Measuring and moderating opinion polarization in social networks

Matakos, Antonis; Terzi, Evimaria; Tsaparas, Panayiotis

doi:10.1007/s10618-017-0527-9

Cited by 126 publications

(113 citation statements)

References 26 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…Given the importance such users appear to have in the network structure (higher PageRank, and higher indegree (more followers)), this aspect remains to be studied in future work. In the former case (i.e., if gatekeepers are open-minded net-citizens), gatekeepers would be good candidates for users to nudge towards the opposing side [17,18,30]. The possibility of identifying gatekeepers to a non-random extent by just using network features (e.g., if they do not actively produce content) makes an interesting application.…”

Section: Discussionmentioning

confidence: 99%

Political Discourse on Social Media

Garimella

Morales

Gionis

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

229

View full text Add to dashboard Cite

Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared, and the "chamber" (i.e., the social network) that allows the opinion to "echo" (i.e., be re-shared in the network)and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a "price of bipartisanship" in terms of their network centrality and content appreciation. In addition, we study the role of "gatekeepers, " users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.

show abstract

Section: Discussionmentioning

confidence: 99%

Political Discourse on Social Media

Garimella

Morales

Gionis

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

229

View full text Add to dashboard Cite

show abstract

“…A related body of algorithmic work [12,25,26] posits that one way to decrease polarization in social networks is to connect people with opposing views by exposing them to new information. Such work differs in focus and approach to modeling from this work because that work is concerned with poor connectivity between communities and we are concerned with individuals who are simply poorly connected.…”

Section: Related Workmentioning

confidence: 99%

Gaps in Information Access in Social Networks?

Fish

Bashardoust

boyd

et al. 2019

The World Wide Web Conference

View full text Add to dashboard Cite

The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e.g. job openings or advertisements for loans. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected individuals are systematically less likely to receive the information, producing a gap in access to the information between individuals. In this work, we study how best to spread information in a social network while minimizing this access gap.We propose to use the maximin social welfare function as an objective function, where we maximize the minimum probability of receiving the information under an intervention. We prove that in this setting this welfare function constrains the access gap whereas maximizing the expected number of nodes reached does not. We also investigate the difficulties of using the maximin, and present hardness results and analysis for standard greedy strategies. Finally, we investigate practical ways of optimizing for the maximin, and give empirical evidence that a simple greedy-based strategy works well in practice.

show abstract

“…To the best of our knowledge, this is the first work to approach this problem from the point of view of increasing diversity of information exposure, and formulating it as a quadratic knapsack-style problem. Detecting polarization: Recently, a significant body of work has emerged that focuses on measures for characterizing polarization in online social media [2], [5], [10], [16], [22]. These works consider mainly the structure in social-media interactions and quantify polarization or compute node polarity scores using network-based techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Reducing polarization: Given the negative effects of fragmentation, there has been recent work that focuses on methods for reducing polarization [11], [22], [24]. Matakos et al [22] study the problem of convincing a set of individuals to adopt a neutral opinion and act as mediators in the discussion. Musco et al [24] study a similar problem, albeit with the dual objective of minimizing both polarization and disagreement among individuals.…”

Section: Related Workmentioning

confidence: 99%

Tell me Something My Friends do not Know: Diversity Maximization in Social Networks

Matakos

Gionis

2018

2018 IEEE International Conference on Data Mining (ICDM)

Self Cite

View full text Add to dashboard Cite

Social media have a great potential to improve information dissemination in our society, yet, they have been held accountable for a number of undesirable effects, such as polarization and filter bubbles. It is thus important to understand these negative phenomena and develop methods to combat them. In this paper we propose a novel approach to address the problem of breaking filter bubbles in social media. We do so by aiming to maximize the diversity of the information exposed to connected social-media users. We formulate the problem of maximizing the diversity of exposure as a quadratic-knapsack problem. We show that the proposed diversity-maximization problem is inapproximable, and thus, we resort to polynomial non-approximable algorithms, inspired by solutions developed for the quadraticknapsack problem, as well as scalable greedy heuristics. We complement our algorithms with instance-specific upper bounds, which are used to provide empirical approximation guarantees for the given problem instances. Our experimental evaluation shows that a proposed greedy algorithm followed by randomized local search is the algorithm of choice given its quality-vs.efficiency trade-off.

show abstract

Measuring and moderating opinion polarization in social networks

Cited by 126 publications

References 26 publications

Political Discourse on Social Media

Political Discourse on Social Media

Gaps in Information Access in Social Networks?

Tell me Something My Friends do not Know: Diversity Maximization in Social Networks

Contact Info

Product

Resources

About