Novel Visual and Statistical Image Features for Microblogs News Verification

Microblog has been a popular media platform for reporting and propagating news. However, fake news spreading on microblogs would severely jeopardize its public credibility. To identify the truthfulness of news on microblogs, images are very crucial content. In this paper, we explore the key role of image content in the task of automatic news verification on microblogs. Existing approaches to news verification depend on features extracted mainly from the text content of news tweets, while image features for news verification are often ignored. According to our study, however, images are very popular and have a great influence on microblogs news propagation. In addition, fake and real news events have different image distribution patterns. Therefore, we propose several visual and statistical features to characterize these patterns visually and statistically for detecting fake news. Experiments on a real-world multimedia dataset collected from Sina Weibo validate the effectiveness of our proposed image features. The news verification performance of our method outperforms baseline methods. To the best of our knowledge, this is the first attempt that systematically explores image features on news verification task.

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

[2]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[3]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[4]  Benjamin King.  Step-Wise Clustering Procedures , 1967 .

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

[6]  Steven Salzberg,et al.  Programs for Machine Learning , 2004 .

[7]  Scott Counts,et al.  Tweeting is believing?: understanding microblog credibility perceptions , 2012, CSCW.

[8]  Yongdong Zhang,et al.  News Verification by Exploiting Conflicting Social Viewpoints in Microblogs , 2016, AAAI.

[9]  Hermann Ney,et al.  Jointly optimising relevance and diversity in image retrieval , 2009, CIVR '09.

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

[11]  Jiebo Luo,et al.  A Multifaceted Approach to Social Multimedia-Based Prediction of Elections , 2015, IEEE Transactions on Multimedia.

[12]  Yang Yang,et al.  Multimedia Summarization for Social Events in Microblog Stream , 2015, IEEE Transactions on Multimedia.

[13]  Steven L. Salzberg,et al.  Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993 , 1994, Machine Learning.

[14]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Hongyan Liu,et al.  Detecting Event Rumors on Sina Weibo Automatically , 2013, APWeb.

[16]  Ricardo Baeza-Yates,et al.  Improved query difficulty prediction for the web , 2008, CIKM '08.

[17]  W. Bruce Croft,et al.  Predicting query performance , 2002, SIGIR '02.

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

[19]  Justin Cheng,et al.  Rumor Cascades , 2014, ICWSM.

[20]  Qiaozhu Mei,et al.  Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts , 2015, WWW.

[21]  Yiannis Kompatsiaris,et al.  Verifying Multimedia Use at MediaEval 2016 , 2015, MediaEval.

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

[23]  Zhihua Xia,et al.  A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data , 2016, IEEE Transactions on Parallel and Distributed Systems.

[24]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[25]  Yiming Yang,et al.  Learning approaches for detecting and tracking news events , 1999, IEEE Intell. Syst..

[26]  M. Shamim Hossain,et al.  Automatic Visual Concept Learning for Social Event Understanding , 2015, IEEE Transactions on Multimedia.

[27]  Dragomir R. Radev,et al.  What’s with the Attitude? Identifying Sentences with Attitude in Online Discussions , 2010, EMNLP.

[28]  Yongdong Zhang,et al.  Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition , 2016, AAAI.

[29]  Ophir Frieder,et al.  Predicting query difficulty on the web by learning visual clues , 2005, SIGIR '05.

[30]  Xinmei Tian,et al.  Query Difficulty Prediction for Web Image Search , 2012, IEEE Transactions on Multimedia.

[31]  Yongdong Zhang,et al.  News Credibility Evaluation on Microblog with a Hierarchical Propagation Model , 2014, 2014 IEEE International Conference on Data Mining.

[32]  M. de Rijke,et al.  Using Coherence-Based Measures to Predict Query Difficulty , 2008, ECIR.

[33]  Xing Zhou,et al.  Real-Time News Cer tification System on Sina Weibo , 2015, WWW.

[34]  M. Shamim Hossain,et al.  Word-of-Mouth Understanding: Entity-Centric Multimodal Aspect-Opinion Mining in Social Media , 2015, IEEE Transactions on Multimedia.

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

[36]  Yongdong Zhang,et al.  MCG-ICT at MediaEval 2015: Verifying Multimedia Use with a Two-Level Classification Model , 2015, MediaEval.

[37]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[38]  Jiawei Han,et al.  Evaluating Event Credibility on Twitter , 2012, SDM.

[39]  Xian-Sheng Hua,et al.  Towards a Relevant and Diverse Search of Social Images , 2010, IEEE Transactions on Multimedia.

[40]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[41]  Changsheng Xu,et al.  Multi-Modal Event Topic Model for Social Event Analysis , 2016, IEEE Transactions on Multimedia.

[42]  Anupam Joshi,et al.  Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy , 2013, WWW.

[43]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.