mlsanityData to 7 Oct 2026

Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study

arXiv 2301.05863 ↗First posted 14 Jan 2023cs.CL cs.AI cs.IRcited by 0

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Abstract (arXiv)

A myriad of studies addressed the problem of rumor verification in Twitter by either utilizing evidence from the propagation networks or external evidence from the Web. However, none of these studies exploited evidence from trusted authorities. In this paper, we define the task of detecting the stance of authorities towards rumors in tweets, i.e., whether a tweet from an authority agrees, disagrees, or is unrelated to the rumor. We believe the task is useful to augment the sources of evidence utilized by existing rumor verification systems. We construct and release the first Authority STance towards Rumors (AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic Twitter. Due to the relatively limited size of our dataset, we study the usefulness of existing datasets for stance detection in our task. We show that existing datasets are somewhat useful for the task; however, they are clearly insufficient, which motivates the need to augment them with annotated data constituting stance of authorities from Twitter.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 14 Jan 2023.

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References · 14

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  2. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359
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  5. 2021The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Modelscited by 118
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  9. 2020ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detectioncited by 15
  10. 2019Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detectioncited by 11
  11. 2021AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checkingcited by 8
  12. 2022PHEMEPlus: Enriching Social Media Rumour Verification with External Evidencecited by 6
2 more
  1. 2022ExaASC: A General Target-Based Stance Detection Corpus in Arabic Languagecited by 5
  2. 2021Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twittercited by 1

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