@inproceedings{fharook-etal-2022-hero,
title = "Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.",
author = "Fharook, Shaik and
Sufyan Ahmed, Syed and
Rithika, Gurram and
Budde, Sumith Sai and
Saumya, Sunil and
Biradar, Shankar",
editor = "Chakraborty, Tanmoy and
Akhtar, Md. Shad and
Shu, Kai and
Bernard, H. Russell and
Liakata, Maria and
Nakov, Preslav and
Srivastava, Aseem",
booktitle = "Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.constraint-1.3/",
doi = "10.18653/v1/2022.constraint-1.3",
pages = "19--23",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.
%A Fharook, Shaik
%A Sufyan Ahmed, Syed
%A Rithika, Gurram
%A Budde, Sumith Sai
%A Saumya, Sunil
%A Biradar, Shankar
%Y Chakraborty, Tanmoy
%Y Akhtar, Md. Shad
%Y Shu, Kai
%Y Bernard, H. Russell
%Y Liakata, Maria
%Y Nakov, Preslav
%Y Srivastava, Aseem
%S Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F fharook-etal-2022-hero
%X 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.
%R 10.18653/v1/2022.constraint-1.3
%U https://aclanthology.org/2022.constraint-1.3/
%U https://doi.org/10.18653/v1/2022.constraint-1.3
%P 19-23
Markdown (Informal)
[Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.](https://aclanthology.org/2022.constraint-1.3/) (Fharook et al., CONSTRAINT 2022)
ACL