Real-time interaction has become a key feature of most Web applications,
enabling live discussions over polemic topics
such as Politics, Sports Competitions and all kind of buzz topics
that pop up on news media~\cite{CrowdDiscoveryWSDM11,pcalaisKDD11}.
Indeed, dealing with web content as soon as it is generated have
unleashed a number of researches that try to adapt key Web
application such as search~\cite{OReilly09,SpamElections} and recommendation~\cite{RealTimeRecommendation}.

One of the most promising applications that explore the real-time nature of today's Web is to summarize the large volumes
of opiniated content generated in social media systems while real life events are developing. The main goal is
infer the polarity of text documents with respect to a given object, usually, products, services or
individuals~\cite{sentiment_analysis}, as well as more complex and subjective impressions
such as happiness and the use of irony~\cite{ThumbsUp,MultiFaceted}. Recently, some attempts
have been made to analyze opinions in real time~\cite{CrowdTwitter,BrevityTwitter}. However,
analyzing text documents is still a huge challenge.

In addition to generating written comments, users in social
media systems can voice their opinions through different mechanisms: the groups
the user has joined also unveil his inclinations regarding a specific
topic, as well as the content he or she recommended to friends.
Users may also unveil their inclinations by posting URLs to blogs
and new media articles which contain arguments in favour of their
position. 

A key observation in this context is that opinions and people
who emit them are not randomly correlated; in fact, human beings exhibit a varying degree of \emph{bias}, an inherent feature of human behavior defined
by social theories as the lack of appropriate balance, neutrality and critical doubt in
argumentation~\cite{Walton}. One of the most clear manifestations of bias is
when someone supports one side too strongly or too often~\cite{Bias_Politics}. This
is very noticeable when we think on supporters of a football team or people
who have a defined political view, such as liberal and conservatives.

Measuring the bias of users with respect to a discussion in social
media sites is useful for several reasons. First, knowing the bias
of content generators can help assessing how \emph{reliable}
the information they provide is. A biased political commentator,
for example, will be inclined to provide positive information about
a candidate and hide negative information about him. In the context
of polemic, heavily-debated topics, the bias of opinion holders
works as a measure of the reputation of this user. 

User bias information can also support content analysis, due to
its consistent, robust nature~\cite{pcalaisKDD11}. While text is subject to be influenced
by external factors, such as new discussions that arise, users are less prone to external perturbations; they
usually will change their view over a topic only in response to major
changes in the facts over which they are emiting opinions. Furthermore,
knowing user preferences and inclinations is key to offering
personalized services and recommendations~\cite{WebPersonalization}.

In this paper, we propose and evaluate a novel, unexplored trace of social media user behavior
that provide evidence to predict their inclinations regarding a topic or subject: their \emph{temporal
behavior}. In today's \emph{real time web}, users can publish content and manifest 
themselves in instantaneous reaction to real-life events that catch
their attention. This assumption is quite natural in live discussions
on sports competitions, in which match events and the teams which
are currently playing influence the decision of social media
users to post or not. To illustrate our idea, we plot in Figure~\ref{fig:game} the buzz
in Twitter during the Brazilian Soccer League match that clashed Atletico Mineiro vs Cruzeiro, which
took place in October 2010 and was won by Atletico by 4-3. Notice that some users react heavily
during goals. In the context of Politics, it is expected that when bad news about a candidate
arise in the news media, users which are on opposite sides will be more inclined to post
with more intensity than the ones who support him.

Our motivation to design a temporal-based bias classification
scheme is twofold. First, the feasibility of such classifier 
sheds light in a remarkable aspect of current social
media platforms: their dynamic nature and the notion
that, in addition to what you post, \emph{when} you
post may play a role in the influence and
reachability of your actions. Furthermore, investigating
strategies of bias measurement which are content-independent
are more likely to generalize to multiple domains, platforms
and languages.

Our main contributions are:

\begin{itemize}

\item we show that, in social media systems, the moment
in which social media users manifest their opinions are
not random and has a degree of relationship with their
bias/preferences;

\item we propose a simple linear classification algorithm
that can identify user bias based solely on their
temporal behavior.

\item we show the feasibility of our approach in
two topics that drive live, real-time discussions
in the Twitter microblogging system: the Brazilian
2010 Soccer League and the 2010-2011 National
Football League (NFL) Season.

\end{itemize}

\begin{figure}[t]
  \centering
  \includegraphics[width=0.65\textwidth]{figures/classico.pdf}
  \caption{Reactions in Twitter during Atletico Mineiro vs Cruzeiro soccer match. FIGURE NEEDS TO BE UPDATED.}
  \label{fig:game}
 % \vspace{-0.4cm}
\end{figure}

Our paper is organized as follows. In Section~\ref{sec:relatedWork}
we discuss related work. In Section~\ref{sec:algorithm} we present
our temporal user classification algorithm, and a case study
of our strategy is presented in Section~\ref{sec:results}. Finally,
Section~\ref{sec:conclusion} presents conclusions and future
directions.