Classifying Tweet Level Judgements of Rumours in Social Media

Łukasik, Michał; Cohn, Trevor; Bontcheva, Kalina

doi:10.18653/v1/d15-1311

Cited by 49 publications

(53 citation statements)

References 10 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

223

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

223

116

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show abstract

“…We choose this method due to interpretability of results, similar to recent work on occupational class classification (Preotiuc-Pietro et al, 2015;Lukasik et al, 2015).…”

Section: Rumor Detection Via Kernel Learningmentioning

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)

471

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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“…We consider the Leave One Out (LOO) setting, introduced by Lukasik et al (2015a), where for each rumour R i ∈ D we construct the test set equal to R i and the training set equal to D \ R i . The final performance scores we report in the paper are averaged across all rumours.…”

Section: Problem Definitionmentioning

confidence: 99%

Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

Łukasik¹,

Srijith²,

Vu³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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114

113

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Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal information of tweets along with the textual content.

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Classifying Tweet Level Judgements of Rumours in Social Media

Cited by 49 publications

References 10 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

Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

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