{"type":"ScholarlyArticle","title":"Neural User Response Generator: Fake News Detection with Collective User Intelligence","authors":[{"name":"Feng Qian","slug":"feng-qian","given":"Feng","family":"Qian","affiliation":"Peking University"},{"name":"Chengyue Gong","slug":"chengyue-gong","given":"Chengyue","family":"Gong","affiliation":"Peking University"},{"name":"Karishma Sharma","slug":"karishma-sharma","given":"Karishma","family":"Sharma","affiliation":"University of Southern California"},{"name":"Yan Liu","slug":"yan-liu","given":"Yan","family":"Liu","affiliation":"University of Southern California"}],"abstract":"Fake news on social media is a major challenge and studies have shown that fake news can propagate exponentially quickly in early stages. Therefore, we focus on early detection of fake news, and consider that only news article text is available at the time of detection, since additional information such as user responses and propagation patterns can be obtained only after the news spreads. However, we find historical user responses to previous articles are available and can be treated as soft semantic labels, that enrich the binary label of an article, by providing insights into why the article must be labeled as fake. We propose a novel Two-Level Convolutional Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection. We conduct experiments on one available dataset and a larger dataset collected by ourselves. Experimental results show that TCNN-URG outperforms the baselines based on prior approaches that detect fake news from article text alone.","doi":"10.24963/ijcai.2018/533","url":"https://ijcai.org/proceedings/2018/533","pdf":"https://ijcai.org/proceedings/2018/0533.pdf","markdown":"https://ijcai.org/proceedings/2018/533.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/533","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":533,"pages":{"first":3834,"last":3840},"keywords":["Machine Learning: Neural Networks","Multidisciplinary Topics and Applications: AI and the Web"],"videos":[],"references":[{"position":1,"raw":"[Castillo et al., 2011] Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 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