Information credibility on twitter

Castillo, Carlos; Mendoza, Marcelo; Poblete, Bárbara

doi:10.1145/1963405.1963500

Cited by 1,902 publications

(1,576 citation statements)

References 27 publications

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“…DTC and SVM-RBF: The Twitter information credibility model using Decision Tree Classifier (Castillo et al, 2011) and the SVM-based model with RBF kernel (Yang et al, 2012), respectively, both using hand-crafted features based on the overall statistics of the posts.…”

Section: Methodsmentioning

confidence: 99%

“…However, such endeavor is manual, thus prone to poor coverage and low speed. Feature-based methods (Castillo et al, 2011;Yang et al, 2012;Ma et al, 2015) achieved certain success by employing large feature sets crafted from message contents, user profiles and holistic statistics of diffusion patterns (e.g., number of retweets, propagation time, etc.). But such an approach was over simplified as they ignored the dynamics of rumor propagation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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)

410

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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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)

410

360

View full text Add to dashboard Cite

show abstract

“…Unsurprisingly, such studies were often conducted in the field of BComputer Science^. Castillo et al for example focus in their studies on the analysis of newsworthy information [6] and later on information credibility [7] on Twitter to establish an automatic discovery process of relevant and credible news. Weitzel et al [47] have a similar goal utilizing social network analysis to assess reputation from source information in the medical domain.…”

Section: Twitter Researchmentioning

confidence: 99%

Twitter data analysis as contribution to strategic foresight-The case of the EU Research Project “Foresight and Modelling for European Health Policy and Regulations” (FRESHER)

Uhl¹,

Kolleck²,

Schiebel³

2016

Eur J Futures Res

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In this article the value of Twitter data analysis for a strategic foresight exercise is discussed. The article offers an overview of Twitter's basic functionalities, previous Twitter research and related studies on using Twitter in foresight projects to date. Based on this knowledge the case of the EU research project BForesight and Modelling for European Health Policy and RegulationsB(FRESHER) is used to conduct a Twitter data analysis in three steps: an analysis of weblinks to get insights into the content spread via Twitter, a social network analysis to define central actors in a Twitter debate, and a hashtag-analysis to find out which topics are discussed a n d t o su p po r t t h e i de n t i f i c at i o n o f d r i v e r s o f noncommunicable diseases. The article shows the benefit a Twitter data analysis provides for the FRESHER project and reveals implications for future research in this field.

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“…[13] explored the information credibility of news propagated through Twitter and proposed to assess the credibility level of newsworthy topics. [14,15] identified the "Link Farmer" in microblog systems.…”

Section: Credibility Of Microblog Datamentioning

confidence: 99%

Detecting Marionette Microblog Users for Improved Information Credibility

Wang

Feng

Fan

et al. 2013

Advanced Information Systems Engineering

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Abstract. In this paper, we mine a special group of microblog users: the "marionette" users, who are created or employed by backstage "puppeteers", either through programs or manually. Unlike normal users that access microblogs for information sharing or social communication, the marionette users perform specific tasks to earn financial profits. For example, they follow certain users to increase their "statistical popularity", or retweet some tweets to amplify their "statistical impact". The fabricated follower or retweet counts not only mislead normal users to wrong information, but also seriously impair microblog-based applications, such as popular tweets selection and expert finding. In this paper, we study the important problem of detecting marionette users on microblog platforms. This problem is challenging because puppeteers are employing complicated strategies to generate marionette users that present similar behaviors as normal ones. To tackle this challenge, we propose to take into account two types of discriminative information: (1) individual user tweeting behaviors and (2) the social interactions among users. By integrating both information into a semi-supervised probabilistic model, we can effectively distinguish marionette users from normal ones. By applying the proposed model to one of the most popular microblog platform (Sina Weibo) in China, we find that the model can detect marionette users with f-measure close to 0.9. In addition, we propose an application to measure the credibility of retweet counts.

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Information credibility on twitter

Cited by 1,902 publications

References 27 publications

Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

Twitter data analysis as contribution to strategic foresight-The case of the EU Research Project “Foresight and Modelling for European Health Policy and Regulations” (FRESHER)

Detecting Marionette Microblog Users for Improved Information Credibility

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