Summary of Efficient Annotator Reliability Assessment and Sample Weighting For Knowledge-based Misinformation Detection on Social Media, by Owen Cook et al.
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media
by Owen Cook, Charlie Grimshaw, Ben Wu, Sophie Dillon, Jack Hicks, Luke Jones, Thomas Smith, Matyas Szert, Xingyi Song
First submitted to arxiv on: 18 Oct 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 This study tackles the pressing issue of misinformation on social media, which can have devastating consequences for individuals and society as a whole. To combat this problem effectively, it is crucial to develop accurate methods for detecting misinformation before implementing mitigation strategies. The authors propose a knowledge-based approach to misinformation detection, drawing parallels with natural language inference models. They introduce the EffiARA annotation framework, which leverages inter- and intra-annotator agreement to gauge the reliability of each annotator and train large language models accordingly. To evaluate this framework, the researchers created the Russo-Ukrainian Conflict Knowledge-Based Misinformation Classification Dataset (RUC-MCD) and made it publicly available. The study finds that using sample weighting based on annotator reliability yields the best results, combining both inter- and intra-annotator agreement with soft-label training. Notably, the highest classification performance achieved using Llama-3.2-1B and TwHIN-BERT-large models was a macro-F1 of 0.757 and 0.740, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is trying to stop misinformation on social media from spreading. Right now, it’s hard to detect when someone is sharing fake news, so the researchers came up with a new way to do it using computer models that understand language. They created a special system called EffiARA to help these models learn what is and isn’t true. The team made a big dataset of examples to test this system and found that it works best when they use information from multiple people who agree on what’s real or not. This could be really important for keeping us safe online. |
Keywords
» Artificial intelligence » Bert » Classification » Inference » Llama
