{"type":"ScholarlyArticle","title":"A Convolutional Approach for Misinformation Identification","authors":[{"name":"Feng Yu","slug":"feng-yu","given":"Feng","family":"Yu","affiliation":"Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition; University of Chinese Academy of Sciences"},{"name":"Qiang Liu","slug":"qiang-liu","given":"Qiang","family":"Liu","affiliation":"Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition; University of Chinese Academy of Sciences"},{"name":"Shu Wu","slug":"shu-wu","given":"Shu","family":"Wu","affiliation":"Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition"},{"name":"Liang Wang","slug":"liang-wang","given":"Liang","family":"Wang","affiliation":"Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition; Center for Excellence in Brain Science and Intelligence Technology Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Tieniu Tan","slug":"tieniu-tan","given":"Tieniu","family":"Tan","affiliation":"Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition; Center for Excellence in Brain Science and Intelligence Technology Institute of Automation , Chinese Academy of Sciences; University of Chinese Academy of Sciences"}],"abstract":"The fast expanding of social media fuels the spreading of misinformation which disrupts people's normal lives. It is urgent to achieve goals of misinformation identification and early detection in social media. In dynamic and complicated social media scenarios, some conventional methods mainly concentrate on feature engineering which fail to cover potential features in new scenarios and have difficulty in shaping elaborate high-level interactions among significant features. Moreover, a recent Recurrent Neural Network (RNN) based method suffers from deficiencies that it is not qualified for practical early detection of misinformation and poses a bias to the latest input. In this paper, we propose a novel method, Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN). CAMI can flexibly extract key features scattered among an input sequence and shape high-level interactions among significant features, which help effectively identify misinformation and achieve practical early detection. Experiment results on two large-scale datasets validate the effectiveness of CAMI model on both misinformation identification and early detection tasks.","doi":"10.24963/ijcai.2017/545","url":"https://ijcai.org/proceedings/2017/545","pdf":"https://ijcai.org/proceedings/2017/0545.pdf","markdown":"https://ijcai.org/proceedings/2017/545.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/545","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":545,"pages":{"first":3901,"last":3907},"keywords":["Multidisciplinary Topics and Applications: AI&Security and Privacy","Multidisciplinary Topics and Applications: AI and Social Sciences"],"videos":[],"references":[{"position":1,"raw":"[Abdel-Hamid et al., 2012] Ossama Abdel-Hamid, Abdel- rahman Mohamed, Hui Jiang, and Gerald Penn. Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition. In ICASSP, pages 4277- 4280, 2012.","title":"Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition","authors":"Abdel-Hamid","year":"2012","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Castillo et al., 2011] Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. Information credibility on twitter. In WWW, pages 675-684, 2011.","title":"Information credibility on twitter","authors":"Carlos Castillo, Marcelo Mendoza, Barbara Poblete","year":"2011","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Giudice, 2010] Katherine Del Giudice. Crowdsourcing credibility: The impact of audience feedback on web page credibility. Proceedings of the American Society for Infor- mation Science and Technology, 47(1):1-9, 2010.","title":"Crowdsourcing credibility: The impact of audience feedback on web page credibility","authors":"Katherine Del, Giudice","year":"2010","venue":"Proceedings of the American Society for Information Science and Technology","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gupta et al., 2013] Aditi Gupta, Hemank Lamba, Ponnu- rangam Kumaraguru, and Anupam Joshi. Faking sandy: characterizing and identifying fake images on twitter dur- ing hurricane sandy. In WWW, pages 729-736, 2013.","title":"Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy","authors":"Aditi Gupta, Hemank Lamba, Ponnurangam Kumaraguru, Anupam Joshi","year":"2013","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Jin et al., 2014] Zhiwei Jin, Juan Cao, Yu-Gang Jiang, and Yongdong Zhang. News credibility evaluation on mi- croblog with a hierarchical propagation model. In ICDM, pages 230-239, 2014.","title":"News credibility evaluation on microblog with a hierarchical propagation model","authors":"Zhiwei Jin, Juan Cao, Yu-Gang Jiang, Yongdong Zhang","year":"2014","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Jin et al., 2016] Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. News verification by exploiting conflicting social viewpoints in microblogs. In AAAI, pages 2972- 2978, 2016.","title":"News verification by exploiting conflicting social viewpoints in microblogs","authors":"Zhiwei Jin, Juan Cao, Yongdong Zhang, Jiebo Luo","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kalchbrenner et al., 2014] Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. A convolutional neural network for modelling sentences. In ACL, pages 655-665, 2014.","title":"A convolutional neural network for modelling sentences","authors":"Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom","year":"2014","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kumar and Geethakumari, 2014] KP Krishna Kumar and G Geethakumari. Detecting misinformation in online so- cial networks using cognitive psychology. Human-centric Computing and Information Sciences, 4(1):1, 2014.","title":"Detecting misinformation in online social networks using cognitive psychology","authors":"Krishna Kp, G Kumar, Geethakumari","year":"2014","venue":"Human-centric Computing and Information Sciences","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kumar et al., 2016] Srijan Kumar, Robert West, and Jure Leskovec. Disinformation on the web: Impact, character- istics, and detection of wikipedia hoaxes. In WWW, pages 591-602, 2016.","title":"Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes","authors":"Srijan Kumar, Robert West, Jure Leskovec","year":"2016","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kwon et al., 2013] Sejeong Kwon, Meeyoung Cha, Ky- omin Jung, Wei Chen, and Yajun Wang. Prominent fea- tures of rumor propagation in online social media. In ICDM, pages 1103-1108, 2013.","title":"Prominent features of rumor propagation in online social media","authors":"Kwon","year":"2013","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Le and Mikolov, 2014] Quoc V Le and Tomas Mikolov. Distributed representations of sentences and documents. In ICML, pages 1188-1196, 2014.","title":"Distributed representations of sentences and documents","authors":"V Quoc, Tomas Le, Mikolov","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2015] Qiang Liu, Feng Yu, Shu Wu, and Liang Wang. A convolutional click prediction model. In CIKM, pages 1743-1746, 2015.","title":"A convolutional click prediction model","authors":"Qiang Liu, Feng Yu, Shu Wu, Liang Wang","year":"2015","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ma et al., 2015] Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, and Kam-Fai Wong. Detect rumors using time series of social context information on microblogging websites. In CIKM, pages 1751-1754, 2015.","title":"Detect rumors using time series of social context information on microblogging websites","authors":"Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, Kam-Fai Wong","year":"2015","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ma et al., 2016] Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J Jansen, Kam-Fai Wong, and Meeyoung Cha. Detecting rumors from microblogs with recurrent neural networks. In IJCAI, pages 3818-3824, 2016.","title":"Detecting rumors from microblogs with recurrent neural networks","authors":"Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J Jansen, Kam-Fai Wong, Meeyoung Cha","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/537"},{"position":15,"raw":"[Mikolov et al., 2011] Tomáš Mikolov, Stefan Kombrink, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. Ex- tensions of recurrent neural network language model. In ICASSP, pages 5528-5531, 2011.","title":"Extensions of recurrent neural network language model","authors":"Mikolov","year":"2011","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Qazvinian et al., 2011] Vahed Qazvinian, Emily Rosen- gren, Dragomir R Radev, and Qiaozhu Mei. Rumor has it: Identifying misinformation in microblogs. In EMNLP, pages 1589-1599, 2011.","title":"Rumor has it: Identifying misinformation in microblogs","authors":"Emily Vahed Qazvinian, Rosengren, Qiaozhu Dragomir R Radev, Mei","year":"2011","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Rieh et al., 2014] Soo Young Rieh, Grace YoungJoo Jeon, Ji Yeon Yang, and Cliff Lampe. Audience-aware credibil- ity: From understanding audience to establishing credible blogs. In ICWSM, 2014.","title":"Audience-aware credibility: From understanding audience to establishing credible blogs","authors":"Soo Young Rieh, Grace Youngjoo Jeon, Ji Yeon Yang, Cliff Lampe","year":"2014","venue":"ICWSM","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Simonyan et al., 2013] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034, 2013.","title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","authors":"Karen Simonyan, Andrea Vedaldi, Andrew Zisserman","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tamar et al., 2016] Aviv Tamar, Sergey Levine, Pieter Abbeel, Yi Wu, and Garrett Thomas. Value iteration net- works. In NIPS, pages 2146-2154, 2016.","title":"Value iteration networks","authors":"Tamar","year":"2016","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Vondrick et al., 2013] Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, and Antonio Torralba. Hoggles: Visualizing object detection features. In ICCV, pages 1-8, 2013.","title":"Hoggles: Visualizing object detection features","authors":"Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba","year":"2013","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yang et al., 2012] Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. Automatic detection of rumor on sina weibo. In SIGKDD Workshop on Mining Data Semantics, page 13, 2012.","title":"Automatic detection of rumor on sina weibo","authors":"Fan Yang, Yang Liu, Xiaohui Yu, Min Yang","year":"2012","venue":"SIGKDD Workshop on Mining Data Semantics","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhao et al., 2015] Zhe Zhao, Paul Resnick, and Qiaozhu Mei. Early detection of rumors in social media from en- quiry posts. In WWW, pages 1395-1405, 2015.","title":"Early detection of rumors in social media from enquiry posts","authors":"Zhe Zhao, Paul Resnick, Qiaozhu Mei","year":"2015","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhao et al., 2017] Hengshuang Zhao, Jianping Shi, Xiao- juan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017.","title":"Pyramid scene parsing network","authors":"Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}