SenTopX: Benchmark for User Sentiment on Various Topics
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913377880637440 |
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| author | Qayyum, Hina Ikram, Muhammad Zhao, Benjamin Wood, Ian Kaafar, Mohamad Ali Kourtellis, Nicolas |
| author_facet | Qayyum, Hina Ikram, Muhammad Zhao, Benjamin Wood, Ian Kaafar, Mohamad Ali Kourtellis, Nicolas |
| contents | Toxic sentiment analysis on Twitter (X) often focuses on specific topics and events such as politics and elections. Datasets of toxic users in such research are typically gathered through lexicon-based techniques, providing only a cross-sectional view. his approach has a tight confine for studying toxic user behavior and effective platform moderation. To identify users consistently spreading toxicity, a longitudinal analysis of their tweets is essential. However, such datasets currently do not exist.
This study addresses this gap by collecting a longitudinal dataset from 143K Twitter users, covering the period from 2007 to 2021, amounting to a total of 293 million tweets. Using topic modeling, we extract all topics discussed by each user and categorize users into eight groups based on the predominant topic in their timelines. We then analyze the sentiments of each group using 16 toxic scores. Our research demonstrates that examining users longitudinally reveals a distinct perspective on their comprehensive personality traits and their overall impact on the platform. Our comprehensive dataset is accessible to researchers for additional analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02801 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SenTopX: Benchmark for User Sentiment on Various Topics Qayyum, Hina Ikram, Muhammad Zhao, Benjamin Wood, Ian Kaafar, Mohamad Ali Kourtellis, Nicolas Social and Information Networks Toxic sentiment analysis on Twitter (X) often focuses on specific topics and events such as politics and elections. Datasets of toxic users in such research are typically gathered through lexicon-based techniques, providing only a cross-sectional view. his approach has a tight confine for studying toxic user behavior and effective platform moderation. To identify users consistently spreading toxicity, a longitudinal analysis of their tweets is essential. However, such datasets currently do not exist. This study addresses this gap by collecting a longitudinal dataset from 143K Twitter users, covering the period from 2007 to 2021, amounting to a total of 293 million tweets. Using topic modeling, we extract all topics discussed by each user and categorize users into eight groups based on the predominant topic in their timelines. We then analyze the sentiments of each group using 16 toxic scores. Our research demonstrates that examining users longitudinally reveals a distinct perspective on their comprehensive personality traits and their overall impact on the platform. Our comprehensive dataset is accessible to researchers for additional analysis. |
| title | SenTopX: Benchmark for User Sentiment on Various Topics |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2406.02801 |