'Substitution': Extremists' New Form of Implicit Hate Speech to Avoid Detection

RISIUS, MARTEN; NAMVAR, MORTEZA; AKHLAGHPOUR, SAEED; XIE, HETIAO (SLIM)

The following excerpt from the document contains multiple links embedded in the original text: "'Content Warning: This insight contains antisemitic, racist, and hateful imagery.' [...] Extremists exploit social media platforms to spread hate against minority groups based on protected attributes such as gender, religion, and ethnicity. Platforms and researchers have been actively developing AI tools to detect and remove such hate speech. However, extremists employ various forms of implicit hate speech (IHS) to evade AI detection systems. IHS spreads hateful messages using subtle expressions and complex contextual semantic relationships instead of explicit abusive words, bringing challenges to automatic detection algorithms. Common forms of IHS include dog whistles, coded language, humorous hate speech, and implicit dehumanisation. Moreover, the forms and expressions of IHS evolve rapidly with societal controversies (e.g., regional wars). Identifying and tracking such changes in IHS is crucial for platforms trying to counter them. In this Insight, we report and analyse 'Substitution' as a new form of IHS. Recently, we observed extremists using 'Substitution' by propagating hateful rhetoric against a target group (e.g., Jews) while explicitly referencing another label group (e.g., Chinese). We show that Substitution not only effectively spreads hate but also exacerbates engagement and obscures detection."

GLOBAL NETWORK ON EXTREMISM AND TECHNOLOGY (GNET). 24 JUN, 2024. 8p.