Rumor Identification in Microblogging Systems Based on Users’ Behavior

Liang, Gang; He, Wenbo; Chun, Xu; Chen, Liangyin; Zeng, Jinquan

doi:10.1109/tcss.2016.2517458

Cited by 157 publications

(87 citation statements)

References 23 publications

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“…The approach has been to train a classifier from a labelled set of tweets to categorise the stance observed in new tweets discussing rumours; however, these authors do not deal with nonrumours, assuming instead that the input to the classifier is already cleaned up to include only tweets related to rumours. There is also work on veracity classification both in the context of rumours and beyond [4,14,15,19,33,18,12]. Work on stance and veracity classification can be seen as complementary to our objectives; one could use the set of rumours detected by a rumour detection system as input to a classifier that determines stance of tweets in those rumours and/or veracity of those rumours [36].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Context for Rumour Detection in Social Media

Zubiaga

Liakata

Procter

2017

Lecture Notes in Computer Science

206

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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Section: Related Workmentioning

confidence: 99%

Exploiting Context for Rumour Detection in Social Media

Zubiaga

Liakata

Procter

2017

Lecture Notes in Computer Science

206

116

View full text Add to dashboard Cite

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“…Yang et al (2012) apply two new types of features -client-based and location-based features -to rumour detection on Sina Weibo. Beyond this, user-based (Liang et al, 2015) and topic-based features have also been explored. Friggeri et al (2014) demonstrate that there are structural differences in the propagation of rumours and non-rumours, and Wu et al (2015) and Ma et al (2017) experiment with using these propagation patterns extensively to improve detection.…”

Section: Related Workmentioning

confidence: 99%

Early Rumour Detection

Zhou¹,

Shu²,

Li³

et al. 2019

Proceedings of the 2019 Conference of the North

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Rumours can spread quickly through social media, and malicious ones can bring about significant economical and social impact. Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them. Although there are numerous studies on rumour detection, few are concerned with the timing of the detection. A successfully-detected malicious rumour can still cause significant damage if it isn't detected in a timely manner, and so timing is crucial. To address this, we present a novel methodology for early rumour detection. Our model treats social media posts (e.g. tweets) as a data stream and integrates reinforcement learning to learn the number minimum number of posts required before we classify an event as a rumour. Experiments on Twitter and Weibo demonstrate that our model identifies rumours earlier than state-of-the-art systems while maintaining a comparable accuracy.

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“…However, in contrast to our approach, uncertainty is simply considered as a design parameter, rather than deriving it from network, user, or information characteristics. Liang et al (2015) take the rumor identification approach based on machine-learning techniques in terms of feature design and selection. They identify unique behavior of the rumor publisher and rumor post, compared to normal users and posts, respectively.…”

Section: Network Analysis-based Approachmentioning

confidence: 99%

Untitled

2019

Trans. Soc. Comput.

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Many network scientists have investigated the problem of mitigating or removing false information propagated in social networks. False information falls into two broad categories: disinformation and misinformation. Disinformation represents false information that is knowingly shared and distributed with malicious intent. Misinformation in contrast is false information shared unwittingly, without any malicious intent. Many existing methods to mitigate or remove false information in networks concentrate on methods to find a set of seeding nodes (or agents) based on their network characteristics (e.g., centrality features) to treat. The aim of these methods is to disseminate correct information in the most efficient way. However, little work has focused on the role of uncertainty as a factor in the formulation of agents' opinions. Uncertainty-aware agents can form different opinions and eventual beliefs about true or false information resulting in different patterns of information diffusion in networks.In this work, we leverage an opinion model, called Subjective Logic (SL), which explicitly deals with a level of uncertainty in an opinion where the opinion is defined as a combination of belief, disbelief, and uncertainty, and the level of uncertainty is easily interpreted as a person's confidence in the given belief or disbelief. However, SL considers the dimension of uncertainty only derived from a lack of information (i.e., ignorance), not from other causes, such as conflicting evidence. In the era of Big Data, where we are flooded with information, conflicting information can increase uncertainty (or ambiguity) and have a greater effect on opinions than a lack of information (or ignorance). To enhance the capability of SL to deal with ambiguity as well as ignorance, we propose an SL-based opinion model that includes a level of uncertainty derived from both causes. By developing a variant of the Susceptible-Infected-Recovered epidemic model that can change an agent's status based on the state of their opinions, we capture the evolution of agents' opinions over time.We present an analysis and discussion of critical changes in network outcomes under varying values of key design parameters, including the frequency ratio of true or false information propagation, centrality metrics used for selecting seeding false informers and true informers, an opinion decay factor, the degree of agents' prior belief, and the percentage of true informers. We validated our proposed opinion model using both the synthetic network environments and realistic network environments considering a real network topology, 5:2 J.-H. Cho et al.user behaviors, and the quality of news articles. The proposed agent's opinion model and corresponding strategies to deal with false information can be applicable to combat the spread of fake news in various social media platforms (e.g., Facebook).

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Rumor Identification in Microblogging Systems Based on Users’ Behavior

Cited by 157 publications

References 23 publications

Exploiting Context for Rumour Detection in Social Media

Exploiting Context for Rumour Detection in Social Media

Early Rumour Detection

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