Summary of Taco — Twitter Arguments From Conversations, by Marc Feger and Stefan Dietze
TACO – Twitter Arguments from COnversations
by Marc Feger, Stefan Dietze
First submitted to arxiv on: 30 Mar 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 This paper presents a novel dataset called TACO for Twitter arguments, which aims to identify structural elements of online discourse. The dataset contains 1,814 tweets from 200 conversations across six topics, annotated by six experts with an agreement rate of 0.718 Krippendorff’s alpha. Additionally, the authors provide an annotation framework based on definitions from the Cambridge Dictionary to define and identify argument components on Twitter. A transformer-based classifier achieves a macro F1 score of 85.06% in detecting arguments. The study reveals that Twitter users tend to engage in discussions involving informed inferences and information, which has implications for tweet classification and conversational reply patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how people have conversations on Twitter. It creates a special set of tweets called TACO that can help machines learn about online arguments. The dataset includes 1,814 tweets from different topics and was checked by six experts to make sure the annotations were consistent. The study shows that people on Twitter like to discuss things using information and inferences. This research is important because it helps us understand how people communicate online. |
Keywords
» Artificial intelligence » Classification » Discourse » F1 score » Transformer
