Explainable Abusive Language Classification Leveraging User and Network Data

Wich, Maximilian; Mosca, E.; Gorniak, Adrian; Hingerl, Johannes; Groh, Georg

doi:10.1007/978-3-030-86517-7_30

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…As part of their study, they used a classification model based on hatebase.org to detect messages with hateful language (Rogers 2020). Previous studies on the platform Twitter have shown that identifying networks and user context for social media have significant beneficial impact on classification tasks, such as hate speech detection (Mosca, Wich, and Groh 2021;Wich et al 2021b) and motivate further in-depth studies on these communities on other platforms. Urman and Katz (2020) conducted an in-depth network analysis of a far-right community on Telegram.…”

Section: Related Workmentioning

confidence: 99%

Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community

Wich

Gorniak

Eder

et al. 2022

ICWSM

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Because traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate user content to the same degree. One popular platform relevant for the German community is Telegram for which limited research efforts have been made so far. This study aimed to develop a broad framework comprising (i) an abusive language classification model for German Telegram messages and (ii) a classification model for the hatefulness of Telegram channels. For the first part, we use existing abusive language datasets containing posts from other platforms to develop our classification models. For the channel classification model, we develop a method that combines channel-specific content information collected from a topic model with a social graph to predict the hatefulness of channels. Furthermore, we complement these two approaches for hate speech detection with insightful results on the evolution of German speaking communities focused on hateful content on the Telegram platform. We also propose methods for conducting scalable network analyses for social media platforms to the hate speech research community. As an additional output of this study, we provide an annotated abusive language dataset containing 1,149 annotated Telegram messages.

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