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.
|Original language||English (US)|
|Title of host publication||Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019|
|Editors||Francesca Spezzano, Wei Chen, Xiaokui Xiao|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|State||Published - Aug 27 2019|
|Event||11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada|
Duration: Aug 27 2019 → Aug 30 2019
|Name||Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019|
|Conference||11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019|
|Period||8/27/19 → 8/30/19|
Bibliographical noteFunding Information:
identify the vulnerable nodes for false news networks with higher precision. Acknowledgement: This work is supported in part by NSF grant III-1422802.
© 2019 Association for Computing Machinery.