由於網路的普遍性以及自由性，造成許多不實的言論透過不同的管道流竄，時常造成社會的恐慌與不安定，因此假新聞偵測的研究議題隨之而生，透過觀察不同種類假新聞的特性，幫助人類判斷網路訊息的真實性。在眾多偵測假新聞的方法之中，透過群眾對於新聞的立場來判斷其真實性是一種主流的方式，大部分的方法會將立場視為重要的特徵，或是觀察立場隨著時間的變化來驗證新聞的真偽，然而如何精準的偵測文章對於新聞的立場是大部分模型的瓶頸。因此，假新聞立場的偵測也被視為一個困難的研究議題。同時，過去的研究只著重於判斷新聞的立場，卻忽略了在判斷真實性時應該要提出有力的證據，所以無法使使用者信服模型預測的結果。因此，偵測新聞的立場同時擷取有說服力的證據成為近年來新興的研究領域。
本研究的目的即為判斷新聞的立場同時找出具有描述力的證據以解釋立場。過去的研究過於依賴新聞內容與事件的描述的相似度來尋找證據，導致無法找出擁有更多額外的資訊以佐證立場的證據，同時也無法利用這些證據來推論立場。因此，我們透過字與字競爭的方式找到段落中與事件描述有相同主題的字，並透過這些字與事件描述的交互作用來推斷新聞的立場。除此之外，我們也利用這些字來決定每個證據對於事件的描述程度，藉此來擷取針對事件有最多描述的證據，使使用者可以更全面的了解新聞的立場。

Due to the universality and the freedom of the Internet, lots of fabricated messages are spread in various ways. These messages often cause panic and chaos in the society. Therefore, fake news detection becomes a critical research field. It verifies the news stories and rumors by observing different characteristics of false information. Among various ways of detecting fake news, one major way is to make use of the stance from the crowd to assess the credibility of the news. Most of the methods take the stance as an important feature or utilize the stance changing over time to validate the rumors. However, how to precisely identify the stance of the news becomes the bottleneck of most models. Thus, the fake news stance detection is also regarded as a difficult issue. At the same time, previous researches only focused on predicting the stance of the news but ignored to provide favorable evidence. Hence, it is hard to convince users of the predicted result. Therefore, detecting stance with evidence become a growing research field in recent years.
In this paper, we aim at detecting the stance of the news and extracting the descriptive evidence which can explain the stance simultaneously. Recent systems are too dependent on the similarity between the news content and the claim to find favorable evidence. The favorable evidence should contain more additional information to prove the stance about the claim. Meanwhile, they cannot take advantage of the evidence to infer the stance. Thus, we propose a method to find the words which have the same topics as the claim in the article by making a competition between words. Moreover, we utilize the interaction between these topic words and the claim to infer the stance. Besides, we use the topic words to assign a descriptive degree of each document toward the claim and extract the most descriptive document as evidence.

[1] Shu, Kai, et al. "Fake news detection on social media: A data mining perspective." ACM SIGKDD Explorations Newsletter 19.1 (2017): 22-36.
[2] Ma, Jing, Wei Gao, and Kam-Fai Wong. "Detect rumor and stance jointly by neural multi-task learning." Companion Proceedings of the The Web Conference 2018. International World Wide Web Conferences Steering Committee, 2018.
[3] Popat, Kashyap, et al. "Where the truth lies: Explaining the credibility of emerging claims on the web and social media." Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 2017.
[4] Zhi, Shi, et al. "ClaimVerif: a real-time claim verification system using the web and fact databases." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017.
[5] Mohtarami, Mitra, et al. "Automatic stance detection using end-to-end memory networks." arXiv preprint arXiv:1804.07581 (2018).
[6] Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "End-to-end memory networks." Advances in neural information processing systems. 2015.
[7] Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
[8] Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. "Neural machine translation by jointly learning to align and translate." arXiv preprint arXiv:1409.0473 (2014).
[9] Chen, Yu, and Mohammed J. Zaki. "Kate: K-competitive autoencoder for text." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017.
[10] Dai, Andrew M., and Quoc V. Le. "Semi-supervised sequence learning." Advances in neural information processing systems. 2015.
[11] Nie, Yixin, Haonan Chen, and Mohit Bansal. "Combining fact extraction and verification with neural semantic matching networks." arXiv preprint arXiv:1811.07039 (2018).
[12] Griffiths, Thomas L., and Mark Steyvers. "Finding scientific topics." Proceedings of the National academy of Sciences 101.suppl 1 (2004): 5228-5235.
[13] Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014).
[14] Goodfellow, Ian J., et al. "Maxout networks." arXiv preprint arXiv:1302.4389 (2013).
[15] Jones, Karen Spärck. "A statistical interpretation of term specificity and its application in retrieval." Journal of documentation (2004).
[16] Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).
[17] Manning, Christopher, et al. "The Stanford CoreNLP natural language processing toolkit." Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 2014.
[18] Veyseh, Amir Pouran Ben, et al. "A temporal attentional model for rumor stance classification." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017.
[19] Popat, Kashyap, et al. "DeClarE: Debunking fake news and false claims using evidence-aware deep learning." arXiv preprint arXiv:1809.06416 (2018).
[20] Conforti, Costanza, Mohammad Taher Pilehvar, and Nigel Collier. "Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles." Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). 2018.
[21] Hanselowski, Andreas, et al. "A retrospective analysis of the fake news challenge stance detection task." arXiv preprint arXiv:1806.05180 (2018).
[22] Lin, Chih-Jen. "Projected gradient methods for nonnegative matrix factorization." Neural computation 19.10 (2007): 2756-2779.
[23] Deerwester, Scott, et al. "Indexing by latent semantic analysis." Journal of the American society for information science 41.6 (1990): 391-407.
[24] Augenstein, Isabelle, et al. "Stance detection with bidirectional conditional encoding." arXiv preprint arXiv:1606.05464 (2016).