| Presentation | 2021-07-24 [Short Paper] COVID-19 and biased information dissemination on Twitter Gefei Li, Yijun Duan, Taehoon Kim, Kyoungsook Kim, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This study aims to examine if Twitter users with a higher level of similarity would have a more similar distribution of biased tweets. We randomly selected 100 users from an open-access COVID-19 Tweets dataset and fetched their Twitter timelines. We pre-trained the attention-based bidirectional long short-term memory (BiLSTM) model with a Media Bias Annotation Dataset and fine-tuned this model with 3,000 manually labelled Tweets concerning coronavirus-related topics. This model was used to classify users’ Tweets and returned the distribution of biased Tweets on their timelines. Users’ similarity was measured from two aspects: profile and text similarity. Ordinary Least Squares regression (OLS) correlation analysis suggests that both profile and text similarity are statistically significant in estimating the similarity of the distribution of biased tweets on users’ timelines. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | BiasTwitterSocial MediaProfile SimilarityText SimilarityNLP |
| Paper # | DE2021-4 |
| Date of Issue | 2021-07-17 (DE) |
| Conference Information | |
| Committee | DE |
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| Conference Date | 2021/7/24(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | KAIT IT extension center (Hybrid) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Social Computing |
| Chair | Naofumi Yoshida(Komazawa Univ.) |
| Vice Chair | Akiyoshi Matono(AIST) / Yu Suzuki(Gifu Univ.) |
| Secretary | Akiyoshi Matono(Kanagawa Inst. of Tech.) / Yu Suzuki(Osaka Univ.) |
| Assistant | Ken Honda(Komazawa Univ.) / Hiroki Nomiya(Kyoto Inst. of Tech) |
| Paper Information | |
| Registration To | Technical Committee on Data Engineering |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Short Paper] COVID-19 and biased information dissemination on Twitter |
| Sub Title (in English) | |
| Keyword(1) | BiasTwitterSocial MediaProfile SimilarityText SimilarityNLP |
| 1st Author's Name | Gefei Li |
| 1st Author's Affiliation | National Institute of Advanced Industrial Science and Technology/Waseda University(AIST/Waseda Univ.) |
| 2nd Author's Name | Yijun Duan |
| 2nd Author's Affiliation | National Institute of Advanced Industrial Science and Technology(AIST) |
| 3rd Author's Name | Taehoon Kim |
| 3rd Author's Affiliation | National Institute of Advanced Industrial Science and Technology(AIST) |
| 4th Author's Name | Kyoungsook Kim |
| 4th Author's Affiliation | National Institute of Advanced Industrial Science and Technology(AIST) |
| Date | 2021-07-24 |
| Paper # | DE2021-4 |
| Volume (vol) | vol.121 |
| Number (no) | DE-125 |
| Page | pp.pp.18-21(DE), |
| #Pages | 4 |
| Date of Issue | 2021-07-17 (DE) |