Summary of A Multi-label Dataset Of French Fake News: Human and Machine Insights, by Benjamin Icard et al.
A Multi-Label Dataset of French Fake News: Human and Machine Insights
by Benjamin Icard, François Maine, Morgane Casanova, Géraud Faye, Julien Chanson, Guillaume Gadek, Ghislain Atemezing, François Bancilhon, Paul Égré
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 presents a corpus of 100 documents, OBSINFOX, annotated with 11 labels by 8 annotators. The collection is unique in that it features more labels and annotators than typical datasets, allowing for the identification of characteristics considered indicative of fake news by humans. The study also analyzes the topic and genre distribution using Gate Cloud, revealing a prevalence of satire-like text. Additionally, the paper employs subjectivity analyzers VAGO and its neural counterpart to investigate the link between subjective and fake news labels. The annotated dataset is publicly available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a big database of news articles that are considered unreliable by experts. They added extra labels and had many people help with annotating the data, which helps identify what makes fake news seem like real news. They also looked at what topics and types of writing are most common in this dataset. By using special tools to analyze how humans label things as subjective or not, they found connections between these labels and fake news. |
