Pandya, M., Jin, M., Bontcheva, K. orcid.org/0000-0001-6152-9600 et al. (1 more author) (Submitted: 2024) Hostility detection in UK politics: A dataset on online abuse targeting MPs. [Preprint] (Submitted)
Abstract
Numerous politicians use social media platforms, particularly X, to engage with their constituents. This interaction allows constituents to pose questions and offer feedback but also exposes politicians to a barrage of hostile responses, especially given the anonymity afforded by social media. They are typically targeted in relation to their governmental role, but the comments also tend to attack their personal identity. This can discredit politicians and reduce public trust in the government. It can also incite anger and disrespect, leading to offline harm and violence. While numerous models exist for detecting hostility in general, they lack the specificity required for political contexts. Furthermore, addressing hostility towards politicians demands tailored approaches due to the distinct language and issues inherent to each country (e.g., Brexit for the UK). To bridge this gap, we construct a dataset of 3,320 English tweets spanning a two-year period manually annotated for hostility towards UK MPs. Our dataset also captures the targeted identity characteristics (race, gender, religion, none) in hostile tweets. We perform linguistic and topical analyses to delve into the unique content of the UK political data. Finally, we evaluate the performance of pre-trained language models and large language models on binary hostility detection and multi-class targeted identity type classification tasks. Our study offers valuable data and insights for future research on the prevalence and nature of politics-related hostility specific to the UK.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). This preprint is made available under a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ECONOMIC & SOCIAL RESEARCH COUNCIL ES/T012714/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Feb 2025 09:55 |
Last Modified: | 14 Feb 2025 10:16 |
Status: | Submitted |
Identification Number: | 10.48550/arXiv.2412.04046 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223238 |