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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.04368 |
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| _version_ | 1866917686832791552 |
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| author | Huang, Chien-Kun Chang, Yi-Ting Ku, Lun-Wei Li, Cheng-Te Shuai, Hong-Han |
| author_facet | Huang, Chien-Kun Chang, Yi-Ting Ku, Lun-Wei Li, Cheng-Te Shuai, Hong-Han |
| contents | This paper provides an overview of the Fake-EmoReact 2021 Challenge, held at the 9th SocialNLP Workshop, in conjunction with NAACL 2021. The challenge requires predicting the authenticity of tweets using reply context and augmented GIF categories from EmotionGIF dataset. We offer the Fake-EmoReact dataset with more than 453k as the experimental materials, where every tweet is labeled with authenticity. Twenty-four teams registered to participate in this challenge, and 5 submitted their results successfully in the evaluation phase. The best team achieves 93.9 on Fake-EmoReact 2021 dataset using F1 score. In addition, we show the definition of share task, data collection, and the teams' performance that joined this challenge and their approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04368 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SocialNLP Fake-EmoReact 2021 Challenge Overview: Predicting Fake Tweets from Their Replies and GIFs Huang, Chien-Kun Chang, Yi-Ting Ku, Lun-Wei Li, Cheng-Te Shuai, Hong-Han Computation and Language Artificial Intelligence Computers and Society This paper provides an overview of the Fake-EmoReact 2021 Challenge, held at the 9th SocialNLP Workshop, in conjunction with NAACL 2021. The challenge requires predicting the authenticity of tweets using reply context and augmented GIF categories from EmotionGIF dataset. We offer the Fake-EmoReact dataset with more than 453k as the experimental materials, where every tweet is labeled with authenticity. Twenty-four teams registered to participate in this challenge, and 5 submitted their results successfully in the evaluation phase. The best team achieves 93.9 on Fake-EmoReact 2021 dataset using F1 score. In addition, we show the definition of share task, data collection, and the teams' performance that joined this challenge and their approaches. |
| title | SocialNLP Fake-EmoReact 2021 Challenge Overview: Predicting Fake Tweets from Their Replies and GIFs |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2406.04368 |