Detecting Event Rumors on Sina Weibo Automatically

Sun, Shengyun; Liu, Hongyan; He, Jun; Du, Xiaoyong

doi:10.1007/978-3-642-37401-2_14

Cited by 92 publications

(57 citation statements)

References 9 publications

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“…At the end of this phase, our labeled data set 5 consists of 2601 false rumors, 2536 normal messages and with 4 million distinct users involved in these messages. Of these 500 false rumors and 500 other messages (called small data set) are used 5 The labeled data set of the original messages (without reposts) is available at http://adapt.seiee.sjtu.edu.cn/ ∼ kzhu/rumor/.…”

Section: A Datasetmentioning

confidence: 99%

“…While some researchers have worked previously on Twitter (see Section V), this paper focuses on false rumor detection on Sina Weibo, for which only limited research has been done [4], [5], [6]. Automatic detection of false rumors is generally a hard problem, because without proper background knowledge or concrete, official evidence against, even human being cannot distinguish between the false rumor and other messages.…”

Section: Introductionmentioning

confidence: 99%

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False rumors detection on Sina Weibo by propagation structures

Yang

Zhu

2015

2015 IEEE 31st International Conference on Data Engineering

476

334

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Abstract-This paper studies the problem of automatic detection of false rumors on Sina Weibo, the popular Chinese microblogging social network. Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. This ignores the propagation structure of the messages and has not achieved very good results. We propose a graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments. The new model achieves a classification accuracy of 91.3% on randomly selected Weibo dataset, significantly higher than state-of-the-art approaches. Moreover, our approach can be applied at the early stage of rumor propagation and is 88% confident in detecting an average false rumor just 24 hours after the initial broadcast.

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Section: A Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

False rumors detection on Sina Weibo by propagation structures

Yang

Zhu

2015

2015 IEEE 31st International Conference on Data Engineering

476

334

View full text Add to dashboard Cite

show abstract

“…Similarly, Rubin et al [29] use satirical cues to detect fakes, which only applies to a specific subset of cases. Another category of methods attempt to include image features in the classification, under the assumption that the image accompanying a post may carry distinct visual characteristics that differ between fake and real posts [14,34]. While this assumption may hold true when contrasting verified posts by news agencies to fake posts by unverified sources, it certainly cannot assist us when comparing user-generated fake and real posts.…”

Section: Related Workmentioning

confidence: 99%

Detection and visualization of misleading content on Twitter

Boididou

Papadopoulos

Zampoglou

et al. 2017

Int J Multimed Info Retr

121

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The problems of online misinformation and fake news have gained increasing prominence in an age where user-generated content and social media platforms are key forces in the shaping and diffusion of news stories. Unreliable information and misleading content are often posted and widely disseminated through popular social media platforms such as Twitter and Facebook. As a result, journalists and editors are in need of new tools that can help them speed up the verification process for content that is sourced from social media. Motivated by this need, in this paper we present a system that supports the automatic classification of multimedia Twitter posts into credible or misleading. The system leverages credibility-oriented features extracted from the tweet and the user who published it, and trains a two-step classification model based on a novel semisupervised learning scheme. The latter uses the agreement between two independent pre-trained models on new posts as guiding signals for retraining the classification model. We analyze a large labeled dataset of tweets that shared debunked fake and confirmed real images and videos, and show that integrating the newly proposed features, and making use of bagging in the initial classifiers and of the semi-supervised learning scheme, significantly improves classification accuracy. Moreover, we present a web-based application for visualizing and communicating the classification results to end users.

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“…Sun Shengyun et.al proposed an idea of using external knowledge to detect specific rumors. Aiming at the incongruent of text and picture problem in rumor, they searched the picture source via search engine and trained classifier to detect rumors [13].…”

Section: Related Workmentioning

confidence: 99%

A Method of Fast Rumors Identification on Microblog

Wang¹,

Liu²,

Zhou³

2017

dtcse

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Microblog realizes the real-time sharing of information whereas there is a great deal of false information at the same time. The widely propagation of rumors on microblog may give rise to public panic, the trust crisis of Internet and even endanger national security. Given the overflow problem of rumors, this paper proposes a method of fast identification of rumors extracting the corresponding eigenvector of each microblog according to the characteristics based on content and propagation digging from rumors on microblog. The similarity degree is measured by cosine theorem. Recognition results can effectively help people identify rumors better.

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Detecting Event Rumors on Sina Weibo Automatically

Cited by 92 publications

References 9 publications

False rumors detection on Sina Weibo by propagation structures

False rumors detection on Sina Weibo by propagation structures

Detection and visualization of misleading content on Twitter

A Method of Fast Rumors Identification on Microblog

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