In today's Web, several microblogging platforms allow anyone to
comment events as soon as they occur. Because of such resource, users who are biased
preferably comment the events that support their opinions, we have
assumed that microbloggers with similar biases send messages at about
the same time. We have designed a very simple algorithm only relying
on this idea. It partitions the stream of messages w.r.t. to a fixed
time interval and computes, from known users in every interval
(supervised learning), the bias of this interval and modify its users'
biases accordingly.

Our results show that temporal information can add useful knowledge
about users in three scenarios (soccer, football and political discussion in Twitter). Therefore, we can confirm our assumption: microbloggers with
similar biases tend to express themselves synchronously. Besides
those results, we have emphasized the two main advantages of our
approach. First of all, it is applicable to any topic that is
discussed live on the microblogging platform, whatever the language of
the discussion. Indeed, our approach completely disregards the
content. For the same reason, it is fast and even a modest hardware
can process, on the fly, all Twitter messages about a popular topic
like soccer.

This opens up many applicative perspectives. Indeed, knowing the
preferences and inclinations of a user is a key to offer her
personalized (hence better) content, services, recommendations,
advertisement, etc. We hope our work motivates researchers to use the temporal dimension to build and design applications that use it to customize and enhance user experience in social media. For example, a possible application is to design a real-time recommendation algorithm that recommends content in real-time, or recommends users that tend to post at the same time. From a sociologist point of view, this information
is valuable as well. It helps in grasping a collective view of the
users over polemic topic such as politics and sports.

Another application of our results is to combine the temporal
patterns we found with other evidences of user's preferences,
such as social ties and textual content, what could generate
better results than when these characteristics are considered in isolation.

As future work, we plan to investigate whether a sophistication of the
time interval selection (using, \eg, activity peak detection) may
provide more accurate results. If so, we could not only better learn
the user biases but also better understand how microblogging platforms
are used. Another exciting research direction is that of
semi-supervised and active learning. The former approach would use the
biases of \emph{all} users in every time interval, whereas the latter
would automatically populate the set of users with known biases (\ie,
that are used to derive the biases of the other users). Finally, the
temporal locality only is one evidence of the bias. Combining it with
other evidences, such as endorsements -- retweets -- and adoption of tags, could
provide greater recalls and, probably, more accurate results.


