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A computational approach to analyzing and detecting trans-exclusionary radical feminists (TERFs) on Twitter
Christina T. Lu
Dartmouth TR2020-900

Abstract:

Within the realm of abusive content detection for social media, little research has been conducted on the transphobic hate group known as trans-exclusionary radical feminists (TERFs). The community engages in harmful behaviors such as targeted harassment of transgender people on Twitter, and perpetuates transphobic rhetoric such as denial of trans existence under the guise of feminism. This thesis analyzes the network of the TERF community on Twitter, by discovering several sub-communities as well as modeling the topics of their tweets. We also introduce TERFSPOT, a classifier for predicting whether a Twitter user is a TERF or not, based on a combination of network and textual features. The contributions of this work are twofold: we conduct the first large-scale computational analysis of the TERF hate group on Twitter, and demonstrate a classifier with a 90% accuracy for identifying TERFs.

Note:

Senior Honors Thesis. Advisor: Saeed Hassanpour


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   Christina T. Lu, "A computational approach to analyzing and detecting trans-exclusionary radical feminists (TERFs) on Twitter." Dartmouth Computer Science Technical Report TR2020-900, June 2020.


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