Automatic Misogyny Detection in Social Media Platforms using Attention-based Bidirectional-LSTM

Rahali, Abir; Akhloufi, Moulay A.; Therien-Daniel, Anne-Marie; Brassard-Gourdeau, Éloi

doi:10.1109/smc52423.2021.9659158

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Many works related to automatic detection of misogyny, hate, sexism on social media and web have been proposed. Abir Rahali (Rahali et al, 2021) proposed a approach for automatic misogyny detection in social media using attention based bidirectional LSTM. Endang Wahyu Pamungkas (Pamungkas et al, 2020) proposed a method for Automatic Identification of Misogyny in English and Italian Tweets at EVALITA 2018 with a Multilingual Hate Lexicon.…”

Section: Related Workmentioning

confidence: 99%

IIT DHANBAD CODECHAMPS at SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification

Barnwal¹,

Kumar²,

Pamula³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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With the growth of the internet, the use of social media based on images has drastically increased like Twitter, Instagram, etc. In these social media, women have a very high contribution as of 75% women use social media multiple times compared to men which is only 65% of men uses social media multiple times a day. However, with this much contribution, it also increases systematic inequality and discrimination offline is replicated in online spaces in the form of MEMEs. A meme is essentially an image characterized by pictorial content with an overlaying text a posteriori introduced by humans, with the main goal of being funny and/or ironic. Although most of them are created with the intent of making funny jokes, in a short time people started to use them as a form of hate and prejudice against women, landing to sexist and aggressive messages in online environments that subsequently amplify the sexual stereotyping and gender inequality of the offline world. This leads to the need for automatic detection of Misogyny MEMEs. Specifically, I described the model submitted for the shared task on Multimedia Automatic Misogyny Identification (MAMI (Fersini et al., 2022)) and my team name is IIT DHANBAD CODECHAMPS.

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Section: Related Workmentioning

confidence: 99%