An Expert Annotated Dataset for the Detection of Online Misogyny

Guest, Ella; Vidgen, Bertie; Mittos, Alexandros; Sastry, Nishanth; Tyson, Gareth; Margetts, Helen

doi:10.18653/v1/2021.eacl-main.114

Cited by 47 publications

(35 citation statements)

References 32 publications

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“…entity tags, as additional inputs to the pre-trained models. Note, there have also been studies of misogyny in uni-modal platforms (Guest et al, 2021;Zeinert et al, 2021;Jiang et al, 2022). Our work differs from the above.…”

Section: Related Workmentioning

confidence: 81%

MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme Detection

Gu¹,

Castro²,

Tyson³

2022

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

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Memes have become quite common in day-today communications on social media platforms. They often appear to be amusing, evoking and attractive to audiences. However, some memes containing malicious content can be harmful to targeted groups. In this paper, we study misogynous meme detection, a shared task in SemEval 2022 -Multimedia Automatic Misogyny Identification (MAMI). The challenge of misogynous meme detection is to co-represent multi-modal features. To tackle with this challenge, we propose a Multi-modal Multi-task Variational Au-toEncoder (MMVAE) to learn an effective corepresentation of visual and textual features in the latent space. Our goal is to automatically determine if a meme contains misogynous information and then identify its fine-grained category. Our model achieves F 1 scores of 0.723 on the MAMI sub-task A and 0.634 on sub-task B. We carry out comprehensive experiments on our model's architecture and show that our approach significantly outperforms several strong uni-modal and multi-modal approaches. Our code is released on github 1 .

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Section: Related Workmentioning

confidence: 81%

MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme Detection

Gu¹,

Castro²,

Tyson³

2022

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

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“…Hate speech: Hate speech on online forums is a common [15] challenge, including in nationally important conversations between citizens and their elected representatives [1]. In other cases, some members of a discussion can be unfairly targeted, for example, misogyny is understood to be an important problem on online forums such as Reddit [23]. Inferring argument polarities at scale can help platforms to detect such problems before they spiral out of control (for example, highly attacking comments towards female participants can be a possible indicator of potential misogyny).…”

Section: Discussionmentioning

confidence: 99%

GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates

Agarwal,

Joglekar,

Young

et al. 2022

Preprint

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Online forums that allow participatory engagement between users have been transformative for public discussion of important issues. However, debates on such forums can sometimes escalate into full blown exchanges of hate or misinformation. An important tool in understanding and tackling such problems is to be able to infer the argumentative relation of whether a reply is supporting or attacking the post it is replying to. This so called polarity prediction task is difficult because replies may be based on external context beyond a post and the reply whose polarity is being predicted. We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walk techniques to capture the wider context of a discussion thread in a principled fashion. Specifically, we propose methods to perform root-seeking graph walks that start from a post and captures its surrounding context to generate additional embeddings for the post. We then use these embeddings to predict the polarity relation between a reply and the post it is replying to. We evaluate the performance of our models on a curated debate dataset from Kialo, an online debating platform. Our model outperforms relevant baselines, including S-BERT, with an overall accuracy of 83%.

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“…Homophobia and transphobia are not given much attention. Recently (Guest et al, 2021) created an expert annotated dataset for detecting online misogyny. We collected our dataset inspired by their work.…”

Section: Data Descriptionmentioning

confidence: 99%

Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments

Chakravarthi¹,

Priyadharshini²,

Thenmozhi³

et al. 2022

Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

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Homophobia and Transphobia Detection is the task of identifying homophobia, transphobia, and non-anti-LGBT+ content from the given corpus. Homophobia and transphobia are both toxic languages directed at LGBTQ+ individuals that are described as hate speech. This paper summarizes our findings on the "Homophobia and Transphobia Detection in social media comments" shared task held at LT-EDI 2022 -ACL 2022 1 . This shared task focused on three sub-tasks for Tamil, English, and Tamil-English (code-mixed) languages. It received 10 systems for Tamil, 13 systems for English, and 11 systems for Tamil-English. The best systems for Tamil, English, and Tamil-English scored 0.570, 0.870, and 0.610, respectively, on average macro F1-score.

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An Expert Annotated Dataset for the Detection of Online Misogyny

Cited by 47 publications

References 32 publications

MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme Detection

MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme Detection

GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates

Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments

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