text:
\section{Introduction}

\section{Introduction}

Contemporary information communication technologies open up new ways of cooperation leading to the emergence of large-scale crowd-sourced collaborative projects \cite{estelles2012towards}.
Examples of such projects are open software development \cite{dabbish2012social}, {\it citizen science} campaigns \cite{sauermann2015crowd}, and most notably Wikipedia \cite{doan2011crowdsourcing}.
All these projects are based on contributions from volunteers, often anonymous and non-experts. Although the success of most of these examples is beyond expectation, there are challenges and shortcomings to be considered as well.
In the case of Wikipedia for instance, inaccuracies \cite{giles2005internet}, edit wars and destructive interactions between contributors \cite{kittur2007he,yasseri2012dynamics}, and biases in coverage and content \cite{halavais2008analysis,samoilenko2014distorted}, are only a few to name among many undesirable aspects of the project that have been studied in detail.

The affordances of  Internet-mediated crowd-sourced platforms has also led to the emergence of crowd-sourced  online dictionaries.
Language is constantly changing. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new meanings  (i.e. senses)\cite{labovsocial}. Dictionaries record new words and new meanings, are regularly updated, \changed{and sometimes used as a source to study language change \cite{siemund_2014}}. However, a new word or a new meaning needs to have enough evidence backing it up before it can enter a traditional dictionary. For example, \emph{selfie} was the Oxford dictionaries word of the year in 2013 and its frequency in the English language increased by 17,000\% in that year. Its first recorded use dates back to 2002,\footnote{\url{http://blog.oxforddictionaries.com/press-releases/oxforddictionaries-word-of-the-year-2013/}} but was only added to OxfordDictionaries.com in August 2013.
Even though some of the traditional online dictionaries, such as Oxford Dictionaries\footnote{\url{https://www.oxforddictionaries.com}} or Macmillan Dictionary,\footnote{\url{https://www.macmillandictionary.com}} have considered implementing  crowdsourcing in their workflow \cite{abel-meyer_2013} \changed{(see \cite[p. 3-6]{rundell_2016} for a typology of crowdsourcing activities in lexicography)}, for most, they rely on professional lexicographers to select, design, and compile their entries.

Unlike traditional online dictionaries \changed{\cite[p. 11]{rundell_2016}},  the content in crowd-sourced  online dictionaries  comes from non-professional contributors and popular examples are Urban Dictionary\footnote{\url{https://www.urbandictionary.com/}} and Wiktionary \cite{meyer2012wiktionary}.\footnote{\url{https://en.wiktionary.org/}}
Collaborative online dictionaries are constantly updated and have a lower threshold for including new material compared to traditional dictionaries \changed{\cite[p. 2]{rundell_2016}}. Moreover, it has also been suggested that such dictionaries might be driving linguistic change, not only reflecting it \cite{creese_2013, creese_2017}.
\changed{
Crowd-sourced dictionaries could potentially complement online sources such as Twitter, blogs and websites (e.g.,  \cite{10.1371/journal.pone.0113114,grieve_nini_guo_2017,Kerremans2011-KERTNI-2}) to study language innovation.}
However, such dictionaries are subject to spam and vandalism, as well as ``unspecific, incorrect, outdated, oversimplified, or overcomplicated descriptions'' \cite{abel-meyer_2013}.
Another concern affecting such collaborative dictionaries is the question of whether their content reflects real language innovation, as opposed to the concerns of a specific community of users, their opinions, and generally neologisms and new word meanings that will not last in the language.

This paper \changed{presents an explorative study of} Urban Dictionary (UD), an online crowd-sourced dictionary \changed{founded} in December 1999.  Users contribute by submitting an entry describing a word and a word might therefore have multiple entries.
According to Aaron Peckham, its founder, ``\textit{People write really opinionated definitions and incorrect definitions. There are also ones that have poor spelling and poor grammar [$\ldots$] I think reading those makes definitions more entertaining and sometimes more accurate and honest than a heavily researched dictionary definition.}''\cite{tenore_2012}.
 An  UD entry for \emph{selfie} is shown in Figure~\ref{selfie_example}, in which \emph{selfie} is defined as `\textit{The beginning of the end of intelligent civilization}.' and accompanied with an example usage `\textit{Future sociologists use the selfie as an artifact for the end of times}'. Furthermore, entries can contain tags (e.g., \emph{\#picture, \#photograph}).
In total, Urban Dictionary contains 76 entries for \textit{selfie} (July 2016), the earliest submitted in 2009, and a range of variations (e.g., \textit{selfie-conscious, selfied, selfieing} and \textit{selfie-esteem}). Overall, there are 353 entries that describe a word (or phrase) containing the string \emph{selfie} (see Figure~\ref{selfie_time} for a plot over time). Figure~\ref{fleek_time} shows a similar plot for \emph{fleek} and \emph{on fleek}, a phrase that went viral in 2014. UD thus not only captures  new words rapidly, but it also captures the many variations that arise over time.
Furthermore, the personal,  informal, and often offensive nature of the content in this popular site is \changed{different from the content typically found in}  both traditional dictionaries \changed{(see e.\,g. \cite[p. 3-4]{rundell_2016} and \cite[p. 7]{rundell_2016})} and more regulated collaborative dictionaries like Wiktionary. The status of UD as source of evidence for popular and current usage is widely recognized \cite{davis_2011, heaton_2010, Smith2011} and it has even been consulted in some legal cases \cite{ni_2017}. \changed{UD has also been used as a source to cross-check emerging word forms identified through Twitter \cite{grieve_nini_guo_2017}.}

\begin{figure}
\centering
 \includegraphics[scale=0.45]{figures/selfie_screenshot.png}
 \caption{An Urban Dictionary entry for \emph{selfie}.}
 \label{selfie_example}
\end{figure}

\begin{figure}
\captionsetup{justification=centering}
    \centering
    \begin{minipage}{0.45\textwidth}
        \centering
    \includegraphics[width=0.85\textwidth]{figures/selfie}
      \caption{The number of new definitions for \emph{selfie} and its variations per year (Dec 1999 -- July 2016).}
      \label{selfie_time}
    \end{minipage}\hfill
    \begin{minipage}{0.45\textwidth}
        \centering
    \includegraphics[width=0.85\textwidth]{figures/fleek}
     \caption{The number of new definitions for \emph{fleek} and \emph{on fleek} and other variations  per year (Dec 1999 -- July 2016).}
       \label{fleek_time}
     \end{minipage}
\end{figure}

\changed{Urban Dictionary has also been used} for the development of natural language processing systems that have to deal with informal language, non-standard language, and slang.  For example, UD has been consulted when building a text normalization system for Twitter \cite{beckley_2015} and it has been used to create more training data for a Twitter-specific sentiment lexicon \cite{tang_2014}.
In a recent study, Urban Dictionary is used to automatically generate explanations of  non-standard words \changed{and phrases
\cite{ni_2017}.}

While Urban Dictionary seems to be a promising resource to record and analyze language innovation, so far little is known about the characteristics of its content.
In this study we take the first step towards characterizing UD.
\changed{
So far, UD has been featured in a few studies, but these qualitative analyses were based on a
small number of entries \cite{damaso_cotter_2007,Smith2011}.}
We study a complete snapshot \changed{(Dec 1999 -- July 2016)} of all the entries in the dictionary as well as selected samples using \changed{content analysis methods}. To the best of our knowledge, this is the first systematic study of Urban Dictionary at this \changed{scale.}

\section{Results}
\label{sec:results}
We start with presenting an overall picture of Urban Dictionary (Section 2 \ref{sec:overall_picture}), such as its growth and how content is distributed. \changed{Next, we compare its size to Wiktionary based on the number of headwords} (Section 2~\ref{sec:lexical_coverage}).
We then present results based on two crowdsourcing experiments in which we analyze the types of content and the offensiveness of the entries (Section 2~\ref{sec:content_analysis}). Finally, we discuss how characteristics of the entries relate to their popularity on UD (Section 2~\ref{sec:content_popularity}).

\subsection{Overall picture}
\label{sec:overall_picture}
Since its inception in 1999, UD has had a rather steady growth. Figure~\ref{week} shows the number of new entries added each week.
So far, UD has collected 1,620,438 headwords (after lower casing)\footnote{
We use `headword' to refer to the title under which a set of definitions appear.
For example, in Wiktionary, the page about \emph{bank} covers different part of speech (e.g., noun and verb) as well as the different senses.
In the context of UD, we use `entry' to refer to an individual content contribution (e.g., the combination of headword, definition, example text and tags submitted by a user).
Due to the heterogeneity in UD, we lower cased the headwords to calculate this statistic. This follows the interface of UD, which also does not match on case when grouping entries. }
and 2,661,625 entries with an average of 1.643 entries per headword.
However, as depicted in Figure~\ref{def_pdf}~(left), the distribution of the number of entries for each headword varies tremendously from one headword to another. While the majority of headwords have only one definition, there are headwords with more than 1,000 definitions. Table~\ref{top_terms_counts} reports the headwords with the largest number of definitions. \\

 \begin{minipage}{\textwidth}
  \begin{minipage}[b]{0.49\textwidth}
    \centering
     \includegraphics[width=0.9\textwidth]{figures/per_week2.pdf}
      \captionof{figure}{Number of contributed definitions to Urban Dictionary per week since its inception in 1999.}
      \label{week}
  \end{minipage}
  \hfill
  \begin{minipage}[b]{0.49\textwidth}
    \centering
    \begin{tabular}{lr}
\toprule
\textbf{Headword} & \# \textbf{Definitions}\\
\midrule
emo	&1,204\\
love	&1,140\\
god	&706\\
urban dictionary	&701\\
chode	&614\\
canada's history	&583\\
sex	&558\\
school	&555\\
cunt	&541\\
scene	&537\\
\bottomrule
\end{tabular}
\captionof{table}{Headwords with the most definitions.}
\label{top_terms_counts}
    \end{minipage}
  \end{minipage}

This fat-tailed, almost power-law distribution is not limited to the number of definitions per headword; the number of definitions contributed by each user follows a similar distribution, shown in Figure~\ref{def_pdf}~(right).
The majority of users have contributed only once, while there are few power-users with more than 1,000 contributed definitions.
These types of distributions are common in self-organized human systems, particularly similar crowd-based systems such as Wikipedia \cite{ortega2008inequality,yasseri2013value} or the citizen science projects Zooniverse \cite{sauermann2015crowd},  social media activity levels such as on Twitter \cite{huberman2008social}, or content sharing systems such as Reddit or Digg \changed{\cite{wu2007novelty}.}

\begin{figure}
\captionsetup{justification=centering}
    \centering
    \begin{minipage}{\textwidth}
        \centering
    \includegraphics[width=0.35\textwidth]{figures/def_per_word.pdf}
    \includegraphics[width=0.35\textwidth]{figures/def_per_user.pdf}
      \caption{The probability density function of left: the number of definitions contributed to each headword and, right: the number of definitions contributed by each user of Urban Dictionary (logarithmic binning). Both axes are logarithmically scaled.}
       \label{def_pdf}
    \end{minipage}
\end{figure}

A noteworthy feature of UD is that  users can express their evaluation of different definitions for each headword by up or down voting the definition. There is little to no guideline on "what a good definition is" in UD and users are supposed to judge the quality of the definitions based on their own subjective perception of how an urban dictionary should be.
Figure~\ref{vote}~(left) shows the distribution of the number of up/down votes that each definition has received among all the definitions of all the headwords. A similar pattern is evident, in which many definitions have received very few votes (both up and down) and few definitions have many votes.  Figure~\ref{vote}~(middle) shows a scatter plot of the number of down votes versus the number of up votes for each definition. There is a striking correlation between the number of up and down votes for each definition which emphasizes  the role of visibility rather than quality in the number of votes. However, there seems to be a systematic deviation from a perfect correlation in which the number of up votes generally outperforms the number of down votes. This is more evident in Figure~\ref{vote}~(right), where the distribution of the ratio of up votes to down votes is shown. Evidently, there is a wide variation among the definitions with some having more than ten times more up votes than down votes and some the other way around.

\begin{figure}
\captionsetup{justification=centering}
    \centering
    \begin{minipage}{\textwidth}
        \centering
    \includegraphics[width=0.35\textwidth]{figures/votes_count.pdf}
    \includegraphics[width=0.30\textwidth]{figures/up_down_votes_binned.png}
    \includegraphics[width=0.30\textwidth]{figures/votes_ratio_hist.pdf}
      \caption{left: histogram of the number of votes of each definition, middle: scatter plot of the number of up votes and down votes that each definition has received, \changed{with error bars for bins and a fitted line}, right: the histogram of the ratio of up votes (U) to down votes (D) of each definition.}
       \label{vote}
    \end{minipage}
\end{figure}

\subsection{\changed{Number of headwords}}
\label{sec:lexical_coverage}
We now compare \changed{the number of unique headwords in} Urban Dictionary to \changed{the number of unique headwords in Wiktionary,  another crowdsourced dictionary}.
\changed{Wiktionary  manifests a different policy than Urban Dictionary. The content in Wiktionary is created and maintained by
administrators (selected by the community),  registered  users, and anonymous contributors \cite{meyer2012wiktionary}. In contrast to UD, there are many different mechanisms in  Wiktionary to ensure that the content adheres to the community guidelines.
Each page is accompanied by a talk page, where users can discuss the content of the page and resolve any possible conflicts. Furthermore, in Wiktionary guidelines can be found for the structure and content of the entries}.
Capitalization is consistent and  content or headwords that do not meet the Wiktionary guidelines  are removed.  For example, while both UD and Wiktionary have misspelled headwords (e.g., \emph{beleive} for \emph{believe}),
\changed{Wiktionary guidelines state that only common misspellings should be  included while rare misspellings should be excluded\footnote{https://en.wiktionary.org/wiki/Wiktionary:Criteria\_for\_inclusion (17 February, 2018)}. In contrast, such guidelines are not present in UD.}
Wiktionary entries thus undergo a deeper level of curation.

Because of the inconsistent capitalization in UD, we experiment with three approaches to match the headwords between both dictionaries: no preprocessing, lower casing of all characters, and mixed.\footnote{The headword will be lower cased when the headword is all upper case or when the first character is upper case and the second character is lower case.} Table \ref{term_comparison_no_threshold} reports the result of this matching. The \changed{number of unique headwords in} UD is much higher and the lexical overlap is relatively low.
Sometimes there is a match on the lexical level (i.e. the headwords match), but UD or Wiktionary cover different or additional meanings. For example, \emph{phased} is described in UD as 'something being done bit by bit -- in phases', a meaning also covered in Wiktionary. However, UD also describes several other meanings, including 'A word that is used when your asking if someone wants to fight.' and 'to be "buzzed." when you arent drunk, but arent sober.'.

\changed{Because there is little curation of UD content, there are many headwords that would not typically be included in a dictionary.}
 Examples include nick names and proper names (e.g. \emph{shaskank} defined as `Akshay Kaushik's nick name for his boyfriend Shashank.'; \emph{dan taylor}, defined as `A very wonderful man that cooks the best beef stew in the whole wide world. [\ldots]'), \changed{as well as
informal spelling  (e.g., \emph{AYYYYYYYYYYYYYYYYYYY!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!}) and
  made-up words that actually no one uses (e.g. \emph{Emptybottleaphobia}\footnote{A Google search only returns 14 results, all of them containing the Urban Dictionary definition (17 Feb, 2018).}). Based on manual inspection, it seems that these are often headwords with only one entry}.

We therefore also perform a matching considering only headwords  from UD with at least two entries (Table \ref{term_comparison_at_least_two}). \changed{In this way, we use the number of entries as a crude proxy for whether the headword is of interest to a wider group of people.  Note that this filtering is not applied to Wiktionary, because each headword has only one page and headwords that do not match Wiktionary guidelines are already removed by the community. For example, an important criteri\changed{on} for inclusion in Wiktionary is that the term is \changed{reasonably widely} attested, e.g. has widespread use or is used in permanently recorded media\footnote{https://en.wiktionary.org/wiki/Wiktionary:Criteria\_for\_inclusion (17 February, 2018)}}.
Compared to the first analysis, the difference is striking. \changed{In this comparison, the number of unique headwords in Wiktionary is higher than that of UD. }
From a manual inspection we see that many Wiktionary-specific headwords include  domain specific and encyclopaedic words (e.g., \emph{acacetins},  \emph{dramaturge} and \emph{shakespearean sonnets}), archaic words (e.g., \emph{unaffrighted}), as well as some commonly used words (e.g., \emph{deceptive}, \emph{e-voucher}).
We also find that many of the popular UD headwords (i.e., headwords that have many entries) that are not covered in Wiktionary are proper nouns: The top five entries are \emph{canada's history},  \emph{justin bieber}, \emph{george w. bush}, \emph{runescape} and \emph{green day}.
In some cases, entries uniquely appearing in UD refer to words with genuine general coverage, such as \emph{loml} (in total 11 entries) defined as, e.g., `Acronym of "Love of My Life"' or \emph{broham} `a close buddy, compadre, smoking and/drinking buddy. a term of endearment between men to reaffirm heterosexuality.' (in total 18 entries).

\begin{table}
\center
\begin{tabular}{lrrrrrr}
\toprule
& \multicolumn{2}{c}{\textbf{No processing}} & \multicolumn{2}{c}{\textbf{All lowercase}} & \multicolumn{2}{c}{\textbf{Mixed}}\\
\midrule
Overlap & 93,167 &(4\%) & 112,762& (5\%) &  108,361& (5\%)\\
Only UD & 1,698,812 &(72\%) & 1,507,675& (70\%) & 1,565,794 &(70\%)\\
Only Wiktionary & 569,787 &(24\%) & 540,641 &(25\%) & 546,263& (25\%)\\
\textbf{Total} & 2,361,766 && 2,161,078& & 2,220,418\\
\bottomrule
\end{tabular}
\caption{Headword comparison between UD and Wiktionary. The table reports the unique number of headwords in each category. No threshold was applied.}
\label{term_comparison_no_threshold}
\end{table}

\begin{table}
\center
\begin{tabular}{lrrrrrr}
\toprule
& \multicolumn{2}{c}{\textbf{No processing}} & \multicolumn{2}{c}{\textbf{All lowercase}} & \multicolumn{2}{c}{\textbf{Mixed}}\\
\midrule
Overlap &50,522 &(6\%) & 56,730 &(7\%)& 55,003 &(7\%)\\
Only UD &220,661 &(25\%)& 165,054 &(20\%)& 178,164 &(21\%)\\
Only Wiktionary & 612,432 &(69\%)& 596,673 &(73\%)& 599,621 &(72\%)\\
\textbf{Total} & 883,615 && 818,457 &&  832,788\\
\bottomrule
\end{tabular}
\caption{Headword comparison between UD and Wiktionary. The table reports the unique number of headwords in each category. Only UD headwords with at least two entries are included.}
\label{term_comparison_at_least_two}
\end{table}

\subsection{Content analysis}
\label{sec:content_analysis}
In this section we present our analyses on the different types of content as well as the offensiveness of the content in UD.

\subsubsection{Content type}
\changed{We now analyze several aspects of the content in UD that we expect to be different from content typically found in traditional dictionaries as well as Wiktionary. For example, manual inspection suggested that UD has a higher coverage of informal and infrequent words and of proper nouns (e.g., names of places or specific people).
Many of the headwords are not covered in knowledge bases or encyclopedia\changed{s}. To characterize the data, we therefore annotated a sample of the data using crowdsourcing (see Data and methods). In order to limit the dominance of headwords with only one entry (which represent the majority of headwords in UD), the sample was created by taking headwords from each of the 11 frequency bins (see Table~\ref{bins} for details on the way the bins were created and sampled from).
Note that the last two bins  are very small.
For each headword, we include up to three entries (top ranked, second ranked, and random based on up and down votes).
Annotations were collected on the entry level and crowd workers were  shown the headword, definition and example.}

\paragraph*{\changed{Proper nouns}}
\changed{Dictionaries are usually selective with including proper nouns  (e.g., names of places or individuals)} \changed{\cite[p. 77]{marconi_1990}.}
In contrast, in UD many entries describe proper nouns. We therefore asked crowdworkers whether the entry described a proper noun (\textit{yes} or \textit{no}).
\changed{In our stratified  sample}, 16.4\% of the entries were annotated as being about a proper noun. Figure~\ref{proper_nouns_proportion} shows the fraction of proper nouns by frequency bin.

\paragraph*{\changed{Opinions}}
\changed{Most dictionaries strive towards objective content. For example, Wiktionary states `Avoid bias. Entries should be written from a neutral point of view, representing all usages fairly and sympathetically'\footnote{https://en.wiktionary.org/wiki/Wiktionary:Policies\_and\_guidelines  (16 Feb, 2018)}. } In contrast, the entries provided in UD do not always describe the meaning of a word, but they sometimes contain an opinion (e.g., \emph{beer} `Possibly the best thing ever to be invented ever. I MEAN IT.' or \emph{Bush} `A disgrace to America'). We therefore asked the crowdworkers whether the definition describes the meaning of the word,
 expresses a personal opinion, or
 both.
Figures \ref{meaning_opinion_not_proper_nouns} and \ref{meaning_opinion_proper_nouns} show the fraction of entries labeled as \emph{opinion}, \emph{meaning} or \emph{both}, separated according to whether they were annotated as describing proper nouns. In higher frequency bins, the fraction of entries marked as \emph{opinion} is higher. We also find that the number of entries marked as \emph{opinion} is  higher for  proper nouns. While most entries are marked as describing a \emph{meaning}, the considerable presence of opinions \changed{suggests} that the type of content in UD is different than in traditional dictionaries \changed{\cite[p. 3-4]{rundell_2016}}.

\paragraph*{\changed{Familiarity}}
\changed{UD enables quick recording of new words and new meanings, many of them which may not have seen a widespread usage yet.  Furthermore, as discussed in the previous section, some entries are about made-up words or words that only concern a small community.
In contrast, many dictionaries require that included headwords should be attested (i.e. have widespread use).
These observations suggest that many definitions in UD may not be familiar to  people.
To quantify this, we asked crowdworkers whether they were familiar with the meaning of the word.}
The majority of the entries in UD were not familiar to the crowdworkers. Examples are common headwords with an uncommon meaning such as \emph{coffee} defined as `a person who is coughed upon' or \emph{shipwreck} 'The opposite of shipmate.
A crew member who is an all round liability and as competent as a one legged man in an arse kicking competition.`,  as well as uncommon headwords and uncommon meanings (e.g., \emph{Once-A-Meeting} defined as `An annoying gathering of people for an hour or more once every pre-defined interval of time (ex: once a day).
Once-A-Meetings could easily be circumvented by a simple phone call or e-mail but are instead used to validate a project managers position within the company.').
Figure \ref{familiarity_plot} shows that  in higher frequency bins, more definitions are marked as being \emph{familiar}, suggesting that the number of definitions per headword is indeed related to the general usage of a headword.

\paragraph*{\changed{Formality}}
\changed{The focus of Urban dictionary on slang words \cite{peckham2009urban} means that many of the words are usually not appropriate in formal conversations, like a formal job interview. To quantify this, we asked crowdworkers whether the word in the described meaning can be used in a formal conversation.
As Figure \ref{formality_plot} shows, most of the words in their described meanings were indeed not appropriate for use in formal settings}.

\begin{figure}
\captionsetup{justification=centering}
    \centering
      \begin{minipage}[t]{0.32\textwidth}
        \centering
 \includegraphics[width=0.92\textwidth]{figures/prop_proper_nouns}
      \caption{Proper nouns}
      \label{proper_nouns_proportion}
    \end{minipage}
    \begin{minipage}[t]{0.32\textwidth}
        \centering
    \includegraphics[width=0.92\textwidth]{figures/opinion_graph}
      \caption{Meaning vs. opinions\\ (proper nouns were excluded)}
      \label{meaning_opinion_not_proper_nouns}
    \end{minipage}\hfill
    \begin{minipage}[t]{0.32\textwidth}
        \centering
    \includegraphics[width=0.92\textwidth]{figures/opinion_graph_proper_noun}
      \caption{Meaning vs. opinions\\ (proper nouns entries only)}
       \label{meaning_opinion_proper_nouns}
     \end{minipage}\hfill

\end{figure}

\begin{figure}
\captionsetup{justification=centering}
    \centering
    \begin{minipage}{0.45\textwidth}
        \centering
    \includegraphics[width=0.8\textwidth]{figures/familiar_graph_meaning}
      \caption{Familiarity\\ \changed{(proper nouns and opinion entries were excluded)}}
      \label{familiarity_plot}
    \end{minipage}\hfill
    \begin{minipage}{0.45\textwidth}
        \centering
    \includegraphics[width=0.8\textwidth]{figures/formality_graph_meaning}
      \caption{Formality\\ \changed{(proper nouns and opinion entries were excluded)}}
       \label{formality_plot}
    \end{minipage}\hfill

\end{figure}

\subsubsection{Offensiveness}

Online platforms with user generated content are often susceptible to offensive content, which may be insulting, profane and/or harmful towards individuals as well as social groups \cite{Sood2012,waseem2017}. Furthermore,
the existence of such content in platforms could signal to other users that such content is acceptable and impact  the social norms of the platform \cite{Sukumaran2011}.
As a response, various online platforms have integrated different mechanisms to detect, report and remove inappropriate content.
In contrast, regulation is minimal in UD and  one of \changed{its  characteristics}   is its often offensive content.

UD not only contains  offensive entries describing the meaning of offensive words, but there are also  offensive entries for non-offensive words (e.g., a definition describing \emph{women} as 'The root of all evil'). We note, however, that UD also contains  non-offensive definitions for  offensive words (e.g., \emph{asshole} defined as 'A person with no concept of boundaries, respect or common decency.'). To investigate how offensive content is distributed in UD, we ran a crowdsourcing task on CrowdFlower (see Data and methods for more details).  Workers were shown three definitions for the same headword, which they had to rank from the most to the least offensive.

\changed{We only included headwords with at least three definitions. In total, we obtained annotations for 1,322 headwords and thus 3,966 definitions. Out of these 1,322 headwords there are 326 headwords for which the majority of the workers agreed that \textit{none} of the definitions were offensive.}

Table~\ref{offensive_scores_meaning_opinion} reports the offensiveness  scores separated by whether the definitions describe a meaning, opinion or both.  An one-way ANOVA test indicates a slight significant difference (F(2, 3963) = 2.766, $p$<0.1).  A post hoc comparison using the Tukey test indeed indicates a slight significant difference between the scores of definitions describing a meaning and opinion ($p$<0.1). Thus, definitions stating an opinion tend to be ranked as more offensive compared to definitions describing a meaning.

Table~\ref{offensive_scores_formality} reports the offensiveness scores by formality. Definitions for words that were annotated as not being appropriate  for formal settings (based on their described meaning)   tend to  be ranked as being more offensive. An one-way ANOVA  confirms that the differences between the groups are highly significant
(F(2, 3963) = 22.72, $p$<0.001).
Post hoc comparisons using the Tukey test
indicate significant differences between the formal and not formal categories ($p$<0.001), and between the unclear and not formal categories ($p$<0.05).
We also find that definitions for which crowdworkers had indicated that they were familiar with the described meaning of the word tended to be perceived as less offensive (Table~\ref{offensive_scores_familiarity},  $p$ < 0.001 based on a t-test).
We observe the same trends when we only consider definitions that describe a meaning.
\\

 \begin{minipage}{\textwidth}
\begin{minipage}[b]{0.40\textwidth}
    \centering
   \begin{tabular}{lll}
\toprule
 \textbf{Type}&  \textbf{Avg. offensiveness}\\
 \midrule
 both     &   2.025\\
 meaning    &    1.989\\
opinion   &     2.050\\
\bottomrule
\end{tabular}
\captionof{table}{Average offensiveness rankings  (3=most offensive, 1=least offensive) by type of definition in UD entries.}
\label{offensive_scores_meaning_opinion}
\end{minipage}
\hspace{0.1\textwidth}
\begin{minipage}[b]{0.40\textwidth}
    \centering
  \begin{tabular}{lll}
\toprule
  \textbf{Formal?}&  \textbf{Avg. offensiveness}\\
 \midrule
 no     &   2.031\\
 unclear    &    1.884\\
yes   &     1.873\\
\bottomrule
\end{tabular}
\captionof{table}{Average offensiveness rankings  (3=most offensive, 1=least offensive) by formality in UD definitions.}
\label{offensive_scores_formality}
    \end{minipage}
    \end{minipage}

  \begin{table}
    \centering
    \begin{tabular}{lll}
\toprule
  \textbf{Familiar?}&  \textbf{Avg. offensiveness}\\
 \midrule
   yes   &    1.915\\
 no  &     2.022 \\
\bottomrule
\end{tabular}
\caption{Average offensiveness rankings  (3=most offensive, 1=least offensive) by familiarity in UD entries.}
\label{offensive_scores_familiarity}
 \end{table}

\subsection{Content and popularity}
\label{sec:content_popularity}
An important feature of UD is the voting mechanism that allows the users to express their evaluation of  entries by up or down voting \changed{them}. For a given headword, entries are ranked according to these votes and the top ranked one is labeled as \emph{top definition}. The votes thus drive the online visibility of entries, leading to the following implications. \changed{First, the top ranked entries are immediately visible when UD is consulted to look up the meaning of a headword. Many users might not browse the additional pages with   lower ranked entries.} Second, by users expressing their evaluation through votes, social norms are formed regarding what content is valued in UD.

UD does not provide clear guidelines on "what a good definition is".  Various factors could  influence the up and down votes an entry receives, including whether the voter thinks the entry is offensive, informative, funny and whether the voter (dis)agrees with the expressed view.
In this section we analyze how characteristics of the content as discussed in the previous section relate to the votes the entries receive. Because the number of up and down votes varies highly depending on the popularity of the headword, we perform the analysis based on the rankings of entries (top ranked, second ranked, and random) instead of the absolute number of up and down votes. Only headwords with at least three entries are included.

Table~\ref{characterization_votes_meaning_opinion} shows the distribution of opinion-based versus meaning-based definitions  separated by whether the headwords are annotated as proper nouns by the crowdworkers. The proportion of definitions that are annotated as opinions is much higher for proper nouns, which is consistent with our previous analysis. However, among the top ranked definitions for proper nouns, the proportion of opinions is lower (but n.s.).

Table~\ref{characterization_votes_familar_formal_offensiveness} characterizes the entries by formality and familiarity. We discard proper nouns and entries marked as opinion, since it is less clear what formality and familiarity mean in these contexts. We find that the top ranked definitions tend to be more familiar (${\chi}^2$ (2, N = 2991) = 15.385, p $<$0.001) and more appropriate for formal settings (but n.s.).

\begin{table}[h!]
\center
\begin{tabular}{llllll}
\toprule
& \multicolumn{3}{c}{\textbf{Opinion or meaning?}}\\
&\textbf{both}&\textbf{meaning}&\textbf{opinion}\\
\midrule
\multicolumn{4}{c}{\textit{No proper nouns (n=3,268)}}\\
top ranked & 0.055&0.852&0.094\\
second ranked & 0.074&0.850&0.076\\
random & 0.051& 0.864&0.084\\
\multicolumn{4}{c}{\textit{Proper nouns (n=698)}}\\
top ranked & 0.172&0.481&0.347\\
second ranked & 0.169&0.477&0.354\\
random & 0.190&0.444&0.366\\
\bottomrule
\end{tabular}
\caption{Characterization of UD entries based on votes. The table reports the proportions of opinion-based versus meaning-based definitions in each of the ranking groups.}
\label{characterization_votes_meaning_opinion}
\end{table}

\begin{table}[h!]
\center
\begin{tabular}{l|ll|lll|l}
\toprule
&\multicolumn{2}{c|}{\textbf{Familiar?}} &\multicolumn{3}{c|}{\textbf{Formal?}} & \textbf{Offensiveness} \\
&\textbf{no}&\textbf{yes}&\textbf{no}&\textbf{unclear}&\textbf{yes} & \textbf{avg. ranking}\\
\midrule
top ranked &  0.799&0.201     & 0.855&0.026& 0.119 & 1.950\\
second ranked & 0.807&0.193  &0.876&0.023& 0.101 & 1.966\\
random & 0.861&0.139            &0.894  &0.020&0.086 & 2.107\\

\bottomrule
\end{tabular}
\caption{Familiarity, formality and offensiveness of UD definitions across rankings based on votes. Definitions for proper nouns and definitions annotated as opinions are not included. The table reports the proportions in each of the rankings for familiarity and formality and the average ranking for offensiveness (3=most offensive, 1=least offensive); n=2,991.}
\label{characterization_votes_familar_formal_offensiveness}
\end{table}

Table~\ref{characterization_votes_familar_formal_offensiveness} also reports the average offensiveness ranking of the definitions separated by their popularity (again, discarding proper nouns and entries marked as opinions).
The difference in rankings between  top ranked and second ranked definitions is minimal, but random definitions are  more often ranked as being more offensive.
A one-way ANOVA test  confirms that the differences between the groups are highly significant
(F(2, 2988) = 22.07, $p$<0.001).
Post hoc comparisons using the Tukey test
indicate significant differences between the random and top ranked, and random and second ranked definitions ($p$<0.001).
A similar trend is observed when we consider all definitions
(F(2, 3963) = 34.87, $p$<0.001).
Thus, although  \changed{UD contains  offensive content}, very offensive definitions do tend to be ranked lower through the voting system. However, the small difference in scores between the groups indicates that offensiveness only plays a small role in the up and down votes a definition receives.

To analyze the different factors jointly, we fit an
ordinal regression model  (Table~\ref{orm_ranking}) using the \emph{ordinal} R library based on definitions that were annotated as not being an opinion and not describing proper nouns.
We find that familiarity and offensiveness indeed have a significant effect. More familiar and less offensive definitions tend to have a higher ranking.
Similar trends in coefficients were observed with fitting logistic regression models when dichotomizing the ranking variable.

\begin{table}[!htbp] \centering
\small
  \label{}
\begin{tabular}{@{\extracolsep{5pt}}lD{.}{.}{-3} }
\toprule
 \multicolumn{2}{r}{\textbf{Dependent variable: ranking}} \\
\midrule
Familiar (yes) &  -0.255^{***}$ $(0.096) \\
Formal (unclear) &  -0.133$\hspace{0.165in} $(0.226)\\
Formal (yes) &-0.073$\hspace{0.165in} $(0.123)\\
Offensiveness & 0.335^{***}$ $(0.059) \\
\midrule
Observations & \multicolumn{1}{c}{2,991} \\
Log likelihood & -3262.19\\
AIC & 6536.38\\
\bottomrule\end{tabular}
  \caption{Ordinal regression results. The dependent variable is the ranking:
  top ranked (0), second ranked (1) or a random rank (2).
  $^{***}$p$<$0.01.}

\label{orm_ranking}
\end{table}

\section{Discussion and conclusion}
In this article, we have studied a complete snapshot (1999--2016) of Urban Dictionary to shed light on the characteristics of its content.
\changed{We} found that most contributors of UD only added one entry and very few added a high number of entries.
Moreover, we found a number of skewed distributions, which need to be taken into account whenever performing analyses on the UD data.
Very few headwords have a high number of entries, while the majority have only one entry. Similarly, few entries are highly popular (i.e. they collected a high number of votes). We also found a strong correlation between the number of up and down votes for each entry, illustrating the importance of visibility on the votes an entry receives.

The lexical content of UD is radically different from that of Wiktionary, another crowdsourced, but more highly moderated dictionary. In general, we can say that the overlap between the two dictionaries is  small.
Considering all unique UD  headwords that are not found in Wiktionary, we found that this number is almost three times  the number of  headwords that uniquely occur in Wiktionary. However, if we exclude words with only one definition in UD (which tend to
be infrequent or idiosyncratic words), we found the opposite pattern, with Wiktionary-only headwords amounting to almost three times the UD-only headwords.

Our analyses based on crowdsourced annotations showed more details on the specific characteristics of UD content.
In particular, we measured a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries. In addition, many entries in UD describe proper nouns.
The crowdworkers were not familiar with most of the definitions presented to them and many words (and their described meaning) were found  not  to be appropriate for formal settings.

\changed{Urban Dictionary captures} many infrequent, informal words and it also contains \changed{ offensive} content, but highly offensive definitions  \changed{tend to get} ranked lower through the voting system.  The high content  heterogeneity in UD could mean that, depending on the goal,  \changed{considerable} effort is needed to filter and process the data (e.g., the removal of opinions) compared to when traditional dictionaries are used.
We also found that words with more definitions tended to be more familiar to crowdworkers, suggesting that UD content does reflect broader trends in language use to some extent.

There are several directions of future work that we aim to explore.
\changed{We have compared the lexical overlap with Wiktionary in terms of headwords.} As future work, we plan to extend the current study  by performing a deeper semantic analysis and by  comparing UD with other non-crowdsourced dictionaries.
Furthermore, we plan to extend the current study by comparing the content in Urban Dictionary with language use in social media to advance our understanding of the extent to which UD reflects broader trends in language use.

\section{Data and methods}
\label{sec:data}

\subsection{Data collection}
\subsubsection{Urban Dictionary}
We crawled UD in July 2016.
First, the definitions were collected by crawling the `browse' pages of UD and by following the `next' links. After collecting the list of words,  the definitions themselves were crawled
directly after (between July 23 and July 29, 2016). We did not make use of the API, since the API restricted the maximum number of definitions returned to ten for each word.

\subsubsection{Wiktionary}
We downloaded the Wiktionary dump of the English language edition  of 20 July, 2016, so that the date matched our crawling process. To parse Wiktionary, we made use of code available through ConceptNet 5.2.2 \cite{speer2012representing}. Pages in the English Wiktionary edition can also include sections describing other languages (e.g., the page about \emph{boot} contains an entry describing the meaning of \emph{boot} in the Dutch language (`boat')). We only considered  the English sections in this study.

\subsection{Crowdsourcing}
Most headwords in UD have only one entry, and therefore these headwords would dominate a random sample. Because such headwords tend to be uncommon, a random sample would not be able to give us much insight into the overall content of UD.
We therefore sampled the headwords according to the number of their entries. For each headword (after lower casing), we counted the number of entries and place the headword in a frequency bin (after taking a log base 2 transformation).
For each bin, we randomly sampled up to 200 headwords. For each  sampled headword,
we included the top two highest scoring entries (scored according to the number of thumbs up minus the number of thumbs down)  and another random entry. In total we sampled 4,465 entries (Table \ref{bins}).

\begin{table}
\begin{tabular}{rlllllllllll}
\toprule
\textbf{Frequency bin (log 2)}&  0 &  1  & 2  & 3  & 4 &  5  & 6 & 7 &  8 &  9&  10 \\
\textbf{\#definitions} & 200 &449& 600& 600 &600& 600 &600& 600 &180 & 30 &  6 \\
\bottomrule
\end{tabular}
\caption{Statistics of the sampled definitions}
\label{bins}
\end{table}

We collected the annotations using CrowdFlower. The quality was ensured using test questions and by restricting the contributors to quality levels two and three and the countries Australia, Canada, Ireland, New Zealand, UK, and the USA. We marked the crowdsourcing tasks as containing explicit content, so that the tasks were only sent to contributors that accepted to work with such content.

\subsubsection{Content Type}
 For each task, we collected three judgements. The workers were paid \$0.03 per judgement.
We collected 13,395 judgements from a total of 201 workers. The median number of judgements per worker is 76. Workers were shown the headword, definition and example.
The crowdworkers were asked the following questions (options for answers are displayed in italic font):
\begin{itemize}
\item  \textbf{Q1}: Is this word a proper noun, for example, a name used for an individual person (like Mark), place (like Paris), or organization (like Starbucks, Apple)? \textit{yes}, \textit{no}
\item  \textbf{Q2}: The definition:
 \textit{describes the meaning of the word,
 expresses a personal opinion,
 both}
 \item \textbf{Q3}: Were you familiar with this meaning of the word before reading this definition?
If you are familiar with this word but NOT with this meaning, then please select no. Example: If you are familiar with the meaning of the word `cat' as the animal, but the definition describes cat as `A person, usually male and generally considered or thought to be cool.' and you are not familiar with this meaning, select no: \textit{yes,
 no}
 \item \textbf{Q4}:
Can this word in the described meaning be used in a formal conversation?
Examples of formal settings are a formal job interview, meeting an important person, or court of law. Examples of informal settings are chatting with close friends or family:
\textit{ yes,
 no,
 unclear}
\end{itemize}

\paragraph*{Agreement}
For each definition we have three judgements. We calculate Fleiss' kappa (using the \emph{irr} package in R) and the pairwise agreement (Table~\ref{agreement_annotation}). The agreement for the first question, asking whether the word is a proper noun, is the highest.
In general the agreement is low, due to the difficulty of the task. For example, in these cases all three workers answered differently to the question whether the definition described a meaning or an opinion: \emph{AR-15} defined as`{AR does NOT stand for Assault Rifle}' and \emph{Law School} defined as `Where you go for to school for four years after college to learn to become a lawyer. In these four years, you will work your butt off every day, slog through endless amounts of reading, suffer through so much writing, and after you graduate, you don't get to call yourself "doctor".'.
We merge the answers for each question by taking the majority vote. We use `\emph{both}' for Q2 and `\emph{unclear}' for Q4 if there was no majority.

\begin{table}
\center
\begin{tabular}{llll}
\toprule
& \textbf{Fleiss' kappa} & \textbf{Pairwise agreement}\\
\midrule
Q1: Proper noun (yes, no)& 0.379& 0.806\\
Q2: Meaning or opinion? (meaning, opinion, both)& 0.207  & 0.691\\
Q3: Familiar (yes, no) &0.206  & 0.713\\
Q4: Formal (yes, no, unclear)& 0.207 &0.712\\
\bottomrule
\end{tabular}
\caption{Agreement statistics}
\label{agreement_annotation}
\end{table}

\subsubsection{Offensiveness}

We experimented with different \changed{pilot} setups in which we asked workers to annotate the level and type of offensiveness for individual definitions. However, we found that this led to confusion and disagreement among the crowdworkers. For example, an offensive word can be described in  a non-offensive way and a non-offensive word can be described in an offensive way.
\changed{Furthermore, people have different thresholds of what they consider to be offensive, making it challenging to ask for a binary judgement}.
In the final setup, we therefore showed the  sampled definitions for the \textit{same} word and asked workers to rank the definitions according to their offensiveness, with 1 being the most offensive and 3 being the least offensive.
\changed{Even if workers have different thresholds of what they consider offensive, they could still agree when being asked
to rank  the definitions}. Indeed, we found that this led to a higher agreement. Note that in this article, we have reversed the ratings (3=most offensive, 1=least offensive) for a more intuitive presentation of the results.
Workers were also asked to indicate whether they considered all definitions equally offensive, equally non-offensive, or none.
For each task, we collected five judgements. We paid \$0.04 per judgement.
We collected 6,610 judgements from a total of 158 workers (median number of judgements per worker: 44).
Table \ref{offensive_ranking_examples} provides examples for two words (\emph{goosed} and \emph{dad}) and their ratings.
\begin{table}
\begin{tabular}{lll}
\toprule
\textbf{Word} & \textbf{Definition} & \textbf{Ratings}\\
\midrule
 & \specialcell{\emph{Def. 1} Old school definition: to pinch someone's buttocks, hopefully the\\ opposite sex, but hey, you take what you get. Always associated in my\\ mind with a British accent....}	 & 2, 2, 2, 2, 2 \\
goosed & 	\specialcell{\emph{Def. 2} adj. 1. a feeling of overwhelmedness 2. a feeling of frusteration \\3. a feeling of joy 4. all emotions easily substituted by the word \\5. the new ``owned"} & 1, 1, 1, 1, 1\\
& \specialcell{\emph{Def. 3} To apply pressure on one's taint (or space between genitalia and \\anus), preferably of the opposite sex!	} & 3, 3, 3, 3, 3\\
\midrule
& \emph{Def. 1} the one who knocked-up your mom & 2, 2, 2, 2, 3\\
dad & \specialcell{\emph{Def. 2} The parent that takes the most shit. Sure, if you had a shitty father,\\ then go ahead and bitch, but not all of us did. Some of us had great fathers,\\ who really loved us, and weren't assholes. Honestly, if you could see\\ how much damage a mother could do to one's self esteem, you wouldn't \\even place so much blame on ``dear old dad"} & 3, 3, 3, 3, 2\\
& \specialcell{\emph{Def. 3} The replacement name for "bro" to call your best friend of whom\\ you have a fatherly bond} & 1, 1, 1, 1, 1\\
\bottomrule
\end{tabular}
\caption{Examples of annotated definitions for offensiveness (3=most offensive, 1=least offensive).}
\label{offensive_ranking_examples}
\end{table}

\paragraph*{Agreement} We calculate agreement using  Kendall's W (also called Kendall's coefficient of concordance), which ranges from 0 (no agreement) to 1 (complete agreement). We calculate Kendall's W for each word separately.
The average value of Kendall's W is 0.511 (standard deviation = 0.303). If we exclude words for which a worker indicated that the definitions were equal in terms of offensiveness, the value increases to 0.714 (standard deviation = 0.238).

\section*{Ethics statement}
In this study we employ crowdsourcing to collect annotations.
The tasks were marked as containing  explicit content, so that the tasks were only visible to contributors that accepted to work with such content. The tasks also explicitly mentioned that the results will be used for scientific research ('\textit{By participating you agree that these results will be used for scientific research}.').
We closely monitored the crowdsourcing tasks and contributor satisfaction was consistently high.

\section*{Data accessibility}
The datasets supporting this article are available upon request.

\section*{Competing interests}
The authors declare no competing interests.

\section*{Authors' contributions}
DN collected and analyzed the data, participated in the design of the study, and drafted the manuscript; BM participated in the design of the study, analysed the data and drafted the manuscript; TY conceived the study, analyzed the data, and helped draft the manuscript. All authors gave final approval for publication.

\section*{Funding}
This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1.
DN was supported by Turing award TU/A/000006 and BM by Turing award TU/A/000010 (RG88751). The crowdsourcing data collection was supported with an Alan Turing Institute seed funding grant (SF024).

\sloppy
\emergencystretch 1.5em
\bibliographystyle{vancouver}

redpajama_set_name: RedPajamaArXiv


text:
Každý den odvahu () je češkoslovaški črno-beli dramski film iz leta 1965, ki ga je režiral Evald Schorm po scenariju Antonína Máše. V glavnih vlogah nastopata Jana Brejchová in Jan Kačer. Zgodba prikazuje Jardo (Kačer), ki je strasten delavec in dejaven pripadnik komunistične partije. Toda sčasoma vsi okoli njega z njim ne delijo več političnega prepričanja, zaradi česar spozna, da je bila vsa njegova dejavnost zaman.

Film je bil premierno predvajan 15. januarja 1965 v češkoslovaških kinematografih. Na Mednarodnem filmskem festivalu Locarno je osvojil glavno nagrado zlati leopard za najboljši film, na Filmskem festivalu v Plzňu pa je bil nagrajen z zlatim vodomcem.

Vloge
 Jana Brejchová kot Věra
 Jan Kačer kot Jarda Lukáš
 Josef Abrhám kot Bořek
 Vlastimil Brodský kot novinar
 Jiřina Jirásková kot Olina, novinarjeva žena
 Olga Scheinpflugová kot stanodajalka
 Václav Trégl kot Eduard Mrázek
 Jan Libíček kot fotograf
 Jan Cmíral kot direktor
 Josef Krameš kot čarodej
 Helena Uhlířová kot čarodejeva pomočnica

Sklici

Zunanje povezave

Češki filmi
Dramski filmi
Črno-beli filmi
Filmi o komunizmu

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text:
Spatial geopolitics | Humanity
Let's talk rockets and satellites. 2022, with 186 rocket launches, 180 of which were successful, was a record for launches since the start of the space age. One shot every six days on average! 78 shots were fired from the United States, closely followed by China at 64 shots. A boom caused the establishment of constellations of satellites, including those of Starlink, already 3,300 launched and operational, of the company SpaceX, using its Falcon rocket. In front of these giants, the Russian star weakens, with 21 shots. Europe collapses, with 6 launches, and the Russian Soyuz rocket banished from Kourou by the invasion of Ukraine. The destruction at its first shot of the little Vega, on December 21, put an end to a dark year. The rest of the world ? 17 shots, including 6 for New Zealand from where a small American rocket was launched.
This gigantic imbalance in the means of accessing space, near and far, is found in the vehicles to be launched. US dominance over the number of deployed satellites is becoming overwhelming. Certainly, in 2022, 47 countries have deployed satellites that they operate, most for communications and Earth observation. Admittedly, Japan and India are still space powers. Admittedly, the European Union (EU) is also embarking on the constellations, operates the Galileo geolocation system. But the geopolitics of space tells us that the EU's claims to strategic parity with the United States will be an illusion as long as it does not make the necessary investments. Investments that China pursues with method and regularity. As for the military aspect of space activities, it is distinguished… by an even clearer domination of the United States.
This explosion in the number of small satellites in low orbit, for the constellations intended for communications and in particular for the Internet, does not only hinder astronomers. In two years, SpaceX had to slightly modify the orbit of its Starlink satellites more than 26,000 times to ensure that it avoided any collision with debris. Spatial geopolitics therefore forces us to consider two questions. How to provide the European Union and its member countries with the means to challenge US domination? And how to regulate near space in order to avoid series collisions which could turn it into a death trap for satellites? Two subjects more important than the negotiation of a jump seat on future NASA astronaut flights.
This drone is excellent, its price is just as good for the Amazon sales
Microsoft will invest billions in the company behind ChatGPT
E3 2023: Sony, Microsoft and Nintendo would skip the event

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No proper clip yet so this snippet from their podcast will have to do.
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Still there Kiki?! This recent dry spell isn't very good for my ears!


text:
James Westbrook – Biography, Age, Height, Wife And Family Facts
Mostly known for being the better half of internet sensation , James Westbrook is equally a social media personality. He has appeared on most of his wife's online videos to much acclaim but his popularity still struggles for attention in the presence of what Tati has accomplished in the online community.
True, he is married to someone who has attained more fame than he may ever dream of but there are several other facts about the life and career of James Westbrook that will keep you fascinated as you keep reading.
1 James Westbrook – Biography, Age
2 Wife And Family Facts
3 Facts About Tati Westbrook
4 James Westbrook's Height
James Westbrook – Biography, Age
The California native was born on 10th September in 1970. Almost nothing has been discovered about the early days and family of the online personality. The names of his parents and other members of his family have remained a mystery. Likewise, the educational institutions he attended, if any, have not been confirmed. Thus, it is not known what he studied in school or what inspired his venture into the online community.
His career achievements have not made it out in details as Tati's popularity seems to always steal the thunder, as well as the spotlight. James has collaborated with her on several occasions and together, they always keep their fans coming back for some more.
Wife And Family Facts
James Westbrook and his charming wife Tati have been together for a long time. They met online which is no surprise as that's one community they have a common ground in. Their relationship blossomed for a while before James popped the question in 2012 and Tati gave a positive answer. Before their wedding, the lovebirds considered eloping to somewhere they can get married privately. Nevertheless, they decided to do it the traditional way and in January of 2017, they walked down the aisle and exchanged marital vows in a star-studded wedding ceremony. Online personalities like and Nikki Phillippi graced the occasion and witnessed the union among many other guests. In memory of their big day, Tati shared a vlog titled "OUR WEDDING" on her online account.
The couple and their son named Taylor are currently enjoying a happy home in Los Angeles, California. Tati and James Westbrook share insane online chemistry which they have replicated in real life.
Facts About Tati Westbrook
A renowned makeup artist, vlogger, and online personality, Tati started her career as a celebrity makeup artist but after a while, she saw the potentials in the social media community and decided to tap into it. She launched her self-titled YouTube channel on Nov 7, 2010, and it has accrued enough fans to earn her the popularity she currently enjoys. Tati's YouTube channel boasts more than five million subscribers and has accumulated well over a billion views as at the time this piece was last updated.
She posts beauty tips, as well as shares reviews of beauty products on her online channel through which she has attracted more fans. Admittedly, she is good at what she does and always charms her audiences with her charisma and passion for the job.
Before fame, Tati lived a modest life with her middle-class family in Washington City, in the United States where she was born in 1982, on Valentine's Day. She did a bit of acting as a child before finding her flare in makeup and beauty. According to the beauty enthusiast, she is completely addicted to everything makeup and this passion is obviously the drive behind her career success.
As a result of her consistency, Tati Westbrook has won a few awards including an Allure Beauty Blogger of the year. She has also been recognized as the breakout beauty star of the year. Additionally, her channel is listed as one of the most popular on YouTube and when it comes to reviewing beauty products, she takes much credit than some of her counterparts.
Asides YouTube, Tati also has a presence on other major social media platforms including Facebook (Glam Life Guru, @GlamLifeGuru), Twitter (@GlamLifeGuru), and Instagram (glamlifeguru).
James Westbrook's Height
Quite an endowed man, James Westbrook stands at an impressive height of 6ft 2in and weighs a complementary 85kg. His general measurements for chest, waist, and hips are 42-32-37 and he wears a shoe size of 12 (US).
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Home » The South African Film Summit, 4 - 5 Feb
The South African Film Summit, 4 - 5 Feb
Government through the National Department of Arts and Culture will host the South African Film Summit from 04 - 05 February 2019 at Skyrink Studios, in Johannesburg Gauteng. The Summit will be held under the theme: "Transformation and innovation in the South African Film/Audio-Visual Industry and the 4th Industrial Revolution. Are we geared for change?"
Industry experts and policy makers within the audio-visual industry will deliberate on key resolutions as guiding efforts towards the development of the local film industry in alignment with emerging trends and global developments.
About the SA Flim Summit
The Summit takes place against the background of a policy shift introduced by the Department of Arts and Culture's (DAC's) Revised White Paper on Arts, Culture and Heritage of 2017. The Revised White Paper replaces the 1996 White Paper on Arts, Culture and Heritage, which has guided and shaped the South African film industry to date.
The Summit also takes places against the backdrop of South Africa's geopolitical position within the African continent, the developments within the global cultural and creative industry (CCI) as informed by changing taste, audience and consumption patterns, as well as the advent of the digital revolution (also called the 3rd revolution) and the Fourth Industrial Revolution. The South African film industry refers to the broader audiovisual media industry, which includes film, television and digital media as defined in the Revised White Paper. In this document the terms 'film' and 'audiovisual' are used interchangeably.
The Summit also takes place at a time when South Africa is no longer Africa's largest audiovisual media market, having been overtaken by Nigeria. South Africa's position in the global cultural and creative economy remains insignificant despite having one of the oldest film industries in the world, whereas Nigeria continues to compete globally with Hollywood in the USA and Bollywood in India. Addressing South Africa's positioning in the film sector, not only on the continent but also globally, is important if the country is to compete in the creative economy.
The Summit is aimed at being a game changer for the South African film industry – an industry that must become inclusive, sustainable, and distinctly South African.
The South African Film Summit seeks to:
Assess the extent to which the current or emerging legislation and policies either enhance or hinder the transformation and development of the film and television industry in South Africa.
Evaluate the extent to which the South African Film Industry is catching up or aligning itself with emerging trends and global developments, premised by Pan-Africanism.
Create a platform for knowledge sharing through case studies and benchmarking with similar countries in the developing world.
Evaluating the successes and challenges of national and regional film industries with particular reference to funding and resources of the sector.
The goals of the Film Summit is to:
Advance a proposal to position a distinct identity of a South African storyline,
Galvanise a common consesus about the potential of the SA film industry to be an intsrument for nation building, and a catalyst for economic growth, and
Produce key recommendations towards a 5-year Implementation Plan.
The South African Film Industry refers to the broader audio-visual media industry which includes film, television and digital media as defined in the Revised White Paper, 2017. The film industry is one of the oldest in the world having initiated in 1896. Despite such a long history, the South African film industry's place within the local economy and globally is a contested one. This is in terms of its contribution in both social and economic value.
As such, whether the industry is in its infancy or not remains a contentious matter. This is considering the low film production volumes, unsustainable business models and a largely freelance workforce because South Africa is not short on policies and strategies to support the industry.
"Policy coordination and coherence is important to ensure there are no unnecessary bottlenecks, contradictions and gaps that will negatively impact on the business environment while simultaneously encouraging investment, particularly from the private sector", states Minister Nathi Mthethwa.
About 300 industry players, experts and policy makers from key organisations, Government and industry institutions, some of which have been part of the planning, such as: National Film and Video Foundation (NFVF), South African Arts & Culture Youth Forum (SAACYF), South African Screen Federation (SASFED); Independent Black Filmmakers Collective (IBFC), South African Guild of Actors (SAGE), the South African Broadcasting Corphttp://www.dac.gov.za/sites/default/files/Executive%20Report%20of%20SA%20Film%20Summit%2012.4.2019.pdforation (SABC), as well as other captains of the South African Film & TV industry and international key stakeholders including a speaker from Netflix, will take part in the summit.
"Addressing South Africa's positioning in the film sector, not only on the continent but also globally, is an important one if the country is to compete in the creative economy", concludes the Minister.
View Summit Documents below:
Executive Report of SA Film Summit
Full Report of SA Film Summit
Summit Presentations
Copyright IP and National Development Ashraf Patel Feb 2019 Commission
MISTRA SA Scenarios 2030 SA Film Summit 5 Feb 2019
NEF Presentation to DAC SA Film Summit 2019
The Fourth Industrial Revolution and Society - Prof Marwala
PWC Entertainment & Media Outlook: an African Perspective 2018–2022
Rapid Lion CLOSING THE LOOP – A Strategy for the Radical Economic Transformation of the South African Film Industry
South African Film Summit Programme
South African Film Summit Booklet
AU/AFRICAN AUDIO-VISUAL INITIATIVES
DISTRUPTION & INNOVATION IN THE VALUE CHAIN
Africa Film Summit April 06 Report
AU Agenda 2063
Telecommunications and Postal Services:
"Universal Access and Service to ICT for All"
National Integrated ICT Policy Discussion Paper
State of ICT Sector Report
EMPOWERMENT, INCLUSIVITY & CAPACITY BUILDING
OWNING THE SA STORYLINE
Code of Good Practice Sexual Harassment in the Workplace
NFVF Final Report Film Industry Skills Audit
NFVF Graduate Absorption Study
NFVF SWIFT Gender Matters in the SAFI Report
Revised White Paper on Arts, Culture and Heritage Fourth Draft
SA Report Women in Media and Film Industry Report
A SWIFT MISSION: Tackling the Gender Disparities in South Africa's Film and Television Industry
Research into Audience Trends of the South African and KwaZulu Natal Film Industry Consumer
The Role of Terms of Trade in the Development of the UK Independent Production Sector
Draft Revised Classification Guidelines
Final Copyright Amendment Bill 15.11.2018
NFVF ARP Findings in Infographics
Publication Report - Intellectual Property in the Cultural and Creative Industries
SA Box Office Report
SABC-IPO-SASFED Report
POLICY, STRUCTURE & INSTITUTIONS
REFERENCE GROUP DOCUMENTS, PROGRAMME
DAC 2018 Annual Performance Plan
CIGS Report
Cultural and Creative Industry Trends
Mzansi Golden Economy Strategy
Research Report - Employment in the Cultural and Creative Industries in South Africa
The establishment of the Film Sector as a catalyst for Economic Growth in South Africa: Toward a Sallywood Framework
Discussion Document on the inquiry into subscription television services Gazette 41070
Multichoice Inquiry into Subscription TV Presentation
Parrot Analytics South Africa SVOD Market Analysis
SABC Inquiry into Subscription TV Presentation


At the Canadian customs booth I handed over our passports. "Where are you going then today, ma'am?" asked a border agent who sounded suspiciously Scottish, making me wonder if I had crossed an ocean when I wasn't looking. I told him. "Visiting who, exactly?" he asked, eyes narrowing on our pictures. I told him.
"And you've got permission from the father, then, have you?"
Here was the rub. The day before my son had eaten the handlebars on his bike and he now had a fat lip and a bruised cheek. A consequence of this little accident was a free-floating epiphany I had when loading up the car: my son and I have different last names. Now my worries were confirmed: the agent suspected I was poaching my son across the border.
"A note, perhaps? Notarized letter telling me it's okay for you to be traveling with your son?" I felt like I was in elementary school needing a pass to go to the dentist.
"You could call my husband," I answered.
His eyes flashed, slightly perturbed. "I could at that, you know," he huffed.
Chastened, I decided not to offer much else in the way of suggestions.
"So," he said, continuing to look over our unchanged documents, "what evidence do I have that you actually have permission to take this boy to Canada?"
"Roll down the boy's window, please." I did as he asked. "Get back in your seat ma'am." I sat back down with a goofy half grin on my face, trying to look placid and not bemused. I think I succeeded in only looking mentally challenged.
"Can you tell me who this lady is?" he asked through the rear window of the car.
For a brief moment I choked on the fear that my son would freeze up, that he would clamp down on his shockingly vivid vocabulary as a result of stress, but he answered crystal clear, bless him. "That's my mom," he said.
"And where's your father?" he asked.
"At home," said my boy, calm as a cucumber. "He's working."
"So he couldn't come with you?"
"Nope. He's working hard in the studio."
"You live with both your mother and your father?"
"You going to buy your father a present to bring back home?"
He paused thoughtfully before answering. "No, I don't think so," he answered earnestly. I laughed, both in relief of my son's casual demeanor, but at the fact that he was probably right. We never buy anything anywhere.
The border guard apparently got what he needed from my son and let us cross. We went to Vancouver where my brother proceeded to out-cool us to such a shockingly amazing degree that my husband and I may never recover in our son's eyes.
But I will always get permission from my husband to travel, an oddness of epic proportion in the era of enlightened individuality and post-feminism.
Or maybe I'll just change my name.
*When we got to Vancouver and his uncle put the whammy on him with his awesomeness, my son said, "I love Vancouver. I LOVE Vancouver! But I hate Antarctica. I'm going to firebomb it instead. Plus, it will get warmer, which is good." This unleashed a completely baffling discussion about climate change.
This is how it goes in our family, any excuse taken for expounding too much and too long on any subject at any time. The kid can never catch a break, poor fellow.


text:
Rights of victims resident in another Member State (Article 17 of the EU Directive)
Suzan van der Aa
Strengthening judicial cooperation to protect victims of crime. Handbook
Superior Council of Magistracy of Romania
van der Aa, S. (2014). Rights of victims resident in another Member State (Article 17 of the EU Directive). In Strengthening judicial cooperation to protect victims of crime. Handbook (pp. 106-112). Superior Council of Magistracy of Romania.
van der Aa, Suzan. / Rights of victims resident in another Member State (Article 17 of the EU Directive). Strengthening judicial cooperation to protect victims of crime. Handbook. Bucarest : Superior Council of Magistracy of Romania, 2014. pp. 106-112
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publisher = "Superior Council of Magistracy of Romania",
address = "Romania",
van der Aa, S 2014, Rights of victims resident in another Member State (Article 17 of the EU Directive). in Strengthening judicial cooperation to protect victims of crime. Handbook. Superior Council of Magistracy of Romania, Bucarest, pp. 106-112.
Rights of victims resident in another Member State (Article 17 of the EU Directive). / van der Aa, Suzan.
Strengthening judicial cooperation to protect victims of crime. Handbook. Bucarest : Superior Council of Magistracy of Romania, 2014. p. 106-112.
T1 - Rights of victims resident in another Member State (Article 17 of the EU Directive)
AU - van der Aa, Suzan
BT - Strengthening judicial cooperation to protect victims of crime. Handbook
PB - Superior Council of Magistracy of Romania
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van der Aa S. Rights of victims resident in another Member State (Article 17 of the EU Directive). In Strengthening judicial cooperation to protect victims of crime. Handbook. Bucarest: Superior Council of Magistracy of Romania. 2014. p. 106-112


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The York County Early Childhood Coalition is a team of business people, service providers and community members interested in advocating for high-quality early childhood care and education across York County. The group is hosted at United Way of York County and had its beginnings as a team created in November 2007 to attend the Governor's Economic Summit on Early Childhood Care and Education. In addition to the promotion of Born Learning, the group has also launched a Speakers Bureau, and hosts a yearly York County Community Conversation on Early Childhood.
Current Coalition activities include national and statewide advocacy; education and outreach on issues related to the importance of investing in young children; grass-roots engagement; and community displays promoting the annual Week of the Young Child in April.
Vision: The future prosperity of our county is based on the positive early development of our children. The first five years are crucial for all children in developing the skills they need as the workforce of tomorrow.
Mission: The York County Early Childhood Coalition, hosted by United Way of York County, promotes the importance of successful birth to five experiences. It is our goal that as a community we ensure a healthy foundation that prepares every child for a bright future.
Interested in becoming a Volunteer Reader? Join the project and share your love of reading while boosting early literacy.
Maine KIDS COUNT, a project of the Maine Children's Alliance, is part of the national KIDS COUNT Network, a state-by-state effort generously funded by the Annie E. Casey Foundation (AECF) to track the status of children across the United States. Since 1994, the Maine KIDS COUNT project has published the annual Maine KIDS COUNT Data Book using the most recent data available on the well-being of children in the areas of physical and emotional health, social and economic status, and child care and education.
"The indicators in this report can be used to support the healthy development of young children, to better understand and address inequities across racial and cultural groups, and to provide a common language that facilitates communication and coordination on behalf of all young children."
2010: Babies to Business: Workforce Development Starts Today!


The benefits of science experiments for kids are significant. Not only do science experiments help children learn about well, science, they also teach skills like patience, problem-solving, and perseverance. Plus science is fun! If you have any doubts about that, check out these 10 super fun and simple science experiments for kids to try at home!
Nearly every kid goes through a dinosaur phase and many never leave it. And there's a good reason for that. Dinosaurs are awesome! And if you have a child in the toddler to kindergarten age range, they probably agree with me. Which is why your kids will love making dinosaur fossils.
I can't believe how cool this experiment is. It does require some pre-planning to purchase supplies but everything needed is inexpensive. And the project itself is easy. You'll totally wow your budding paleontologist with this awesome science experiment for kids.
It's no secret that explosions are one of the most fun outcomes of a science experiment. At least the ones that happen on purpose. And this science activity gives your kids a chance to play with the results of their foamy, explosive mess.
The name of this experiment makes me smile. And the results are super fun for kids. This science activity can keep your little ones occupied for hours (that's the dream, right?).
Be sure to read the entire post. Kim includes ideas for customizing or scaling up the experiment to fit your needs. And to keep the experiment interesting over and over again.
This is a simple science experiment for preschoolers. And it's something you don't need to buy anything special to do. But you do need to plan ahead by at least a day or so to have time for everything to freeze.
Basically, this involves freezing the contents of your junk drawer in a large chunk of ice. Your kids can then experiment with melting, poking, and examining the frozen block and what's inside. It's messy, it's fun, and I suggest you take it outside on a hot summer day.
As a bonus, it might even inspire you to organize the junk from your drawer after everything has been thawed and dried.
This is a simple science experiment even young toddlers can do. And it's perfect for warm weather days if you want to take it outside.
There is nothing tricky here. It involves letting your child experiment with different objects to see what will float, what will sink, and what will hold water. Kids love playing in the water. I struggle to convince my kids that we shouldn't keep the water toys out Fall, Winter, and Spring as well as Summer.
And I love water experiments with kids because no matter how messy they get it still cleans up pretty easy.
If your kids love rainbows, this is the perfect science experiment for them! It's a fun way to teach your child primary and secondary colors as well. It does take patience to see the final result. Which is as valuable a skill for kids to learn as anything discovered by science.
Supplies needed are simple and likely things you already have. So, since this one takes a while, go ahead and get started today!
This activity is a fun way to cultivate your child's creativity. And a great project for school-age kids. Take some time to talk to your kids about recycling. Why we do it and the kinds of things we can recycle.
Then have a brainstorming session! Let their creativity run wild! Not all the ideas will pan out. But be sure to follow up the best ones by gathering supplies and building your child's recycled items project!
I'll admit that chemistry was never my favorite subject. It combined all the things about math and science that I struggled with in school. But the experiments could be so much fun! And that's the part to emphasize with your kids when you do science experiments at home.
This is an awesome way to try some chemistry at home with your kids. The supplies needed are pretty basic but be sure to follow the instructions to get the most out of the experimentation process.
Often the science activities my kids enjoy the most are the simplest to do. This experiment with oil, water, and food coloring is simple. But there is plenty to learn from simple science activities. And this one is really cool.
Younger kids especially will get a kick out of watching the "color explosions".
Your kids will be excited about this one! There are eruptions AND toy cars. It's a magical combination. This clever science experiment is a great way to beat boredom on a long day at home. Definitely, something to try out this summer when your kids need a new activity.
The science behind baking soda and vinegar explosions is one of the best things to teach your kids because there is so much they can do with it. And this car wash is one of the more fun possibilities I've seen. My kids loved it!
This is another color experiment for your younger kids to try at home. Color science is pretty AND fun. Plus this one has candy! This is a perfect science experiment for preschoolers. And toddlers too.
Not only will your kids enjoy watching the colors travel and mix. They'll love eating the leftover supplies from this experiment. Which isn't something many scientists can say.
I let my kids swirl the colors around and mix colors which my preschooler and toddler both enjoyed. Almost as much as they enjoyed eating the candy afterward.
What are your favorite science experiments to do with kids? Share your ideas in the comments!
Oh my gosh, these look so fun and engaging!! Can't wait to try them with my nieces and nephews.
You know what? That ice melt is genius. The other day, my kids watched ice cubes melt on the deck and thought it was the coolest thing ever. Totally doing this!
The dinosaur fossils would be fun. My 3 year old would have a blast and I think my 10 year old would even get on that one!
Thanks for reminding me about the walking rainbow and elephant toothpaste. I pulled together the supplies ages ago but never got around to doing either.
We did the skittle experiment last year at VBS. The kids loved it and were so impressed.


text:
Morning News takes West of England crown
by Paul Linford Published 03 Jul 2014 Last updated 04 Jul 2014
The Western Morning News was named best daily newspaper in the South West yesterday while the Western Gazette took the weekly crown.
This year's EDF Energy South West Media Awards, held in association with HoldtheFrontPage. were handed out in a lunchtime ceremony at the Bristol Hotel in Bristol.
There were three awards for the Bristol Post including Specialist Journalist of the Year and Digital Journalist of the Year but it was pipped to the Daily Newspaper of the Year prize by Local World sister title the WMN.
Plymouth daily The Herald took two awards with the Newcomer of the Year and Daily Journalist of the Year prizes.
Western Morning News editor Bill Martin said: "This is a fantastic recognition of all the hard work the Western Morning News does to champion the Westcountry and stand up for and stand by the issues that really matter to the people that live and work here.
"It's also a great recognition of all the hard work of all the staff in all departments across the region."
WMN editor Bill Martin with the paper's award
Coverage of the winter floods figured heavily among the winners with ITV News Westcountry picking up Television News or Current Affairs Programme of the Year, for its coverage of the Dawlish rail line collapse.
BBC Radio Somerset's Ben McGrail Programme won best Radio News Programme, for its coverage of the floods which struck large parts of the county while the Helston Packet won Front Page of the Year for 'Storm Force', with a striking image capturing the moment giant waves engulf seafront buildings on the Cornish coast.
Now in their tenth year, the awards honour the best in local journalism in Cornwall, Devon, Somerset, Gloucestershire, Wiltshire, Bath and Bristol.
Tim McCoy, EDF Energy's Head of Media Relations, said: "Congratulations to all the winners. They are clearly at the top of their game, producing content that resonates with their readers and viewers, and reflecting the lives and concerns of communities across the South West of England."
The full list of winners was as follows:
Newcomer of the Year: Sian Davies, The Plymouth Herald
Digital Journalist of the Year: Emily Koch, Bristol Post
Specialist Journalist of the Year: Gavin Thompson, Bristol Post
News photographer of the Year: Paul Nicholls, Gloucestershire Media
Sports Journalist of the Year: Steve Cotton, Bristol News and Media
Feature Writer of the Year: Steven Impey, Gloucestershire Media
Columnist of the Year: Barrie Hudson, Swindon Advertiser
Designer of the Year: Jon Lewis, Express and Echo
Radio Journalist of the Year: Chris Brierley, BBC Bristol
TV Journalist of the Year: David Woodland, ITV Westcountry
Print Journalist of the Year (Weekly): Siobhan Stayt, The Bath Chronicle
Print Journalist of the Year (Daily): Claire Jones, The Plymouth Herald
Website of the Year: SoGlos.com
Community Campaign of the Year: Mid Devon Gazette, Protect Our Kids
Front Page of the Year: Helston Packet, Storm force
Radio News/Current Affairs Programme of the Year: BBC Somerset, Ben McGrail Programme
Television News/Current Affairs Programme of the Year: ITV Westcountry, Dawlish Rail line
Weekly Newspaper of the Year (Free): Nailsea, Clevedon and Portishead Times
Weekly Newspaper of the Year (Paid for): Western Gazette
Daily Newspaper of the Year: The Western Morning News
Ken S, Portsmouth
A cracking paper produced by a cracking team. Well done to Bill and the team at the Western Morning News
downnotout
Congratulations to all the winners. But wait… Nothing for the Western Daily Press? I remember the day when it scooped almost everything – even on the national stage. Could its sad demise be anything to do with the quality of its recently appointed management?
Avron.
The Western Daily Press passed away about three months ago I think.
Dipstick
Looks like a case of downANDout.
But is this really true the WDP has finished? I knew a few old sods who worked on it once upon a time and they won't have heard (rest homes etc).
A chance to recall very happy days, maybe.
Can someone please set the record straight?
Idle Rich
@downnotout
I don't know the Western Daily Press either then or now but just to say winning a little plastic thing is not always the sign of a good paper, day in day out. It's the sign of a paper that likes (needs?) to win awards, for whatever reason.