Summary of Hoaxpedia: a Unified Wikipedia Hoax Articles Dataset, by Hsuvas Borkakoty and Luis Espinosa-anke
Hoaxpedia: A Unified Wikipedia Hoax Articles Dataset
by Hsuvas Borkakoty, Luis Espinosa-Anke
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research paper proposes an innovative approach to detecting hoaxes on Wikipedia by analyzing similarities and discrepancies between legitimate and fake articles. The authors introduce Hoaxpedia, a dataset comprising 311 hoax articles paired with semantically similar legitimate articles. They then employ several language models and text classifiers to identify deceitful content based on article content alone. The results suggest that while detection is challenging, it is feasible with the right approach. Additionally, the study highlights the importance of considering edit histories in identifying hoaxes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to spot fake information on Wikipedia. It’s hard to detect because hoax articles are written in a similar style to real ones. The researchers created a special dataset called Hoaxpedia with 311 fake articles and their matching real counterparts. They used different methods to analyze the text and found that while it’s tricky, we can still identify deceitful content by looking at things like the article’s definition or edit history. |
