% 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}.
Microblogging platforms such as Twitter (or the similar features of
social networks such as Facebook) grant the ability to express one's
opinions in a real-time fashion. Web users now may comment political
speeches, sports competitions, or any ``buzz'', \emph{while} they
occur~\cite{CrowdDiscoveryWSDM11,pcalaisKDD11}. Taking into account
this instantaneous aspect of the Web tells a lot about its users. For
example, it allows to better answer Web search
queries~\cite{OReilly09,SpamElections} or to provide better
recommendations~\cite{RealTimeRecommendation}.

% One of the most promising applications, which 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 this paper, we show that the time at which messages are sent in
social media platforms (and, more specifically, in microblogs) can
ease the automatic understanding of the opinions they
express. Usually, algorithms rely on the content of the messages to
infer the sentiment the author has w.r.t. an entity, \eg, a product, a service
or a political person~\cite{sentiment_analysis,ThumbsUp,MultiFaceted}.
In this paper, we argue that, in some scenarios, we can rely on
\emph{when} the messages were sent to predict the user views regarding
a given topic. Those scenarios are the ones in which people comments
are driven by real-life events that motivate them to react or not
depending on their preferences. In the context of microblogging, this paradigm shift
may greatly enhance the quality of the results. Indeed, the ability to
learn from the posted content not only suffers from the difficulty to
process natural languages (that can be, \eg, ironic) but also from the
shortness of the messages. For instance, temporal information is
essential to learn anything about a supporter writing ``Goal!'' during
a soccer match.

%~\cite{CrowdTwitter,BrevityTwitter}

% 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 clearest 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.

This paper deals with determining (or learning) the \emph{bias} of microbloggers
w.r.t. a given topic based on their \emph{temporal behavior}. Social
theories define bias as the lack of appropriate balance, neutrality
and critical doubt in argumentation~\cite{Walton}. Bias is inherent to
humans, which tend to support one side too strongly or too
often~\cite{Bias_Politics}. This behavior is particularly noticeable
with sport or political supporters
% which voice positive comments in favor of their sides much more
% frequently than a neutral, a biased person would.
and our proposal makes the assumption that it does
% a fraction of manifestations which are positive or negative towards a
% specific side of a discussion (e.g, a person or a football team) do
not occur at random times but, usually, in reaction to particular
events. As a consequence, people having the same bias would often
express it at the same time on a microblogging platform. For instance,
soccer supporters will write their joy after a goal for their team,
political partisans will be eager to comment a new scandal involving
the opposing party as soon as it is revealed, etc. Furthermore, most
microblogging platforms make it easy to repeat the message of another
user. The use of such mechanism amplifies the link between temporal locality and similar
bias. Indeed, repeating a message indicates an endorsement of its content
(hence the similar bias~\cite{pcalaisKDD11}) and usually happens within a short period of
time because it only takes one click to do so and the carried
information quickly is considered outdated.

We have designed, implemented and evaluated a novel and simple
algorithm that learns the bias of microbloggers from the timestamps of
their messages. It may be applied to any topic (\eg, the Brazilian
soccer championship or an electoral campaign) and regardless of the
language (\eg, Portuguese or English). In fact, the actual content of
the messages is not used at all by our approach and that
explains another advantage of our algorithm: its efficiency. Even
being executed on a modest hardware, the whole stream of messages about a
given topic may be processed on the fly.
% This is crucial to deal with vast amounts of data generated on a
% continuous flow.
In comparison, a real-time analysis based on the written content may
become intractable when considering the rate of the message flow about
a popular topic (such as soccer) on a popular platform (such as
Twitter).

% 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.

This temporal ``pattern'' in the usage of microblogging platforms is remarkable and
opens-up great perspectives. Of course, the bias is, by itself, a
useful piece of information. Nevertheless, other interesting measures
may be (at least partially) derived from it. For instance, it may help
in identifying the \emph{reliability} of a content generator (\eg, a
political commentator). It can also be key to provide personalized
services such as recommendations~\cite{WebPersonalization} because,
unlike the posted content that fluctuates w.r.t. external
perturbations (\eg, along a discussion with an opponent), the bias is
consistent and robust~\cite{pcalaisKDD11}. It is valuable as well for the
light it sheds on this subject.

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 show that microbloggers do not express their opinions at
  random times but follow a temporal ``pattern'' that reveals their
  bias;

\item we propose an efficient linear time algorithm that solely relies on
  temporal information to learn these biases;

% \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.

\item we apply and validate our approach using messages from three topics that
drive live reactions in the Twitter microblogging platform: the Brazilian 2010
Soccer League, the 2010-2011 National Football League (NFL) season and the 2010
Brazilian Presidential Elections. Our results show that temporal information
greatly helps in discovering user preferred sides. In fact, our proposal
correctly identifies the dominant opinion of between 70\% and 80\% of the users
who frequently post content on Twitter.

\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}

The remainder of this paper is organized as
follows. Section~\ref{sec:relatedWork} discusses the related
work. Section~\ref{sec:algorithm} presents our
algorithm. Section~\ref{sec:results} empirically studies its
behavior. Section~\ref{sec:conclusion} concludes and presents
future research directions.
