Hateful Signals and where to find them? -Sarah Masud, Phd Student @IIIT-Delhi 

Disclaimer Subsequent content may contain extreme language (verbatim from social media), which does not reflect the opinions of myself or my collaborators. Reader’s discretion is advised. 

Workflow for Analysing and Mitigating Online Hate Speech [1]: Tanmoy and Sarah, Nipping in the bud: detection, diffusion and mitigation of hate speech on social media, ACM SIGWEB Winter, Invited Publication Fig 1: The various input signals (red), models (green) and user groups (blue) involved in analysing hate speech. [1] 

Why Context is important for Hate Speech? ● No clear definition of hate speech. ● Subjective annotation from the point of view of NLP modeling. ● No standard list of vulnerable groups. Endogenous Signals Exogenous Signals 

Exogenous Signals: News articles & Topical Affinity Fig 1: Hatefulness of different users towards different hashtags/topics in RETINA [1] Fig 2: Exogenous attention Model RETINA [1] XN: News Headline XT: Incoming Tweet [1]: Masud et al., Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter, ICDE 2021 

Fig 1: Motivation for Auxiliary Data Signals in Hate Speech Detection [1] Endogenous Signals: User’s interaction on platform [1]: Kulkarni et al., Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment, KDD 2023 

More about our work: https://sara-02.github.io/publications/ _themessier on Twitter lcs2lab on Twitter