Information Consumption on Social Media: Efficiency, Divisiveness, and Trust

This new model has many advantages: the propagation of news is faster, the
number of news sources is large, and the topics covered are diverse.
However, in this new model, users are often overloaded with redundant
information, and they can get trapped in filter bubbles by consuming
divisive and potentially false information. To tackle these concerns, in
my thesis, I address the following important questions:
• (i) How efficient are users at selecting their information sources? We
have defined three intuitive notions of users’ efficiency in social media
– link (the number of sources the user follows), in-flow (the amount of
redundant information she acquires), and delay efficiency (the delay with
which she receives the information). We use these three measures to assess
how good users are at selecting who to follow within the social media
system in order to acquire information most efficiently.
• (ii) How can we break the filter bubbles that users get trapped in?
Users on social media sites such as Twitter often get trapped in filter
bubbles by being exposed to radical, highly partisan, or divisive
information. To prevent users from getting trapped in filter bubbles, we
propose an approach to inject diversity in users’ information consumption
by identifying non-divisive, yet informative information. We propose a new
method to identify less divisive information on controversial topics using
features such as the publishers’ political leaning.
• (iii) How can we design an efficient framework for fact-checking? The
proliferation of false information is a major problem in social media. To
counter it, social media platforms typically rely on expert fact-checkers
to detect false news. However, human fact-checkers can realistically only
cover a tiny fraction of all stories. So, it is important to automatically
prioritize and select a small number of stories for human to fact check.
However, the goals for prioritizing stories for fact-checking are unclear.

We identify three desired objectives to prioritize news for fact-checking.
These objectives are based on the users’ perception of the truthfulness of
stories. Our key finding is that these three objectives are incompatible
in practice.