Evaluating vulnerability to fake news in social networks: A community health assessment model
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 Assessme...
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sg-smu-ink.sis_research-55592021-09-09T02:49:48Z Evaluating vulnerability to fake news in social networks: A community health assessment model RATH, Bhavtosh GAO, Wei SRIVASTAVA, Jaideep 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. 2019-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4556 info:doi/10.1145/3341161.3342920 https://ink.library.smu.edu.sg/context/sis_research/article/5559/viewcontent/3341161.3342920.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Social media fake news community health detection algorithms Databases and Information Systems Social Media |
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Social media fake news community health detection algorithms Databases and Information Systems Social Media RATH, Bhavtosh GAO, Wei SRIVASTAVA, Jaideep Evaluating vulnerability to fake news in social networks: A community health assessment model |
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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. |
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text |
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RATH, Bhavtosh GAO, Wei SRIVASTAVA, Jaideep |
author_facet |
RATH, Bhavtosh GAO, Wei SRIVASTAVA, Jaideep |
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RATH, Bhavtosh |
title |
Evaluating vulnerability to fake news in social networks: A community health assessment model |
title_short |
Evaluating vulnerability to fake news in social networks: A community health assessment model |
title_full |
Evaluating vulnerability to fake news in social networks: A community health assessment model |
title_fullStr |
Evaluating vulnerability to fake news in social networks: A community health assessment model |
title_full_unstemmed |
Evaluating vulnerability to fake news in social networks: A community health assessment model |
title_sort |
evaluating vulnerability to fake news in social networks: a community health assessment model |
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Institutional Knowledge at Singapore Management University |
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2019 |
url |
https://ink.library.smu.edu.sg/sis_research/4556 https://ink.library.smu.edu.sg/context/sis_research/article/5559/viewcontent/3341161.3342920.pdf |
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1770574886691930112 |