Can Rumour Stance Alone Predict Veracity?

Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets’ times as the only features for modelling true and false rumours achieve F1 scores in the range of 80%, outperforming those approaches where stance is used jointly with content and user based features.

[1]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[2]  Arkaitz Zubiaga,et al.  Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads , 2015, PloS one.

[3]  Samhaa R. El-Beltagy,et al.  NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter. , 2017, *SEMEVAL.

[4]  Keiichi Tokuda,et al.  Multi-Space Probability Distribution HMM , 2002 .

[5]  Man Lan,et al.  ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models , 2017, *SEMEVAL.

[6]  R. Procter,et al.  Reading the riots: what were the police doing on Twitter? , 2013 .

[7]  Kyomin Jung,et al.  Prominent Features of Rumor Propagation in Online Social Media , 2013, 2013 IEEE 13th International Conference on Data Mining.

[8]  Takao Terano,et al.  Detecting rumor patterns in streaming social media , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[9]  Ankit Srivastava,et al.  DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics , 2017, *SEMEVAL.

[10]  Alex Waibel,et al.  Readings in speech recognition , 1990 .

[11]  Chiew Tong Lau,et al.  Behavior deviation: An anomaly detection view of rumor preemption , 2016, 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[12]  Hung-Yu Kao,et al.  IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification , 2017, *SEMEVAL.

[13]  Arkaitz Zubiaga,et al.  SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours , 2017, *SEMEVAL.

[14]  Fan Yang,et al.  Automatic detection of rumor on Sina Weibo , 2012, MDS '12.

[15]  Kenny Q. Zhu,et al.  False rumors detection on Sina Weibo by propagation structures , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[16]  Kwan-Liu Ma,et al.  Breaking news on twitter , 2012, CHI.

[17]  Wei Gao,et al.  Detect Rumors Using Time Series of Social Context Information on Microblogging Websites , 2015, CIKM.

[18]  Deying Li,et al.  An efficient randomized algorithm for rumor blocking in online social networks , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[19]  Arkaitz Zubiaga,et al.  Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter , 2016, ACL.

[20]  Pushpak Bhattacharyya,et al.  IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation , 2017, SemEval@ACL.

[21]  Arkaitz Zubiaga,et al.  Detection and Resolution of Rumours in Social Media , 2017, ACM Comput. Surv..

[22]  Kalina Bontcheva,et al.  Classifying Tweet Level Judgements of Rumours in Social Media , 2015, EMNLP.

[23]  Barbara Poblete,et al.  Twitter under crisis: can we trust what we RT? , 2010, SOMA '10.

[24]  M. Cha,et al.  Rumor Detection over Varying Time Windows , 2017, PloS one.

[25]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

[26]  Quan Z. Sheng,et al.  Extreme User and Political Rumor Detection on Twitter , 2016, ADMA.

[27]  Kalina Bontcheva,et al.  Simple Open Stance Classification for Rumour Analysis , 2017, RANLP.

[28]  Xiaomo Liu,et al.  Real-time Rumor Debunking on Twitter , 2015, CIKM.

[29]  Arkaitz Zubiaga,et al.  Stance Classification in Out-of-Domain Rumours: A Case Study Around Mental Health Disorders , 2017, SocInfo.

[30]  Soroush Vosoughi,et al.  Automatic detection and verification of rumors on Twitter , 2015 .