Summary of Detecting Fake News on Social Media: a Novel Reliability Aware Machine-crowd Hybrid Intelligence-based Method, by Yidong Chai et al.
Detecting Fake News on Social Media: A Novel Reliability Aware Machine-Crowd Hybrid Intelligence-Based Method
by Yidong Chai, Kangwei Shi, Jiaheng Xie, Chunli Liu, Yuanchun Jiang, Yezheng Liu
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed Reliability Aware Hybrid Intelligence (RAHI) method addresses the reliability issue in fake news detection on social media platforms. The RAHI method comprises three modules: a Bayesian deep learning model to capture machine intelligence’s reliability, an Item Response Theory-based user response aggregation for crowd intelligence, and a distribution fusion mechanism combining both inputs. Experimental results on the Weibo dataset demonstrate the advantages of this novel approach. This study contributes to the research field with a practical RAHI-based method that has implications for internet users, online platform managers, and governments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news on social media is a big problem because it can hurt people’s trust in what they see online. To stop fake news from spreading, we need better ways to detect it. Right now, there are different methods to find fake news, but none of them take into account how reliable the information is. So, researchers came up with a new way called Reliability Aware Hybrid Intelligence (RAHI). RAHI uses three parts: a special kind of computer model, another part that looks at what people think, and a third part that combines these two to give an answer with its reliability level. This new method was tested on real data and showed it works better than the old methods. This is important because fake news can be very harmful, so we need ways to stop it from spreading. |
Keywords
» Artificial intelligence » Deep learning
