@inproceedings{anastasi-etal-2024-vida,
title = "{VIDA}: The Visual Incel Data Archive. A Theory-oriented Annotated Dataset To Enhance Hate Detection Through Visual Culture",
author = "Anastasi, Selenia and
Schneider, Florian and
Biemann, Chris and
Fischer, Tim",
editor = {Chung, Yi-Ling and
Talat, Zeerak and
Nozza, Debora and
Plaza-del-Arco, Flor Miriam and
R{\"o}ttger, Paul and
Mostafazadeh Davani, Aida and
Calabrese, Agostina},
booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.woah-1.6",
doi = "10.18653/v1/2024.woah-1.6",
pages = "59--67",
abstract = "Images increasingly constitute a larger portion of internet content, encoding even more complex meanings. Recent studies have highlight the pivotal role of visual communication in the spread of extremist content, particularly that associated with right-wing political ideologies. However, the capability of machine learning systems to recognize such meanings, sometimes implicit, remains limited. To enable future research in this area, we introduce and release VIDA, the Visual Incel Data Archive, a multimodal dataset comprising visual material and internet memes collected from two main Incel communities (Italian and Anglophone) known for their extremist misogynistic content. Following the analytical framework of Shifman (2014), we propose a new taxonomy for annotation across three main levels of analysis: content, form, and stance (hate). This allows for the association of images with fine-grained contextual information that help to identify the presence of offensiveness and a broader set of cultural references, enhancing the understanding of more nuanced aspects in visual communication. In this work we present a statistical analysis of the annotated dataset as well as discuss annotation examples and future line of research.",
}
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%0 Conference Proceedings
%T VIDA: The Visual Incel Data Archive. A Theory-oriented Annotated Dataset To Enhance Hate Detection Through Visual Culture
%A Anastasi, Selenia
%A Schneider, Florian
%A Biemann, Chris
%A Fischer, Tim
%Y Chung, Yi-Ling
%Y Talat, Zeerak
%Y Nozza, Debora
%Y Plaza-del-Arco, Flor Miriam
%Y Röttger, Paul
%Y Mostafazadeh Davani, Aida
%Y Calabrese, Agostina
%S Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F anastasi-etal-2024-vida
%X Images increasingly constitute a larger portion of internet content, encoding even more complex meanings. Recent studies have highlight the pivotal role of visual communication in the spread of extremist content, particularly that associated with right-wing political ideologies. However, the capability of machine learning systems to recognize such meanings, sometimes implicit, remains limited. To enable future research in this area, we introduce and release VIDA, the Visual Incel Data Archive, a multimodal dataset comprising visual material and internet memes collected from two main Incel communities (Italian and Anglophone) known for their extremist misogynistic content. Following the analytical framework of Shifman (2014), we propose a new taxonomy for annotation across three main levels of analysis: content, form, and stance (hate). This allows for the association of images with fine-grained contextual information that help to identify the presence of offensiveness and a broader set of cultural references, enhancing the understanding of more nuanced aspects in visual communication. In this work we present a statistical analysis of the annotated dataset as well as discuss annotation examples and future line of research.
%R 10.18653/v1/2024.woah-1.6
%U https://aclanthology.org/2024.woah-1.6
%U https://doi.org/10.18653/v1/2024.woah-1.6
%P 59-67
Markdown (Informal)
[VIDA: The Visual Incel Data Archive. A Theory-oriented Annotated Dataset To Enhance Hate Detection Through Visual Culture](https://aclanthology.org/2024.woah-1.6) (Anastasi et al., WOAH-WS 2024)
ACL