Characterization of Political Polarized Users Attacked by Language Toxicity on Twitter
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916567749492736 |
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| author | Xu, Wentao |
| author_facet | Xu, Wentao |
| contents | Understanding the dynamics of language toxicity on social media is important for us to investigate the propagation of misinformation and the development of echo chambers for political scenarios such as U.S. presidential elections. Recent research has used large-scale data to investigate the dynamics across social media platforms. However, research on the toxicity dynamics is not enough. This study aims to provide a first exploration of the potential language toxicity flow among Left, Right and Center users. Specifically, we aim to examine whether Left users were easier to be attacked by language toxicity. In this study, more than 500M Twitter posts were examined. It was discovered that Left users received much more toxic replies than Right and Center users. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_12471 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Characterization of Political Polarized Users Attacked by Language Toxicity on Twitter Xu, Wentao Computers and Society Artificial Intelligence Computation and Language 91, 94 J.4 Understanding the dynamics of language toxicity on social media is important for us to investigate the propagation of misinformation and the development of echo chambers for political scenarios such as U.S. presidential elections. Recent research has used large-scale data to investigate the dynamics across social media platforms. However, research on the toxicity dynamics is not enough. This study aims to provide a first exploration of the potential language toxicity flow among Left, Right and Center users. Specifically, we aim to examine whether Left users were easier to be attacked by language toxicity. In this study, more than 500M Twitter posts were examined. It was discovered that Left users received much more toxic replies than Right and Center users. |
| title | Characterization of Political Polarized Users Attacked by Language Toxicity on Twitter |
| topic | Computers and Society Artificial Intelligence Computation and Language 91, 94 J.4 |
| url | https://arxiv.org/abs/2407.12471 |