Abstract
Social media has been leveraged by many groups to share their ideas, ideology, and other messages. Some of these posts promote extremist ideology. In this paper, we propose an approach for identifying users who engage in extremist discussions online. Our approach uses detailed feature selection to identify relevant posts and then uses a novel weighted network that models the information flow between the publishers of the relevant posts. An empirical evaluation of a post collection crawled from a web forum containing racially driven discussions and a tweet stream discussing the ISIS extremist group show that our proposed method for relevant post identification is significantly better than the state of the art and using a network flow graph for user identification leads to very accurate user identification.
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Notes
- 1.
vaderSentiment is also the tool employed by [18] to identify extremist users.
References
Stanford corenlp sentiment analyzer. http://stanfordnlp.github.io/CoreNLP/sentiment.html
Stromfront. https://www.stormfront.org/forum/
Vader sentiment analysis. https://github.com/cjhutto/vaderSentiment
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Acknowledgments
We thank subject matter experts for labeling data and their general subject matter expertise provided throughout the process. This work was supported in part by the National Science Foundation (NSF) Grant SMA-1338507 and the Georgetown University Mass Data Institute (MDI).
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Wei, Y., Singh, L. (2017). Using Network Flows to Identify Users Sharing Extremist Content on Social Media. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_26
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DOI: https://doi.org/10.1007/978-3-319-57454-7_26
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