Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations
Abstract
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. \url{https://github.com/robi56/harmful_memes_block_fusion}
Keywords
Cite
@article{arxiv.2205.04402,
title = {Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations},
author = {Rabindra Nath Nandi and Firoj Alam and Preslav Nakov},
journal= {arXiv preprint arXiv:2205.04402},
year = {2022}
}
Comments
Accepted at CONSTRAINT 2022 (Colocated with ACL-2022), disinformation, misinformation, factuality, harmfulness, fake news, propaganda, multimodality, text, images, videos, network structure, temporality