mlsanityData to 7 Oct 2026

Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations

arXiv 2205.04402 ↗First posted 9 May 2022cs.CL cs.CV cs.MMcited by 2

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Abstract (arXiv)

Harmful or abusive online content has been increasing over time, raising concerns for social media platforms, government agencies, and policymakers. Such harmful or abusive content can have major negative impact on society, e.g., cyberbullying can lead to suicides, rumors about COVID-19 can cause vaccine hesitance, promotion of fake cures for COVID-19 can cause health harms and deaths. The content that is posted and shared online can be textual, visual, or a combination of both, e.g., in a meme. Here, we describe our experiments in detecting the roles of the entities (hero, villain, victim) in harmful memes, which is part of the CONSTRAINT-2022 shared task, as well as our system for the task. We further provide a comparative analysis of different experimental settings (i.e., unimodal, multimodal, attention, and augmentation). For reproducibility, we make our experimental code publicly available. urlhttps://github.com/robi56/harmfulmemesblockfusion

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 82 references that are in the map. First posted on arXiv 9 May 2022.

Builds on

34 of 82 references.

References · 34

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22 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?cited by 14
  2. 2024Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicitiescited by 13