Bias has been defined by sociologists as a trace of people or
argumentations that exhibit one or more of the following
characteristics~\cite{Walton}: 1) lack of appropriate balance and
neutrality in argumentation, 2) lack of appropriate critical doubt, 3)
the arguer has a particular position over a subject, and 4) the arguer
has a personal interest in the outcome of an argument or discussion.

% The most common types of bias are, for individuals, the political or
% ideological bias~\cite{}, and, for news media
Most studies focus on the biases of news media. In this context,
\cite{MediaBias} distinguishes the \emph{selection bias}, which guides
the decision of which events are covered (a biased media outlet will favour some types of news), from the \emph{description
bias} --- the veracity of the media coverage. A way to measure media
biases is to count the number of times a particular media outlet cites
various think tanks and policy institutes. By comparing those numbers
to the citation rates of the same organizations by congressmen, a
strong liberal bias has been unveiled in the US news
media~\cite{MeasureOfMediasBias}. Our proposal focuses on the
selection bias. It assumes that sending a message using the
microblogging platform indicates the selection of the event it is
about. Although the selection bias has been originally defined for news media (and
some users of Twitter are indeed media~\cite{TwitterSocialMedia}), our proposal successfully
applies to ``regular'' users as well.

The study of biases in social media has mainly focused on
bloggers. The main approach to map the opinions of the blogosphere
relies on the link structure that connect the blogs. It assumes that
blogs with similar views are likely to cite each other~\cite{BLEWS}.
In this way, US political blogs can be clustered into two main groups
(liberal/conservative)~\cite{DividedTheyBlog}. A more recent work uses
the graph induced by endorsements (\eg, ``retweets'' in Twitter or the
``Like'' button in Facebook), which are assumed to indicate
similarities of views, to predict user
biases~\cite{pcalaisKDD11}. Those approaches, although highly
accurate, may be computationally prohibitive, \ie, unable to process
on the fly the stream of messages about a popular topic.

In this paper, we propose to observe the \emph{temporal behavior} of
microbloggers to discover their biases. This idea is motivated by
previous works that have shown temporal patterns in social media
systems. For example, the popularity of textual phrases and hashtags
along time follows six different
patterns~\cite{TemporalVariationWSDM11}; the communications among
social media users form transient communities (\ie, clusters that
discuss a topic in specific moments)~\cite{CrowdDiscoveryWSDM11};
among other works. Thus, taking into consideration the temporal
aspects of the microblog analysis looks promising. Indeed, one of the
main appeals of microblogging is the ability to comment live events. A
selection bias should make it more likely that users express
themselves right after (or, maybe, before if the event is planned) an
event supporting their bias. For example, a soccer supporter is more
likely to react to a goal scored by her favorite team than by another
team (she may react to goals made by the opponent, but over all data,
she will more often comment her favorite teams' events). A political
partisan is eager to immediately comment a faulty statement made by
and adversary candidate. On the contrary, partisans of the candidate
prefer to ignore her/his declaration.

Even without any information about those external events and without
considering the written content or the relations between the users,
our work shows that it is possible to learn user biases from those of
known users who express themselves at about the same time (supervised
learning).
% To the best our knowledge, our work is the first attempt to
% \emph{learn} user profiles from their temporal behaviors.
Notice that this temporal locality has nothing to do with concept
drift~\cite{LearningConceptDrift}. Supervised classifiers taking the
latter effect into
consideration~\cite{LcrochaTemporal,TSallesTemporal} assume that the
relationship between attributes and classes changes over time.
% Our work, on the other hand exploits the temporal locality as the
% property which is useful characterize microblog users and their
% inclinations towards a topic. In addition to that, we show that a
% simple classifier is capable to capture useful user patterns, and thus
% can nicely complement, or even replace, current graph-based strategies
% that mine user profiles.
% Our user classification model is novel because it is based
% \emph{solely} on the temporal behavior of users, i.e., the moment in
% which they post their messages in a social media environment.
