A multi-task attention tree neural net for stance classification and rumor veracity detection

Bai, Na; Meng, Fei; Rui, Xiaobin; Wang, Zhixiao

doi:10.1007/s10489-022-03833-5

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…A closer look to the literature on rumor verification in social media reveals that no study to date has examined exploiting evidence from authorities. Existing studies for rumor verification in social media exploited evidence from the propagation networks [8,9,13,14,16], Web [17], and stance of conversational threads [19,20,39].…”

Section: Authorities For Rumor Verificationmentioning

confidence: 99%

“…Those rumors may have a lasting effect on users' opinion even after it is debunked, and may continue influence them if not replaced with convincing evidence [3]. Existing studies for rumor verification in social media exploited the propagation networks as a source of evidence, where they focused on the stance of replies [4][5][6][7][8][9], structure of replies [10][11][12][13][14][15], and profile features of retweeters [16]. Recently, Dougrez-Lewis et al [17] proposed augmenting the propagation networks with evidence from the Web, and Hu et al [18] proposed exploiting both text and images retrieved from the web as sources of evidence.…”

Section: Introductionmentioning

confidence: 99%

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Are Authorities Denying or Supporting? Detecting Stance of Authorities Towards Rumors in Twitter

Haouari,

Elsayed

2023

Preprint

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Several studies examined the leverage of the stance in conversational threads or news articles as a signal for rumor verification. However, none of these studies leveraged the stance of trusted authorities. In this work, we define the task of detecting the stance of authorities towards rumors in Twitter, i.e., whether a tweet from an authority supports the rumor, denies it, or neither. We believe the task is useful to augment the sources of evidence exploited by existing rumor verification models. We construct and release the first Authority STance towards Rumors (AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic Twitter. The collection comprises 811 (rumor tweet, authority tweet) pairs relevant to 292 unique rumors. Due to the relatively limited size of our dataset, we explore the adequacy of existing Arabic datasets of stance towards claims in training BERT-based models for our task, and the effect of augmenting AuSTR with those datasets. Our experiments show that, despite its limited size, a model trained solely on AuSTR with a class-balanced focus loss exhibits a comparable performance to the best studied combination of existing datasets augmented with AuSTR, achieving a performance of 0.84 macro-F1 and 0.78 F1 on debunking tweets. The results indicate that AuSTR can be sufficient for our task without the need for augmenting it with existing stance datasets. Finally, we conduct a thorough failure analysis to gain insights for the future directions on the task.

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Section: Authorities For Rumor Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Are Authorities Denying or Supporting? Detecting Stance of Authorities Towards Rumors in Twitter

Haouari,

Elsayed

2023

Preprint

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show abstract

“…Existing studies for rumor verification in social media exploited the propagation networks as a source of evidence, where they focused on the stance of replies [33,24,13,34,8,29], structure of replies [27,12,14,32,19,9], and profile features of retweeters [26]. Recently, Dougrez-Lewis et al [17] proposed augmenting the propagation networks with evidence from the Web.…”

Section: Introductionmentioning

confidence: 99%

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

Haouari¹,

Elsayed²

2023

Preprint

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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.

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

Haouari

Elsayed

2023

Lecture Notes in Computer Science

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A multi-task attention tree neural net for stance classification and rumor veracity detection

Cited by 12 publications

References 26 publications

Are Authorities Denying or Supporting? Detecting Stance of Authorities Towards Rumors in Twitter

Are Authorities Denying or Supporting? Detecting Stance of Authorities Towards Rumors in Twitter

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

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

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