Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.
Identifying good and evil through representations of victimhood, heroism, and villainy (i.e., role labeling of entities) has recently caught the research community’s interest. Because of the growing popularity of memes, the amount of offensive information published on the internet is expanding at an alarming rate. It generated a larger need to address this issue and analyze the memes for content moderation. Framing is used to show the entities engaged as heroes, villains, victims, or others so that readers may better anticipate and understand their attitudes and behaviors as characters. Positive phrases are used to characterize heroes, whereas negative terms depict victims and villains, and terms that tend to be neutral are mapped to others. In this paper, we propose two approaches to role label the entities of the meme as hero, villain, victim, or other through Named-Entity Recognition(NER), Sentiment Analysis, etc. With an F1-score of 23.855, our team secured eighth position in the Shared Task @ Constraint 2022.
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named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERSentiment AnalysisSimilar Papers 제목 키워드 기반
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