A Temporal Attentional Model for Rumor Stance Classification

Veyseh, Amir Pouran Ben; Ebrahimi, Javid; Dou, Dejing; Lowd, Daniel

doi:10.1145/3132847.3133116

Cited by 26 publications

(15 citation statements)

References 5 publications

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“…For task A, we will provide code for a state-of-the-art baseline from RumourEval 2017 Task A (Kochkina, Liakata, and Augenstein 2017) together with later higher-performing entry published at RANLP that year (Aker, Derczynski, and Bontcheva 2017). The latest state of the art system for stance classification on RumourEval 2017 Task A dataset (Veyseh et al 2017) may be provided in case of successful implementation.…”

Section: Baselinementioning

confidence: 99%

SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

Gorrell¹,

Kochkina²,

Liakata³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

172

198

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This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year's SemEval event.Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the dangers of "fake news" have become a mainstream concern. Yet automated support for rumour checking remains in its infancy. For this reason, it is important that a shared task in this area continues to provide a focus for effort, which is likely to increase. We therefore propose a continuation in which the veracity of further rumours is determined, and as previously, supportive of this goal, tweets discussing them are classified according to the stance they take regarding the rumour. Scope is extended compared with the first RumourEval, in that the dataset is substantially expanded to include Reddit as well as Twitter data, and additional languages are also included.

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Section: Baselinementioning

confidence: 99%

SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

Gorrell¹,

Kochkina²,

Liakata³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

172

198

View full text Add to dashboard Cite

show abstract

“…Actually, CNN processes the text information in a parallel way. By introducing self attention mechanism, CNN can achieve satisfactory results for the rumor stance classification [17]- [26]. In addition, some scholars consider the sequential relation between replies.…”

Section: B Stance Classification Based On Sequencementioning

confidence: 99%

“…In the past few years, there is a growing interest in studying rumor stance classification task in social media [6]- [9], but conventional methods do not consider the temporal correlation between replies. In order to extract textual features and temporal correlation between replies [10]- [13], Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) and the attention mechanism are proposed to finish this classification task [14], [15]. However, these models neglect the reading habits of the public.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Attention CNN Model for Rumor Stance Classification

2020

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Rumor stance classification is a task for identifying different stances about specific social media posts, and it is considered as an important step to prevent rumors from spreading. However, most previous studies for the rumor stance classification, which focus on content features of posts, ignore the textual information granularity and reading (or writing) habits of the public. In social networks, people may have different stances towards unverified posts with different reading habits. Based on this observation, we propose a Stochastic Attention Convolutional Neural Net (SACNN) under different textual information granularity to catch different habits of the public for the rumor stance classification task. Specifically, being treated as view-windows of readers, convolutional kernels in a SACNN contain trainable convolutional kernels and stochastic untrainable convolutional kernels, and different sizes of the stochastic convolutional kernels are used to simulate casual online reading habits and to extract course-grained features such as phrase features. After the convolutional layer, fine-grained features such as keywords can be extracted by a pooling layer. The experiments show that the average accuracy and F1 score of our proposed model are respectively 0.26% and 0.46% higher than the state-of-the-art results on the rumor stance classification task on PHEME dataset.

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“…In addition to the main task of rumor detection, there are other approaches that attempt to detect the stance of the replies toward the main post and then detect the rumor [13], [16]. It has been shown that for this task the evolution of the people's stance toward the main post is very helpful and considering time series is important for this problem.…”

Section: Related Workmentioning

confidence: 99%

Rumor detection in social networks via deep contextual modeling

Veyseh

Thai

Nguyen

et al. 2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Self Cite

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Fake news and rumors constitute a major problem in social networks recently. Due to the fast information propagation in social networks, it is inefficient to use human labor to detect suspicious news. Automatic rumor detection is thus necessary to prevent devastating effects of rumors on the individuals and society. Previous work has shown that in addition to the content of the news/posts and their contexts (i.e., replies), the relations or connections among those components are important to boost the rumor detection performance. In order to induce such relations between posts and contexts, the prior work has mainly relied on the inherent structures of the social networks (e.g., direct replies), ignoring the potential semantic connections between those objects. In this work, we demonstrate that such semantic relations are also helpful as they can reveal the implicit structures to better capture the patterns in the contexts for rumor detection. We propose to employ the self-attention mechanism in neural text modeling to achieve the semantic structure induction for this problem. In addition, we introduce a novel method to preserve the important information of the main news/posts in the final representations of the entire threads to further improve the performance for rumor detection. Our method matches the main post representations and the thread representations by ensuring that they predict the same latent labels in a multitask learning framework. The extensive experiments demonstrate the effectiveness of the proposed model for rumor detection, yielding the state-of-the-art performance on recent datasets for this problem.

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A Temporal Attentional Model for Rumor Stance Classification

Cited by 26 publications

References 5 publications

SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

A Stochastic Attention CNN Model for Rumor Stance Classification

Rumor detection in social networks via deep contextual modeling

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