Summary of Why Misinformation Is Created? Detecting Them by Integrating Intent Features, By Bing Wang et al.
Why Misinformation is Created? Detecting them by Integrating Intent Features
by Bing Wang, Ximing Li, Changchun Li, Bo Fu, Songwen Pei, Shengsheng Wang
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 This paper proposes a novel approach to detect misinformation on social media platforms by reasoning about the intent behind an article. Inspired by the opposition between misinformation and real information, the authors create a hierarchy of intents for both categories based on psychological theories. They use an encoder-decoder structure to progressively generate binary answers and form corresponding intent features. These features are then integrated with token features to achieve more discriminative article features for Misinformation Detection (MD). The proposed method, DM-INTER, is evaluated on benchmark MD datasets, showing improved performance compared to existing baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us detect false information on social media by understanding the purpose behind an article. It’s like trying to figure out why someone wrote something, and then using that information to decide if it’s true or not. The authors use special theories from psychology to create a list of intentions for both real and fake news. They then use computers to make decisions based on these intentions. This new method is better at detecting false information than old methods. |
Keywords
» Artificial intelligence » Encoder decoder » Token
