參考文獻 |
Ajao, O., Bhowmik, D., & Zargari, S. (2018). Fake news identification on twitter with hybrid cnn and rnn models. Paper presented at the Proceedings of the 9th International Conference on Social Media and Society.
Al-Sarem, M., Boulila, W., Al-Harby, M., Qadir, J., & Alsaeedi, A. (2019). Deep Learning-Based Rumor Detection on Microblogging Platforms: A Systematic Review. IEEE Access, 7, 152788-152812.
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-236.
Allport, G. W., & Postman, L. (1946). An analysis of rumor. Public Opinion Quarterly, 10(4), 501-517.
Asghar, M. Z., Habib, A., Habib, A., Khan, A., Ali, R., & Khattak, A. (2019). Exploring deep neural networks for rumor detection. Journal of Ambient Intelligence and Humanized Computing, 1-19.
Bondielli, A., & Marcelloni, F. (2019). A survey on fake news and rumour detection techniques. Information Sciences, 497, 38-55.
Cao, J., Guo, J., Li, X., Jin, Z., Guo, H., & Li, J. (2018). Automatic rumor detection on microblogs: A survey. arXiv preprint arXiv:1807.03505.
Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on twitter. Paper presented at the Proceedings of the 20th international conference on World wide web.
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. P. (2010). Measuring user influence in twitter: The million follower fallacy. Paper presented at the fourth international AAAI conference on weblogs and social media.
Chen, T., Chen, H., & Li, X. (2018). Rumor Detection via Recurrent Neural Networks: A Case Study on Adaptivity with Varied Data Compositions. Paper presented at the Pacific-Asia Conference on Knowledge Discovery and Data Mining.
Chen, Y., Conroy, N. J., & Rubin, V. L. (2015). Misleading online content: Recognizing clickbait as false news. Paper presented at the Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection.
Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197-387.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
DiFonzo, N., & Bordia, P. (2007). Rumor, gossip and urban legends. Diogenes, 54(1), 19-35.
Erlich, A., Jung, D. F., Long, J. D., & McIntosh, C. (2018). The double-edged sword of mobilizing citizens via mobile phone in developing countries. Development Engineering, 3, 34-46.
Geng, Y., Lin, Z., Fu, P., & Wang, W. (2019). Rumor Detection on Social Media: A Multi-view Model Using Self-attention Mechanism. Paper presented at the International Conference on Computational Science.
Gottfried, J., & Shearer, E. (2017). Americans’ online news use is closing in on TV news use. Pew Research Center, 7.
Gupta, A., Lamba, H., Kumaraguru, P., & Joshi, A. (2013). Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. Paper presented at the Proceedings of the 22nd international conference on World Wide Web.
Hu, X., Tang, J., & Liu, H. (2014). Online social spammer detection. Paper presented at the Twenty-Eighth AAAI Conference on Artificial Intelligence.
Jin, Z., Cao, J., Zhang, Y., & Luo, J. (2016). News verification by exploiting conflicting social viewpoints in microblogs. Paper presented at the Thirtieth AAAI Conference on Artificial Intelligence.
Kwon, S., Cha, M., Jung, K., Chen, W., & Wang, Y. (2013). Prominent features of rumor propagation in online social media. Paper presented at the 2013 IEEE 13th International Conference on Data Mining.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Li, L., Cai, G., & Chen, N. (2018). A Rumor Events Detection Method Based on Deep Bidirectional GRU Neural Network. Paper presented at the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC).
Liang, G., He, W., Xu, C., Chen, L., & Zeng, J. (2015). Rumor identification in microblogging systems based on users’ behavior. IEEE Transactions on Computational Social Systems, 2(3), 99-108.
Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B. J., Wong, K.-F., & Cha, M. (2016). Detecting rumors from microblogs with recurrent neural networks. Paper presented at the Ijcai.
Ma, J., Gao, W., Wei, Z., Lu, Y., & Wong, K.-F. (2015). Detect rumors using time series of social context information on microblogging websites. Paper presented at the Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.
Ma, J., Gao, W., & Wong, K.-F. (2017). Detect rumors in microblog posts using propagation structure via kernel learning. Paper presented at the Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Ma, J., Gao, W., & Wong, K.-F. (2019). Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning. Paper presented at the The World Wide Web Conference.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Paper presented at the Advances in neural information processing systems.
Nguyen, T. N., Li, C., & Niederée, C. (2017). On early-stage debunking rumors on twitter: Leveraging the wisdom of weak learners. Paper presented at the International Conference on Social Informatics.
Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. Paper presented at the Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP).
Rubin, V. L., Chen, Y., & Conroy, N. J. (2015). Deception detection for news: three types of fakes. Paper presented at the Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community.
Ruchansky, N., Seo, S., & Liu, Y. (2017). Csi: A hybrid deep model for fake news detection. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22-36.
Singh, J. P., Rana, N. P., & Dwivedi, Y. K. (2019). Rumour Veracity Estimation with Deep Learning for Twitter. Paper presented at the International Working Conference on Transfer and Diffusion of IT.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. Paper presented at the Advances in neural information processing systems.
Vosoughi, S., Mohsenvand, M. N., & Roy, D. (2017). Rumor gauge: predicting the veracity of rumors on twitter. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(4), 50.
WHO, F. a. (2003). Assuring food safety and quality: guidelines for strengthening national food
control systems.
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., . . . Funtowicz, M. (2019). Transformers: State-of-the-art Natural Language Processing. arXiv preprint arXiv:1910.03771.
Wu, K., Yang, S., & Zhu, K. Q. (2015). False rumors detection on sina weibo by propagation structures. Paper presented at the 2015 IEEE 31st international conference on data engineering.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., . . . Macherey, K. (2016). Google′s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Yang, F., Liu, Y., Yu, X., & Yang, M. (2012). Automatic detection of rumor on Sina Weibo. Paper presented at the Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics.
Yu, F., Liu, Q., Wu, S., Wang, L., & Tan, T. (2017). A convolutional approach for misinformation identification. International Joint Conferences on Artificial Intelligence (IJCAI).
Zhou, Z., Qi, Y., Liu, Z., Yu, C., & Wei, Z. (2018). A C-GRU Neural Network for Rumors Detection. Paper presented at the 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS).
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2018). Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR), 51(2), 32.
Zubiaga, A., Liakata, M., & Procter, R. (2016). Learning reporting dynamics during breaking news for rumour detection in social media. arXiv preprint arXiv:1610.07363.
Zubiaga, A., Liakata, M., Procter, R., Bontcheva, K., & Tolmie, P. (2015). Towards detecting rumours in social media. Paper presented at the Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence.
黃致喬(2018年)。消費者的健康意識、信任、知覺價值對食品的購買意願影響之研究。
食藥署-藥物食品安全週報第731期(2019年9月20日)。惡傳食安謠言 最高罰百萬。
吳亮儀(2017年12月10日)。食安謠言亂傳 害民生恐慌。取自https://news.ltn.com.tw/news/focus/paper/1158995 (2019年11月12日)。
食力foodNEXT(2016年12月24日)。眼睛業障重呀!你也被食謠言騙了嗎?。取自 https://www.foodnext.net/science/scsource/paper/4616154160 (2020年6月23日)。 |