56 research outputs found
Factors in Recommending Contrarian Content on Social Media
Polarization is a troubling phenomenon that can lead to societal divisions
and hurt the democratic process. It is therefore important to develop methods
to reduce it.
We propose an algorithmic solution to the problem of reducing polarization.
The core idea is to expose users to content that challenges their point of
view, with the hope broadening their perspective, and thus reduce their
polarity. Our method takes into account several aspects of the problem, such as
the estimated polarity of the user, the probability of accepting the
recommendation, the polarity of the content, and popularity of the content
being recommended.
We evaluate our recommendations via a large-scale user study on Twitter users
that were actively involved in the discussion of the US elections results.
Results shows that, in most cases, the factors taken into account in the
recommendation affect the users as expected, and thus capture the essential
features of the problem.Comment: accepted as a short paper at ACM WebScience 2017. arXiv admin note:
substantial text overlap with arXiv:1703.1093
Minimizing Polarization and Disagreement in Social Networks
The rise of social media and online social networks has been a disruptive
force in society. Opinions are increasingly shaped by interactions on online
social media, and social phenomena including disagreement and polarization are
now tightly woven into everyday life. In this work we initiate the study of the
following question: given agents, each with its own initial opinion that
reflects its core value on a topic, and an opinion dynamics model, what is the
structure of a social network that minimizes {\em polarization} and {\em
disagreement} simultaneously?
This question is central to recommender systems: should a recommender system
prefer a link suggestion between two online users with similar mindsets in
order to keep disagreement low, or between two users with different opinions in
order to expose each to the other's viewpoint of the world, and decrease
overall levels of polarization? Our contributions include a mathematical
formalization of this question as an optimization problem and an exact,
time-efficient algorithm. We also prove that there always exists a network with
edges that is a approximation to the optimum.
For a fixed graph, we additionally show how to optimize our objective function
over the agents' innate opinions in polynomial time.
We perform an empirical study of our proposed methods on synthetic and
real-world data that verify their value as mining tools to better understand
the trade-off between of disagreement and polarization. We find that there is a
lot of space to reduce both polarization and disagreement in real-world
networks; for instance, on a Reddit network where users exchange comments on
politics, our methods achieve a -fold reduction in polarization
and disagreement.Comment: 19 pages (accepted, WWW 2018
Quantifying and minimizing risk of conflict in social networks
Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people’s prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network’s connectivity.
Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these.
For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures.
Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk
Illuminating an Ecosystem of Partisan Websites
This paper aims to shed light on alternative news media ecosystems that are
believed to have influenced opinions and beliefs by false and/or biased news
reporting during the 2016 US Presidential Elections. We examine a large,
professionally curated list of 668 hyper-partisan websites and their
corresponding Facebook pages, and identify key characteristics that mediate the
traffic flow within this ecosystem. We uncover a pattern of new websites being
established in the run up to the elections, and abandoned after. Such websites
form an ecosystem, creating links from one website to another, and by `liking'
each others' Facebook pages. These practices are highly effective in directing
user traffic internally within the ecosystem in a highly partisan manner, with
right-leaning sites linking to and liking other right-leaning sites and
similarly left-leaning sites linking to other sites on the left, thus forming a
filter bubble amongst news producers similar to the filter bubble which has
been widely observed among consumers of partisan news. Whereas there is
activity along both left- and right-leaning sites, right-leaning sites are more
evolved, accounting for a disproportionate number of abandoned websites and
partisan internal links. We also examine demographic characteristics of
consumers of hyper-partisan news and find that some of the more populous
demographic groups in the US tend to be consumers of more right-leaning sites.Comment: Published at The Web Conference 2018 (WWW 2018). Please cite the WWW
versio
The Effect of Collective Attention on Controversial Debates on Social Media
We study the evolution of long-lived controversial debates as manifested on
Twitter from 2011 to 2016. Specifically, we explore how the structure of
interactions and content of discussion varies with the level of collective
attention, as evidenced by the number of users discussing a topic. Spikes in
the volume of users typically correspond to external events that increase the
public attention on the topic -- as, for instance, discussions about `gun
control' often erupt after a mass shooting.
This work is the first to study the dynamic evolution of polarized online
debates at such scale. By employing a wide array of network and content
analysis measures, we find consistent evidence that increased collective
attention is associated with increased network polarization and network
concentration within each side of the debate; and overall more uniform lexicon
usage across all users.Comment: accepted at ACM WebScience 201
Characterizing Controversiality of Topics Utilizing Eccentricity of Opinions
Access to abundant biased information in echo chambers and social bubbles often intensifies opinions to the extremes. The extremization of opinions results in several topics becoming controversial. However, it is very difficult to measure the degree of controversiality of a topic objectively since the controversiality of any topic is subjective and perceived differently from different communities. The absence of an objective measure of controversiality has been a major hindrance in understanding the causes and effects of it. In this work we propose a method to quantify controversiality of a topic by utilizing eccentricity of opinions on that topic. The eccentricity of an opinion is the amount of strangeness of the opinion relative to other opinions in the social neighborhood. The collective eccentricity of all opinions for a topic works as an indicator of the controversiality of that topic and can be represented by any measure of central tendency. With the help of social network data, we also demonstrate that opinions on several issues related to our routine life show similar trends for diversity though they differ in their controversiality
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