Summary of Heterogeneous Subgraph Transformer For Fake News Detection, by Yuchen Zhang et al.
Heterogeneous Subgraph Transformer for Fake News Detection
by Yuchen Zhang, Xiaoxiao Ma, Jia Wu, Jian Yang, Hao Fan
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach for detecting fake news on social media is presented in this paper, which constructs a heterogeneous graph to capture the relationships between news topics, entities, and content. The study reveals that fake news can be identified by analyzing atypical subgraphs centered around them, which contain essential semantics and intricate relations between news elements. However, exploring these subgraphs remains an open problem due to their heterogeneity. To address this challenge, a heterogeneous subgraph transformer (HeteroSGT) is proposed, which leverages pre-trained language models to derive word-level and sentence-level semantics, followed by random walk with restart (RWR) to extract subgraphs centered on each news piece, and finally uses a subgraph Transformer to quantify authenticity. Experimental results on five real-world datasets demonstrate the superior performance of HeteroSGT over five baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news is a big problem on social media that can cause harm to people and society. This paper tries to find a way to detect fake news by looking at how different pieces of news are related to each other. They created a special kind of graph called a heterogeneous graph, which shows how news topics, entities, and content are connected. The researchers found that fake news is often surrounded by unusual patterns in this graph, which can be used to identify it. However, finding these patterns is difficult because the graphs are very different from one another. To solve this problem, they developed a new tool called HeteroSGT, which uses special algorithms to analyze the subgraphs and determine if the news is real or fake. They tested their tool on five real-world datasets and found that it performed better than other methods. |
Keywords
» Artificial intelligence » Semantics » Transformer
