Determining the Veracity of Rumours on Twitter

Giasemidis, Georgios; Singleton, Colin; Agrafiotis, Ioannis; Nurse, Jason R. C.; Pilgrim, Alan; Willis, Chris; Greetham, Danica Vukadinović

doi:10.1007/978-3-319-47880-7_12

Cited by 50 publications

(43 citation statements)

References 21 publications

(40 reference statements)

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“…It is also worthwhile noting that researchers, such as Wu et al (2015), worked with rumours that had a good volume of tweets, in this case at least 1,000. Giasemidis et al (2016) reported experiments run on 100 million tweets associated with 72 different rumours. Features and machine learning classifiers used in previous work were adopted in this case.…”

Section: Approaches To Rumour Veracity Classificationmentioning

confidence: 99%

Detection and Resolution of Rumours in Social Media

et al. 2018

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Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

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Section: Approaches To Rumour Veracity Classificationmentioning

confidence: 99%

Detection and Resolution of Rumours in Social Media

et al. 2018

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“…be as input to classifiers that determine stance of tweets towards rumours [16,38] or classifiers that determine the veracity of rumours [9]. A rumour detection system can in fact be the first component of a system that deals with rumours [36]: (1) rumour detection; (2) rumour tracking; (3) rumour stance classification, and (4) rumour veracity classification.…”

Section: Germanwingsmentioning

confidence: 99%

Exploiting Context for Rumour Detection in Social Media

Zubiaga

Liakata

Procter

2017

Lecture Notes in Computer Science

204

116

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Abstract. Tools that are able to detect unverified information posted on social media during a news event can help to avoid the spread of rumours that turn out to be false. In this paper we compare a novel approach using Conditional Random Fields that learns from the sequential dynamics of social media posts with the current state-of-the-art rumour detection system, as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying the stance of a post to deem it a rumour but, instead, exploits context learned during the event. Our classifier has improved precision and recall over the state-of-the-art classifier that relies on querying tweets, as well as outperforming our best baseline. Moreover, the results provide evidence for the generalisability of our classifier.

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“…Our dataset consists of the 72 rumours used in [10] 2 . The rumours were manually identified from messages (tweets) collected from Twitter, using the Twitter public API and searching for keywords related to specific events.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Most of the research on social media rumours focuses on determining their veracity. Several authors have proposed different supervised systems using temporal, structural, linguistic, network and user-oriented features [10,11,12,13]. However, these approaches assume that message annotation 1 is granted.…”

Section: Introductionmentioning

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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Determining the Veracity of Rumours on Twitter

Cited by 50 publications

References 21 publications

Detection and Resolution of Rumours in Social Media

Detection and Resolution of Rumours in Social Media

Exploiting Context for Rumour Detection in Social Media

A Semi-Supervised Approach to Message Stance Classification

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