Against the Others! Detecting Moral Outrage inSocial Media Networks
Wienke Strathern, Mirco Schoenfeld, Raji Ghawi, Juergen Pfeffer

TL;DR
This paper presents a method to detect the onset of moral outrage firestorms on Twitter by analyzing linguistic cues, enabling early identification of negative social media dynamics to help mitigate hate speech.
Contribution
It introduces a systematic approach to detect social media firestorms early using linguistic cues, advancing the understanding of online moral outrage dynamics.
Findings
Early detection of firestorms is possible with linguistic analysis.
Linguistic cues are effective indicators of moral outrage outbreaks.
The method can help mitigate hate speech on social media.
Abstract
Online firestorms on Twitter are seemingly arbitrarily occurring outrages towards people, companies, media campaigns and politicians. Moral outrages can create an excessive collective aggressiveness against one single argument, one single word, or one action of a person resulting in hateful speech. With a collective "against the others" the negative dynamics often start. Using data from Twitter, we explored the starting points of several firestorm outbreaks. As a social media platform with hundreds of millions of users interacting in real-time on topics and events all over the world, Twitter serves as a social sensor for online discussions and is known for quick and often emotional disputes. The main question we pose in this article, is whether we can detect the outbreak of a firestorm. Given 21 online firestorms on Twitter, the key questions regarding the anomaly detection are: 1) How…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHate Speech and Cyberbullying Detection · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining
