Toward Multimodal Cyberbullying Detection

By Vivek K. Singh, Souvick Ghosh, Christin Jose. 2017

96 citations recorded by the source corpus.

As human beings utilize computing technologies to mediate multiple aspects of their lives, cyberbullying has grown as an important societal challenge. Cyberbullying may lead to deep psychiatric and emotional disorders for those affected. Hence, there is an urgent need to devise automated methods for cyberbullying detection and prevention. While recent cyberbullying detection efforts have defined sophisticated text processing methods for cyberbullying detection, there are as yet few efforts that leverage visual data processing to automatically detect cyberbullying. Based on early analysis of a public, labeled cyberbullying dataset, we report that visual features complement textual features in cyberbullying detection and can help improve predictive results.

Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “[72] conduct a detailed study, using the same dataset, of the types of features that matter for cyber-bullying detection in this task.”
Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021, background): “Indeed the community is aware of this form of abuse and there have been several attempts for multimodal analysis (Singh et al., 2017; Yang et al., 2019; Gomez et al., 2020).”
DeepHate: Hate Speech Detection via Multi-Faceted Text Representations (2020, methodology): “Beyond the textual content, some studies have also utilized other meta-information from the users’ profiles and network structures (i.e., followers, mentioned, etc.) [9, 31, 36].”
Exploring Visual Engagement Signals for Representation Learning (2021, background): “This area includes (1) detecting cyberbullying and hate speech [29, 71, 20, 40], (2) identifying emotions [43, 1, 59, 62, 81], (3) understanding rhetoric and intentions [36, 37, 70, 30, 31, 88, 74, 45, 34].”