Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

Abstract

Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individual-level) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using three popular community detection algorithms for twelve real-world news spreading networks collected from Twitter. Experimental results show that the proposed metrics perform significantly better on the fake news spreading networks than on the true news, indicating that our community health assessment model is effective.

Keywords

Social media, fake news, community health, detection algorithms

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

ASONAM '19: Proceedings of the IEEE/ACM International Conference on Social Networks Analysis and Mining: Vancouver, Canada, August 28-30

First Page

432

Last Page

435

ISBN

9781450368681

Identifier

10.1145/3341161.3342920

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3341161.3342920

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