Real-time Rumor Debunking on Twitter

Liu, Xiaomo; Nourbakhsh, Armineh; Li, Quanzhi; Fang, Rui; Shah, Sameena

doi:10.1145/2806416.2806651

Cited by 297 publications

(225 citation statements)

References 8 publications

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“…For instance, there is an increasing body of work [24,15,10,11,17,34] looking into stance classification of tweets discussing rumours, categorising tweets as supporting, denying or questioning the rumour. 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.…”

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

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“…Castillo et al (2011) studied information credibility on Twitter using a wide range of hand-crafted features. Following that, various features corresponding to message contents, user profiles and statistics of propagation patterns were proposed in many studies (Yang et al, 2012;Wu et al, 2015;Sun et al, 2013;Liu et al, 2015). Zhao et al (2015) focused on early rumor detection by using regular expressions for finding questing and denying tweets as the key for debunking rumor.…”

Section: Related Workmentioning

confidence: 99%

“…We constructed our datasets based on a couple of reference datasets, namely Twitter15 (Liu et al, 2015) and Twitter16 (Ma et al, 2016). The original datasets were released and used for binary classification of rumor and non-rumor with respect to given events that contain their relevant tweets.…”

Section: Data Setsmentioning

confidence: 99%

Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

Gao²,

Wong

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

446

360

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How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-ofthe-art rumor detection models.

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“…These include: news gathering, verification, and delivery of corrections [42][43][44][45][46]. These activities are already capitalizing on the growing number of tools, data sets, and platforms contributed by computer scientists to detect, define, model, and counteract the spread of misinformation [47][48][49][50][51][52][53][54][55][56]. Without a clear understanding of what are the most effective countermeasures, and of who is best equipped to deliver them, these tools may never be brought to complete fruition.…”

Section: A Call To Action For Computational Social Scientistsmentioning

confidence: 99%

Fighting fake news: a role for computational social science in the fight against digital misinformation

Ciampaglia

2017

J Comput Soc Sc

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The massive, uncontrolled, and oftentimes systematic spread of inaccurate and misleading information on the Web and social media poses a major risk to society. Digital misinformation thrives on an assortment of cognitive, social, and algorithmic biases and current countermeasures based on journalistic corrections do not seem to scale up. By their very nature, computational social scientists could play a twofold role in the fight against fake news: first, they could elucidate the fundamental mechanisms that make us vulnerable to misinformation online and second, they could devise effective strategies to counteract misinformation.

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Real-time Rumor Debunking on Twitter

Cited by 297 publications

References 8 publications

Exploiting Context for Rumour Detection in Social Media

Exploiting Context for Rumour Detection in Social Media

Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

Fighting fake news: a role for computational social science in the fight against digital misinformation

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