflicts but with a HEA. Just what I enjoy but most of all family and community involvement in the couple's business makes it so much more fun.
Christi Snow February 23, 2019 at 12:32 PM
Thank you so much for hosting me this week. It was so fun! <3


To me, Gajanan Madhav "Muktibodh" is more complex as a poet than as a critic. His poetry has such complexity of expression and is so incomprehensible that it makes no sense to me. Once, when I shared this issue with Pranay Krishna he told me that once he had explained his poetry to me I would be able to appreciate him. He was right because he taught Muktibodh and had command over his subject. But, what is the point of poetry that needs to be read under the guidance of an expert?


text:
Q: Error al convertir Json a String[] C# Un servicio me responde con la siguiente cadena json y quiero convertirla a un string[]:
string JSON = "{\"GetResult\":[\"200\",\"300\"]}";
string[] mensaje = new string[2];

JavaScriptSerializer js = new JavaScriptSerializer();
mensaje = js.Deserialize<string[]>(JSON);

Console.WriteLine(mensaje[1]);
Console.ReadLine();

Pero me da el siguiente error:

No hay ningún constructor sin parámetros definido para el tipo de 'System.String[]'.

¿Cómo podría convertir el json a un string[]?

A: Es por la estructura a la cual quieres desserializae. Es altamente recomendable usar una clase DTO para las estructuras de JSON.
class DeserializadaDTO{
    public string[] GetResult;
}

    string JSON = "{\"GetResult\":[\"200\",\"300\"]}";
    JavaScriptSerializer js = new JavaScriptSerializer();
    var mensaje = js.Deserialize<DeserializadaDTO>(JSON);

    Console.WriteLine(mensaje.GetResult[1]);
    Console.ReadLine();

El tener una estructura clase.array[string] es distinta a tener un array[string];

A: Te esta faltando la propiedad GetResultque contiene el array, si usas
http://json2csharp.com/
para obtener la clase a la cual deserializar, obtienes:
public class RootObject
{
    public List<string> GetResult { get; set; }
}

Usa la libreria json.net para deserializar
Newtonsoft.Json
Entonces quedaria
string JSON = "{\"GetResult\":[\"200\",\"300\"]}";
RootObject mensaje = JsonConvert.DeserializeObject<RootObject>(json);

foreach(var item in mensaje.GetResult){
    Console.WriteLine(item);
}

redpajama_set_name: RedPajamaStackExchange


Namibia, as a developing nation, has 97 percent of its local financial institutions and insurance companies being foreign owned, an official says.
Kabbe Constituency Councillor, John Likando, argued recently that 97 percent of financial and insurance institutions are not Namibian companies, but rather foreign companies that do not exist in the interest and aspiration of the Namibian people.
He said these foreign companies rather tape the existing opportunities in the financial sector with a capitalistic and monopoly approach.
Likando also claimed the corporate social responsivity of foreign financial institutions is hardly felt locally, compared to what they offer in the country of origin.
He said in Namibia, these companies need to be approached for them to assist in any field or sectors that requires help - unlike in their country of origin, where they instead ask their governments and communities on sectors that need financial support.
The councillor made the remarks during discussions regarding the Namibia Deposit Guarantee Authority Bill in the National Council.
The bill comes with an opportunity for the black majority community to create a bank that is wholly owned and created by Namibians, based on Article 98 of the Namibian Constitution.


text:
Revision as of 08:55, 19 October 2014 by Mith (Talk | contribs)
Dead Ringers was a comedy sketch show on BBC Two which frequently parodied and referenced The Lord of the Rings (film series) and the works of J.R.R. Tolkien more broadly. It ran for seven series, between 2002 and 2007.
1 Series 3
2 Places, People and Events
2.1 People
2.2 Places
2.3 Events
[edit] Series 3
In Episode 1, the use of Bigatures by Weta Workshop for The Lord of the Rings Trilogy was referenced in a sketch wherein Tony Blair claimed the Iraq War was all faked with the use of models; Blair asks Kirsty Wark, "I take it you've seen The Lord of the Rings?" to which she replies, "No, I fear dwarves.". Also, the show parodied How Clean Is Your House? with Aggie MacKenzie calling herself "the Gollum of grime" and the owner's house being described as "a cross between Mordor and Afghanistan".[1]
In Episode 2, parodying the fact that the Prime Minister of Thailand, Thaksin Shinawatra, had tried to purchase Liverpool Football Club, BBC News reports that "another foreign leader with a questionable human rights record", Saruman, had bought Newcastle United F.C. The manager, Bobby Robson, explains that Shelob and Sméagol (invisible thanks to The One Ring) will be playing for the team.[2]
The third episode combined the characters of the recently-returned, Auf Wiedersehen, Pet - a programme about seven fictional British construction-workers working abroad - whose characters go to Middle-earth in search of work. They are employed by Gandalf to rebuild The Shire following the Scouring of the Shire, but - in common with all series of Auf Wiedersehen, Pet - a nasty foreman turns up (Saruman) and one of the team falls in love with a local (an orc) who is then taught to speak Geordie.[3]
In a lengthy sketch in Episode 4, the Dead Ringers team are parodying the ending to The Return of the King film, and its length, by having a voice-over over their version of The Grey Havens: "Coming soon on DVD, a must-have for all fans of The Lord of the Rings. Yes, all of the extra endings to Return of the King that they couldn't fit in the film." Gandalf suggests they could catch a later boat - which Frodo suggests will come "about 9" - and that in the meantime they could play Scrabble. "We know how disappointed you were that the cinema release of The Return of King had only 23 final scenes, now experience the DVD director's cut with all 98 endings." Every time Frodo and Gandalf are about to leave, they have a reason to come back: Gandalf tells Merry that he is Merry's father; they have a custard-pie fight; they destroyed the Death Star. As they are about to leave (now with Merry, Sam and Pippin reading newspapers in a bored manner), a shark arrives. "You can relive the cinematic experience of starting to leave because you think the film has finished, only having to sit back down again and again... this time from the comfort of your own home. Every last bum-numbing ending is here."[4]
Finally, in Episode 6, Gandalf and Frodo are once again at the Grey Havens, beckoning Bilbo to the ship. Bilbo refuses, saying that he wants to go home and the elves dislike him; acting like an obstinate - and paranoid - elderly man, refusing to go into care, he insists, "I'm not getting on any boat! ... You're just after my savings. I went on a f***ing quest for you young hobbits, and this is all the thanks I get! You lay one finger on me, beardy, and I'll call social services!"[5]
[edit] Places, People and Events
The following - demonstrating an impressive knowledge of The Lord of the Rings - have been mentioned within the course of the seven series of Dead Ringers:
[edit] People
Bilbo[5]
dwarves[1]
elves[5]
Frodo[4]
Gandalf[4]
Gollum/Sméagol[2]
hobbits[5]
Merry[4]
orcs[3]
Pippin[4]
Sam[4]
Saruman[2]
Sauron[2]
Shelob[2]
[edit] Places
Dead Marshes[3]
Grey Havens[4]
Ithilien[3]
Middle-earth[3]
Mordor[1]
Parth Galen[3]
The Shire[3]
Valinor[4]
[edit] Events
Scouring of the Shire[3]
[edit] Miscellaneous
Bigatures (from the film trilogy)[1]
The One Ring[2]
Orkish[3]
[edit] Cast
Jon Culshaw: Sam[4]
Jan Ravens:
Aggie MacKenzie (called "Gollum"),[1] an orc,[3] Frodo[4]
Phil Cornwell: Saruman,[3] Bilbo[5]
Mark Perry: Gandalf[3]
Dead Ringers (comedy) at Wikipedia
Dead Ringers at IMDb
Dead Ringers on SeeSaw (series 3 to 6 available to UK residents)
Dead Ringers at TV.com
↑ 1.0 1.1 1.2 1.3 1.4 Dead Ringers, Series 3, Episode 1 (see 3:26 - 4:56 and 17:00 - 18:13). Originally broadcast on BBC Two on 10 May 2004
↑ 2.0 2.1 2.2 2.3 2.4 2.5 Dead Ringers, Series 3, Episode 2 (see 10:38 - 12.08). Originally broadcast on BBC Two on 17 May 2004
↑ 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10 3.11 Dead Ringers, Series 3, Episode 3 (see 16:30 - 18:16). Originally broadcast on BBC Two on 24 May 2004
↑ 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 Dead Ringers, Series 3, Episode 4 (see 9:23 -11:44). Originally broadcast on BBC Two on 31 May 2004
↑ 5.0 5.1 5.2 5.3 5.4 Dead Ringers, Series 3, Episode 6 (see 9:20 -10:19). Originally broadcast on BBC Two on 14 June 2004
Retrieved from "http://tolkiengateway.net/wiki/Dead_Ringers"
Category: Parodies


VANCOUVER, BRITISH COLUMBIA--(Marketwire - Jan. 26, 2012) - Sniper Resources Ltd. (TSX VENTURE:SIP)(FRANKFURT:A1H38M) ("Sniper" or "the Company") is pleased to announce that the Company's Phase 2 Drilling Project Plan of Operations commenced on January 24th at the Company's Overland Pass property located immediately north of Barrick's Bald Mountain Mine. The Company has engaged Boart Longyear as contractor for the drill program which will comprise 25 drill holes from 20 drill sites.
Overland Pass previously had 13 holes drilled by Sniper during 2006-2007 with very encouraging results. The present work plan is based, in part, upon results from that program and in part, is designed to test additional areas of favourable geology that could not be tested during that program because of the biological and cultural clearances required in those areas, some of which had known high grade Au surface sampling results.
All scientific and technical information contained in this news release has been reviewed and approved by Douglas H. McGibbon, a director and the Vice-President, Exploration of the Company, who is a "qualified person" within the meaning of National Instrument 43-101.
This news release includes certain forward-looking statements or information. All statements other than statements of historical fact included in this release, including, without limitation, statements relating to the potential mineralization and geological merits of the Overland Pass property, and other future plans, objectives or expectations of the Company are forward-looking statements that involve various risks and uncertainties. There can be no assurance that such statements will prove to be accurate and actual results and future events could differ materially from those anticipated in such statements. Important factors that could cause actual results to differ materially from the Company's plans or expectations include risks relating to the actual results of current and planned exploration activities; actual results of trench samples remaining to be verified by a qualified person; the geology, grade and continuity of any mineral deposits; fluctuating gold prices; risks associated with property option agreements, leases, joint ventures and the ability to conclude joint venture agreements on favourable terms; possibility of accidents, equipment breakdowns and delays during exploration; exploration cost overruns or unanticipated costs and expenses; availability of capital and financing required to continue the Company's exploration programs; general economic, market or business conditions; regulatory changes and restrictions including in relation to environmental liability; timeliness of government or regulatory approvals and other risks detailed herein and from time to time in the filings made by the Company with securities regulators. The Company expressly disclaims any intention or obligation to update or revise any forward-looking statements whether as a result of new information, future events or otherwise except as otherwise required by applicable securities legislation.


We are looking for the best street photography in the world shot in 2016, to make our fourth World Street Photography charity photo book. Curator of this competition is Michael Ernest Sweet.
Photos selected by the curator and the WSP team will be nominated for the book. In February 2017 the nominated photos will be curated again, the best photos then will find their in the fourth street photography book.


text:
Bloomberg to Reassess Campaign After Spending Half a Billion Dollars to Win American Samoa
One thing is clear: Billionaire and former New York City Mayor Michael Bloomberg did not have anything close to a good night on Super Tuesday. It was so not good, in fact, that it apparently might be the end of his campaign altogether. Bloomberg campaign officials said they'd take "another look to see if there's…
One thing is clear: Billionaire and former New York City Mayor Michael Bloomberg did not have anything close to a good night on Super Tuesday. It was so not good, in fact, that it apparently might be the end of his campaign altogether.
Bloomberg campaign officials said they'd take "another look to see if there's a reason to continue with this after tomorrow," NBC News' Josh Lederman reported.
Still, Lederman said, the campaign insisted that Bloomberg would spend "no small fortune" to help the eventual nominee, no matter who that ends up being. (The campaign of Sen. Bernie Sanders, however, has said it would reject Bloomberg's money.)
In public, the campaign projected optimism, with a press release insisting that only one-third of the delegates will have been awarded as of tonight.
READ: Early Super Tuesday results look like a blowout for Joe Biden
"No matter how many delegates we win tonight, we have done something no one else thought was possible," Bloomberg said during his remarks earlier in the night. "In just three months, we've gone from just 1% in the polls to being a contender for the Democratic nomination."


text:
Q: How to find the $f\left(x_i\right)$ for midpoint Riemann sum
$\begin{array}{|c|c|c|c|c|c|c|c|c|c|}
\hline
\mbox{Time} & \mbox{9am} & \mbox{10:30 am} & \mbox{Noon} & \mbox{1:30 pm} & \mbox{3 pm} & \mbox{4:30 pm} & \mbox{6 pm} & \mbox{7:30 pm} & \mbox{9 pm} \\
\hline
t & 0 & 1.5 & 3 & 4.5 & 6 & 7.5 & 9 & 10.5 & 12 \\
\hline
P\left(t\right) & 200 & 728 & 1193 & 1329 & 1583 & 1291 & 804 & 256 & 0 \\
\hline
\end{array}
$
Use a midpoint Riemann sum with four intervals of equal size to estimate the total number of people seeking care during the 12-hour period.

I know the formula for the midpoint Riemann sum is $ \int_a^b f\left(x\right) \; \mathrm{d}x \approx \frac{b-a}{n} \left[ f\left(x_1\right) + f\left(x_2\right) + \ldots + f\left(x_n\right) \right]$
I know that $b = 12$ and $a = 0$ and $n = 4$, but what is my $f\left(x_1\right), \; f\left(x_2\right), \; f\left(x_3\right), \; f\left(x_4\right)$

A: The midpoint of $[0,3]$ is $x_1=1.5$, so $f(x_1)=728$. The next interval to consider is $[3,6]$ with midpoint $x_2=4.5$. I think you can continue.

A: For $n$ points from $a$ to $b$,
$x_i = a + \frac{b-a}{n}(i-\frac1{2})$.
Note that $x_1 = a + \frac1{2}\frac{b-a}{n}$
is just to the right of $a$,
and $x_n = a+(n-\frac1{2})\frac{b-a}{n}
= b - \frac1{2}\frac{b-a}{n}$
is just to the left of $b$.


text:
Style/Titles/Series numbers
< Style | Titles
Revision as of 11:05, 15 August 2011 by LukasLalinsky (talk | contribs) (Style/Recording and release group titles/Series numbers moved to Style/Titles/Series numbers)
Status: This is an official style guideline.
Applies to release group titles and recording titles that are part of a series.
When an item is one of a series which are labeled as parts, volumes, etc., separate the number from the title by a comma and a space like so:
"Release group title, Volume 2"
"Recording title, Part 2"
See Volume numbers for specific details on Release groups.
See Part numbers for specific details on Recordings.
Note that the Abbreviations guideline says that you must not use abbreviations in titles with the only exception that Artist intent overrules the StyleGuidelines. In this case you need to provide information about why it is an exceptional case in your Edit Note!
"UK Space Techno, Volume 5"
"Café del Mar, Volumen Cinco"
Rationale and Details
The rationale behind the Series numbers guideline was so that we can create some consistency through groups of series.
In most cases this guideline is the only applicable rule, since it is rare and hard to prove that there is any real artist intent with series numbers. Also many series have a title scheme that is consistent (there are exceptions!), but still things like ", Vol. X" or "(Volume X)" are changed to ", Volume X".
However, if a particular way of labelling is consistently (at least in the majority of cases) used in a series, then that can be retained when these names are entered into MusicBrainz. For example, these things are usually not changed by SeriesNumberStyle:
Number schemes other than "1, 2, 3, ..."; e.g. "I, II, III, ...", "One, Two, Three, ...", "1st, 2nd, 3rd" or the use of foreign language numbers.
Volume labeling schemes other than "Volume"; e.g. "Series", "Edition", etc.
If the ReleaseTitle ends with a different punctuation mark; e.g. an exclamation mark The Best Sixties Album in the World... Ever! Volume 4 (disc 2) . In that case just use the punctuation mark that is specified by the title.
Combinations of the above; e.g. "Fetenhits: The Real Classics, the 2nd", or The Emo Diaries which use Chapter as volume delimiter, and English words as volume numbers.
and similar cases (still to be added after discussion).
Retrieved from "http://wiki.musicbrainz.org/index.php?title=Style/Titles/Series_numbers&oldid=47164"
Official Style


2016-17 — Played in 23 games averaging over 20 minutes per game … was third on the team with 8.5 points per game and fourth with 4.4 rebounds per game … scored a season-high 22 points at Wesley … was named to the CAC All-Academic Team and CAC Weekly Honor Roll in January.
2015-16 — Played in 16 games this season as a forward … earned a 37.0 percentage from the field …recorded a 60.4 percentage from the free-throw line … named to fall semester Provost's List.
High School — Recieved the Coach's Award at Centennial High School.
Personal — Parents are Kirsten and David Eads ... has five siblings ... is majoring in psychology ... enjoys spending time with family and friends and strawberries.


text:
MMWR. Morbidity and mortality weekly report, Vol. 69, no.17, May 1, 2020
By Centers for Disease Control and Prevention (U.S.)
Series: MMWR. Morbidity and mortality weekly report ; v. 69, no. 17
Title : MMWR. Morbidity and mortality weekly report, Vol. 69, no.17, May 1, 2020 Corporate Authors(s) : Centers for Disease Control and Prevention (U.S.) Published Date : 05/01/2020 Series : MMWR. Morbidity and mortality weekly report ; v. 69, no. 17 URL : https://stacks.cdc.gov/view/cdc/87393
Preliminary Incidence and Trends of Infections with Pathogens Transmitted Commonly Through Food — Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2016–2019 / Danielle M. Tack; Logan Ray; Patricia M. Griffin; Paul R. Cieslak et al. -- Progress Toward Maternal and Neonatal Tetanus Elimination — Worldwide, 2000–2018 / Henry N. Njuguna; Nasir Yusuf; Azhar Abid Raza; Bilal Ahmed; Rania A. Tohme et al. -- Assessment of SARS-CoV-2 Infection Prevalence in Homeless Shelters — Four U.S. Cities, March 27–April 15, 2020 / Emily Mosites; Erin M. Parker; Kristie E. N. Clarke; Jessie M. Gaeta; Travis P. Baggett et al. -- COVID-19 Outbreak Among Three Affiliated Homeless Service Sites — King County, Washington, 2020 /Farrell A. Tobolowsky; Elysia Gonzales; Julie L. Self; Carol Y. Rao et al. -- Notes from the Field: Cholera Outbreak — Zimbabwe, September 2018–March 2019/Alison Winstead; Jonathan Strysko; Pryanka Relan; Erin E. Conners et al. -- QuickStats: Age-Adjusted Suicide Rates by State — National Vital Statistics System, United States, 2018
Journal Issue ;
urn:sha256:dfb78616a8ff56aacfabb0e80dc3dc316c1b42e8f5ca936d537e65cc2abad25d
Chronic disease and health promotion; adapted from the MMWR; Tobacco topics 1990-1999
National Center for Chronic Disease Prevention and Health Promotion (U.S.)
MMWR. Morbidity and mortality weekly report
Home and leisure injuries in the United States : a compendium of articles from the Morbidity and Mortality Weekly Report, 1985-1995
Stevens, Judy A. ; Branche-Dorsey, Christine M. ;
National Center for Injury Prevention and Control (U.S.)
This compendium contains all of the MMWR articles concerning home and leisure injuries published from 1985 through 1995; a summary public health messa...


text:
José Carranza may refer to:

 José Andrés Carranza (fl. 1974–1989), Peruvian football forward
 José Luis Carranza (born 1964), Peruvian football midfielder
 Jose Carranza (soccer, born 1999), American soccer midfielder
 José León de Carranza Bridge, bridge in Cádiz, Spain

redpajama_set_name: RedPajamaWikipedia