Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
Shivam Sharma, Atharva Kulkarni, Tharun Suresh, Himanshi Mathur,, Preslav Nakov, Md. Shad Akhtar, Tanmoy Chakraborty

TL;DR
This paper introduces a new multi-modal framework, VECTOR, for identifying roles of entities in harmful memes, improving role detection accuracy and addressing complex semantic labeling challenges.
Contribution
The paper presents VECTOR, a novel multi-modal model that effectively detects entity roles in memes, advancing meme understanding and harmful content analysis.
Findings
VECTOR outperforms baseline models by 4% in role detection accuracy.
The model demonstrates robustness across US Politics and Covid-19 meme datasets.
Challenges in semantic role labeling within memes are discussed in detail.
Abstract
Memes can sway people's opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the 'hero', the 'villain', and the 'victim' in the meme, if any. We utilize HVVMemes - a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their…
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Taxonomy
TopicsHumor Studies and Applications · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining
