Summary of Socialnlp Fake-emoreact 2021 Challenge Overview: Predicting Fake Tweets From Their Replies and Gifs, by Chien-kun Huang et al.
SocialNLP Fake-EmoReact 2021 Challenge Overview: Predicting Fake Tweets from Their Replies and GIFs
by Chien-Kun Huang, Yi-Ting Chang, Lun-Wei Ku, Cheng-Te Li, Hong-Han Shuai
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Fake-EmoReact 2021 Challenge is a machine learning competition that involves predicting the authenticity of tweets using reply context and augmented GIF categories from the EmotionGIF dataset. The challenge provided a large-scale dataset, Fake-EmoReact, with over 453k labeled tweets for evaluation purposes. Twenty-four teams participated in the challenge, and five teams successfully submitted their results. The winning team achieved an F1 score of 93.9 on the Fake-EmoReact 2021 dataset using a specific approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This competition challenges machine learning models to identify fake tweets from real ones by analyzing reply context and augmented GIF categories. The goal is to develop a model that can accurately distinguish between authentic and inauthentic tweets. By participating in this challenge, teams can improve their skills in natural language processing and social media analysis. |
Keywords
» Artificial intelligence » F1 score » Machine learning » Natural language processing
