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- \begin{thebibliography}{10}
- \bibitem{webster_social_media}
- Merriam-Webster.
- Social Media~[EB/OL].
- 2018~[2018-04-15].
- \url{http://www.merriam-webster.com/dictionary/social media}.
- \bibitem{digital_in_2018}
- We Are Social.
- Digital in 2018: World's Internet Users Pass the 4 Billion Mark~[EB/OL].
- 2018~[2018-04-15].
- \url{https://wearesocial.com/uk/blog/2018/01/global-digital-report-2018}.
- \bibitem{pew_news_use_2017}
- Pew Research Center.
- News Use Across Social Media Platforms 2017~[EB/OL].
- 2017~[2018-04-15].
- \url{http://www.journalism.org/2017/09/07/news-use-across-social-media-platforms-2017}.
- \bibitem{internet_news_2016}
- 中国互联网络信息中心．
- 2016年中国互联网新闻市场研究报告~[EB/OL]．
- 2017~[2018-04-15]．
- \url{http://www.cnnic.cn/hlwfzyj/hlwxzbg/mtbg/201701/P020170112309068736023.pdf}.
- \bibitem{false_news_spread_2018}
- Vosoughi~S, Roy~D, Aral~S.
- The spread of true and false news online~[J].
- Science, 2018, 359~(6380): 1146--1151.
- \bibitem{唐绪军2013中国新媒体发展报告}
- 唐绪军，吴信训，黄楚新~等．
- 中国新媒体发展报告No.4(2013)~[M]．
- 社会科学文献出版社，2013．
- \bibitem{唐绪军2015中国新媒体发展报告}
- 唐绪军，吴信训，黄楚新~等．
- 中国新媒体发展报告No.6(2015)~[M]．
- 社会科学文献出版社，2015．
- \bibitem{Bengio2006A}
- Bengio~Y, Ducharme~R, Vincent~P~et~al.
- A neural probabilistic language model.~[J].
- Journal of Machine Learning Research, 2006, 3~(6): 1137--1155.
- \bibitem{Hinton1986Learning}
- Hinton~G~E.
- Learning distributed representations of concepts.~[C].
- In Eighth Conference of the Cognitive Science Society, 1986.
- \bibitem{mikolov2013distributed}
- Mikolov~T, Sutskever~I, Chen~K~et~al.
- Distributed representations of words and phrases and their
- compositionality~[C].
- In Advances in neural information processing systems, 2013: 3111--3119.
- \bibitem{Chen2016Behavior}
- Chen~W, Chai~K~Y, Lau~C~T~et~al.
- Behavior deviation: An anomaly detection view of rumor preemption~[C].
- In Information Technology, Electronics and Mobile Communication Conference,
- 2016: 1--7.
- \bibitem{Yan2017OneHot}
- Zhang~Y, Chen~W, Chai~K~Y~et~al.
- A distance-based outlier detection method for rumor detection exploiting user
- behaviorial differences~[C].
- In International Conference on Data and Software Engineering, 2017: 1--6.
- \bibitem{Goodfellow2016DeepLearning}
- Goodfellow~I, Bengio~Y, Courville~A.
- Deep Learning~[M].
- The MIT Press, 2016.
- \bibitem{Sakurada2014Anomaly}
- Sakurada~M, Yairi~T.
- Anomaly Detection Using Autoencoders with Nonlinear Dimensionality
- Reduction~[C].
- In Mlsda 2014 Workshop on Machine Learning for Sensory Data Analysis, 2014: ~4.
- \bibitem{Yan2017Autoencoder}
- Zhang~Y, Chen~W, Chai~K~Y~et~al.
- Detecting rumors on Online Social Networks using multi-layer autoencoder~[C].
- In 2017 IEEE Technology Engineering Management Conference (TEMSCON), 2017:
- 437--441.
- \bibitem{Rumerlhar1986Learning}
- Rumerlhar~D~E.
- Learning representation by back-propagating errors~[J].
- Nature, 1986, 323~(3): 533--536.
- \end{thebibliography}
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