Gaussian Processes for Rumour Stance Classification in Social Media

Łukasik, Michał; Bontcheva, Kalina; Cohn, Trevor; Zubiaga, Arkaitz; Liakata, Maria; Procter, Rob

doi:10.1145/3295823

Cited by 43 publications

(42 citation statements)

References 36 publications

(34 reference statements)

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“…Based on their experimentation and coded dataset, they are able to achieve an accuracy of over 88% in classifying rumour stances in crisis-related posts; here, random forest models result in the best performance. Lukasik et al, [26,27] designed a novel approach based on Gaussian Processes. They explored its effectiveness on two datasets with varying distributions of stances.…”

Section: Related Workmentioning

confidence: 99%

A Semi-Supervised Approach to Message Stance Classification

Giasemidis¹,

Kaplis²,

Agrafiotis

et al. 2020

IEEE Trans. Knowl. Data Eng.

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Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely on determining the constituent messages' stance towards the rumour, a feature known as the "wisdom of the crowd". Although several supervised machine-learning approaches have been proposed to tackle the message stance classification problem, these have numerous shortcomings. In this paper we argue that semi-supervised learning is more effective than supervised models and use two graph-based methods to demonstrate it. This is not only in terms of classification accuracy, but equally important, in terms of speed and scalability. We use the Label Propagation and Label Spreading algorithms and run experiments on a dataset of 72 rumours and hundreds of thousands messages collected from Twitter. We compare our results on two available datasets to the state-of-theart to demonstrate our algorithms' performance regarding accuracy, speed and scalability for real-time applications.

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Section: Related Workmentioning

confidence: 99%

A Semi-Supervised Approach to Message Stance Classification

Giasemidis¹,

Kaplis²,

Agrafiotis

et al. 2020

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…Deep semantics Ma et al 2016Ma et al 2018Wu et al 2018Reis et al 2019Rashkin et al 2017Martin et al 2018Tseng et al 1999Karimi et al 2018Long et al 2017Shu et al 2019 Profiles Influence Interests Yang et al 2018Ghenai et al 2018 Dynamic networks Static networks Jin et al 2016Tacchini et al 2017Ma et al 2017Ruchansky et al 2017Wu et al 2018 Stance-based Ma et al 2018Lukasik et al 2019 Meta-data based Figure 2. The review of information credibility evaluation methods Wu et al, 2019a).…”

Section: Shallow Semanticsmentioning

confidence: 99%

Discovering differential features: Adversarial learning for information credibility evaluation

Rao

Nazir

et al. 2020

Information Sciences

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A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves the state-of-theart performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twit-ter16 validate the robustness of the model with 1.8% performance improvements.

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“…First, we aim to classify the stance of tweets towards rumours that emerge while breaking news stories unfold; these rumours are unlikely to have been observed before and hence rumours from previously observed events, which are likely to diverge, need to be used for training. As far as we know, only work by Lukasik et al [30,32,18] has tackled stance classification in the context of breaking news stories applied to new rumours. Zeng et al [33] have also performed stance classification for rumours around breaking news stories, but overlapping rumours were used for training and testing.…”

Section: Related Workmentioning

confidence: 99%

Discourse-aware rumour stance classification in social media using sequential classifiers

Zubiaga

Kochkina

Liakata

et al. 2018

Information Processing & Management

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123

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Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or 'conversational threads'. Testing the effectiveness of four sequential classifiers -Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM) -on eight datasets associated with breaking news stories, and looking at different types of local and contextual features, our work sheds new light on the development of accurate stance classifiers. We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers. Furthermore, we show that LSTM using a reduced set of features can outperform the other sequential classifiers; this performance is consistent across datasets and across types of stances. To conclude, our work also analyses the different features under study, identifying those that best help characterise and distinguish between stances, such as supporting tweets being more likely to be accompanied by evidence than denying tweets. We also set forth a number of directions for future research.

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Gaussian Processes for Rumour Stance Classification in Social Media

Cited by 43 publications

References 36 publications

A Semi-Supervised Approach to Message Stance Classification

A Semi-Supervised Approach to Message Stance Classification

Discovering differential features: Adversarial learning for information credibility evaluation

Discourse-aware rumour stance classification in social media using sequential classifiers

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