Against the Others! Detecting Moral Outrage inSocial Media Networks

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 can we detect the changing point? 2) How can we distinguish the features that cause a moral outrage? In this paper we examine these challenges developing a method to detect the point of change systematically spotting on linguistic cues of tweets. We are able to detect outbreaks of firestorms early and precisely only by applying linguistic cues. The results of our work can help detect negative dynamics and may have the potential for individuals, companies, and governments to mitigate hate in social media networks.