Summary of Using Llms to Discover Emerging Coded Antisemitic Hate-speech in Extremist Social Media, by Dhanush Kikkisetti et al.
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media
by Dhanush Kikkisetti, Raza Ul Mustafa, Wendy Melillo, Roberto Corizzo, Zois Boukouvalas, Jeff Gill, Nathalie Japkowicz
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology detects emerging coded hate-laden terminology in online antisemitic discourse, leveraging large language models and semantic similarity analysis. By identifying representative expressions, filtering out grammatical inconsistencies and previously encountered terms, the approach focuses on novel well-formed terminology. This is then compared to known antisemitic expressions using a fine-tuned model, allowing for the removal of coded expressions containing Jewish-related topics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps social media platforms tackle online hate speech by detecting coded language used by extremist groups. It’s like finding hidden clues in a puzzle! The method uses big language models and checks if words are similar to known hate speech. By removing these coded messages, we can better understand and combat online antisemitic discourse. |
Keywords
* Artificial intelligence * Discourse
