ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Information Science and Technology 11 May 2022

A data-driven model for social media fake news detection

Cite this:
https://doi.org/10.52396/JUSTC-2021-0215
More Information
  • Author Bio:

    Xin Chen is currently pursuing a master’s degree at the University of Science and Technology of China, Hefei, China. Her research focuses mainly on rumor detection in social media and cross-modal understanding

    Zhendong Mao received his PhD degree in computer application technology from the Institute of Computing Technology, Chinese Academy of Sciences, in 2014. He was an assistant professor with the Institute of Information Engineering, Chinese Academy of Sciences, Beijing, from 2014 to 2018. He is currently a professor at the School of Cyberspace Science and Technology, University of Science and Technology of China, Hefei, China. His research interests include computer vision, natural language processing and cross-modal understanding

  • Corresponding author: E-mail: zdmao@ustc.edu.cn
  • Received Date: 01 October 2021
  • Accepted Date: 14 December 2021
  • Available Online: 11 May 2022
  • The rapid development of social media leads to the spread of a large amount of false news, which not only affects people’s daily life but also harms the credibility of social media platforms. Therefore, detecting Chinese fake news is a challenging and meaningful task. However, existing fake news datasets from Chinese social media platforms have a relatively small amount of data and data collection in this field is relatively old, thus being unable to meet the requirements of further research. In consideration of this background, we release a new Chinese Weibo Fake News dataset, which contains 26320 fake news data collected from Weibo. In addition, we propose a fake news detection model based on data augmentation that can effectively solve the problem of a lack of fake news, and we improve the generalization ability and robustness of the model. We conduct numerous experiments on our Chinese Weibo Fake News dataset and successfully deploy the model on the web page. The experimental performance proves the effectiveness of the proposed end-to-end model for detecting fake news on social media platforms.

      The overall framework of our fake news detection model.

    The rapid development of social media leads to the spread of a large amount of false news, which not only affects people’s daily life but also harms the credibility of social media platforms. Therefore, detecting Chinese fake news is a challenging and meaningful task. However, existing fake news datasets from Chinese social media platforms have a relatively small amount of data and data collection in this field is relatively old, thus being unable to meet the requirements of further research. In consideration of this background, we release a new Chinese Weibo Fake News dataset, which contains 26320 fake news data collected from Weibo. In addition, we propose a fake news detection model based on data augmentation that can effectively solve the problem of a lack of fake news, and we improve the generalization ability and robustness of the model. We conduct numerous experiments on our Chinese Weibo Fake News dataset and successfully deploy the model on the web page. The experimental performance proves the effectiveness of the proposed end-to-end model for detecting fake news on social media platforms.

    • We create a new Chinese social platform fake news dataset containing high quantity content and abundant information to facilitate research on fake news detection.
    • Creatively introduce data augmentation in fake news detection research, and combine user attributes to better realize fake news detection.

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  • [1]
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    [2]
    Allport G W, Postman L. The Psychology of Rumor. New York: Henry Holt, 1947.
    [3]
    Peterson W A, Gist N P. Rumor and public opinion. American Journal of Sociology, 1951, 57: 159–167. doi: 10.1086/220916
    [4]
    Liu F, Burton-Jones A, Xu D. Rumors on social media in disasters: Extending transmission to retransmission. In: Proceeding of the 19th Pacific Asia Conference on Information Systems (PACIS 2014), Chengdu, China. 2014: 49.
    [5]
    Tasnim S, Hossain M M, Mazumder H. Impact of rumors and misinformation on COVID-19 in social media. Journal of Preventive Medicine and Public Health, 2020, 53 (3): 171–174. doi: 10.3961/jpmph.20.094
    [6]
    Fake news. In: 7 Words from Political Scandals. https://www. merriam-webster.com/words-at-play/political-scandal-words/fake-news.
    [7]
    Shu K, Sliva A, Wang S, et al. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 2017, 19 (1): 22–36. doi: 10.1145/3137597.3137600
    [8]
    Ajao O, Bhowmik D, Zargari S. Sentiment aware fake news detection on online social networks. In: ICASSP 2019: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019: 2507–2511.
    [9]
    Wang W Y. "Liar, liar pants on fire": A new benchmark dataset for fake news detection. https://arxiv.org/abs/1705.00648.
    [10]
    Pérez-Rosas V, Kleinberg B, Lefevre A, et al. Automatic detection of fake news. https://arxiv.org/abs/1708.07104.
    [11]
    Cha M, Gao W, Li C T. Detecting fake news in social media: An Asia-Pacific perspective. Communications of the ACM, 2020, 63 (4): 68–71. doi: 10.1145/3378422
    [12]
    Ma J, Gao W, Mitra P, et al. Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). New York: IJCAI, 2016: 3818–3824.
    [13]
    Ma J, Gao W, Wong K F. Rumor detection on Twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics, 2018: 1980–1989.
    [14]
    Davis C A, Varol O, Ferrara E, et al. BotOrNot: A system to evaluate social bots. In: Proceedings of the 25th International Conference Companion on World Wide Web. Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2016: 273–274.
    [15]
    Yang X, Lyu Y, Tian T, et al. Rumor detection on social media with graph structured adversarial learning. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) . Virtual, Japan: IJCAI, 2020: 1417–1423.
    [16]
    Ferrara E, Varol O, Davis C, et al. The rise of social bots. Communications of the ACM, 2016, 59 (7): 96–104. doi: 10.1145/2818717
    [17]
    Zhao J, Cao N, Wen Z, et al. # FluxFlow: Visual analysis of anomalous information spreading on social media. IEEE Transactions on Visualization and Computer Graphics, 2014, 20 (12): 1773–1782. doi: 10.1109/TVCG.2014.2346922
    [18]
    Sun S, Liu H, He J, et al. Detecting event rumors on Sina Weibo automatically. In: Web Technologies and Applications. APWeb 2013. Berlin: Springer, 2013.
    [19]
    Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding. https:// arxiv.org/abs/1810.04805.
    [20]
    Yang Z, Dai Z, Yang Y, et al. XLNet: Generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Red Hook, NY: Curran Associates Inc., 2019, 32: 5753–5763.
    [21]
    Ke G, Meng Q, Finley T, et al. LightGBM: A highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems 30 (NIPS 2017). Red Hook, NY: Curran Associates Inc., 2017, 30: 3149–3157.
    [22]
    Song C, Yang C, Chen H, et al. CED: Credible early detection of social media rumors. IEEE Transactions on Knowledge and Data Engineering, 2021, 33 (8): 3035–3047. doi: 10.1109/TKDE.2019.2961675
    [23]
    Shu K, Mahudeswaran D, Wang S, et al. FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, 2020, 8 (3): 171–188. doi: 10.1089/big.2020.0062
    [24]
    Shu K, Wang S, Liu H. Exploiting tri-relationship for fake news detection. https://arxiv.org/abs/1712.07709.
    [25]
    Wang Y, Yang W, Ma F, et al. Weak supervision for fake news detection via reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (01): 516–523. doi: 10.1609/aaai.v34i01.5389
    [26]
    Rubin V L, Conroy N, Chen Y, et al. Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection. San Diego, California: Association for Computational Linguistics, 2016: 7–17.
    [27]
    Mihalcea R, Strapparava C. The lie detector: Explorations in the automatic recognition of deceptive language. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers. Suntec, Singapore: Association for Computational Linguistics, 2009: 309–312.
    [28]
    Castillo C, Mendoza M, Poblete B. Information credibility on Twitter. In: Proceedings of the 20th International Conference on World Wide Web. New York: ACM, 2011: 675–684.
    [29]
    Potthast M, Kiesel J, Reinartz K, et al. A stylometric inquiry into hyperpartisan and fake news. https://arxiv.org/abs/1702.05638.
    [30]
    Zhang X, Cao J, Li X, et al. Exploiting emotions for fake news detection on social media. https://arxiv.org/abs/1903.01728.
    [31]
    Przybyla P. Capturing the style of fake news. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (01): 490–497. doi: 10.1609/aaai.v34i01.5386
    [32]
    Popat K. Assessing the credibility of claims on the web. In: Proceedings of the 26th International Conference on World Wide Web Companion. Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2017: 735–739.
    [33]
    Potthast M, Kiesel J, Reinartz K, et al. A stylometric inquiry into hyperpartisan and fake news. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics, 2018: 231–240.
    [34]
    Chen T, Li X, Yin H, et al. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. In: Trends and Applications in Knowledge Discovery and Data Mining. Cham, Switzerland: Springer, 2018: 40–52.
    [35]
    Yang F, Liu Y, Yu X, et al. Automatic detection of rumor on Sina Weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. New York: ACM, 2012: 1-7.
    [36]
    Shu K, Zhou X, Wang S, et al. The role of user profiles for fake news detection. In: 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM, 2019: 436–439.
    [37]
    Liu Y, Wu Y F B. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 354–361.
    [38]
    Qian F, Gong C, Sharma K, et al. Neural user response generator: Fake news detection with collective user intelligence. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18). Stockholm, Sweden: IJCAI, 2018: 3834–3840.
    [39]
    Wu L, Liu H. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. New York: ACM, 2018: 637–645.
    [40]
    Monti F, Frasca F, Eynard D, et al. Fake news detection on social media using geometric deep learning. https://arxiv.org/abs/ 1902.06673.
    [41]
    Shu K, Mahudeswaran D, Wang S, et al. Hierarchical propagation networks for fake news detection: Investigation and exploitation. Proceedings of the International AAAI Conference on Web and Social Media, 2020, 14: 626–637.
    [42]
    Shu K, Wang S, Liu H. Beyond news contents: The role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. New York: ACM, 2019: 312–320.
    [43]
    Yuan C, Ma Q, Zhou W, et al. Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In: 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019: 796–805.
    [44]
    Sampson J, Morstatter F, Wu L, et al. Leveraging the implicit structure within social media for emergent rumor detection. In: Proceedings of the 25th ACM international on Conference on Information and Knowledge Management. New York: ACM, 2016: 2377–2382.
    [45]
    Friedman J H. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 2001, 29 (5): 1189–1232. doi: 10.1214/aos/1013203451
    [46]
    Hassan S, Rafi M, Shaikh M S. Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment. In: 2011 IEEE 14th International Multitopic Conference. IEEE, 2011: 31–34.
    [47]
    Li S, Zhao Z, Hu R, et al. Analogical reasoning on Chinese morphological and semantic relations. https://arxiv.org/abs/ 1805.06504.
    [48]
    Yu F, Liu Q, Wu S, et al. A convolutional approach for misinformation identification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia: IJCAI, 2017: 3901–3907.
  • 加载中

Catalog

    Figure  1.  A piece of fake news about taxes on social media Weibo.

    Figure  2.  An example of Sina Weibo’s official fake news busting service page.

    Figure  3.  Statistics of the annual number of fake news.

    Figure  4.  Statistics of the number of followers of fake news accounts.

    Figure  5.  Statistics of the influence of fake news.

    Figure  6.  The overall framework of our fake news detection model, which consists of the following steps:(i) The input consists of a variety of content of microblog and corresponding users’ information. (ii) We use the augmenter to augment the data of limited fake news microblogs. (iii)The original data and augmented data contextualized representations and users’ descriptions are obtained by Bert. (iv) These representations are concatenated and input to LightGBM for fake news detection.

    [1]
    Zhou Y Q. A Study of the Rumors in the Internet of Contemporary China. Beijing: The Commercial Press, 2012.
    [2]
    Allport G W, Postman L. The Psychology of Rumor. New York: Henry Holt, 1947.
    [3]
    Peterson W A, Gist N P. Rumor and public opinion. American Journal of Sociology, 1951, 57: 159–167. doi: 10.1086/220916
    [4]
    Liu F, Burton-Jones A, Xu D. Rumors on social media in disasters: Extending transmission to retransmission. In: Proceeding of the 19th Pacific Asia Conference on Information Systems (PACIS 2014), Chengdu, China. 2014: 49.
    [5]
    Tasnim S, Hossain M M, Mazumder H. Impact of rumors and misinformation on COVID-19 in social media. Journal of Preventive Medicine and Public Health, 2020, 53 (3): 171–174. doi: 10.3961/jpmph.20.094
    [6]
    Fake news. In: 7 Words from Political Scandals. https://www. merriam-webster.com/words-at-play/political-scandal-words/fake-news.
    [7]
    Shu K, Sliva A, Wang S, et al. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 2017, 19 (1): 22–36. doi: 10.1145/3137597.3137600
    [8]
    Ajao O, Bhowmik D, Zargari S. Sentiment aware fake news detection on online social networks. In: ICASSP 2019: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019: 2507–2511.
    [9]
    Wang W Y. "Liar, liar pants on fire": A new benchmark dataset for fake news detection. https://arxiv.org/abs/1705.00648.
    [10]
    Pérez-Rosas V, Kleinberg B, Lefevre A, et al. Automatic detection of fake news. https://arxiv.org/abs/1708.07104.
    [11]
    Cha M, Gao W, Li C T. Detecting fake news in social media: An Asia-Pacific perspective. Communications of the ACM, 2020, 63 (4): 68–71. doi: 10.1145/3378422
    [12]
    Ma J, Gao W, Mitra P, et al. Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). New York: IJCAI, 2016: 3818–3824.
    [13]
    Ma J, Gao W, Wong K F. Rumor detection on Twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics, 2018: 1980–1989.
    [14]
    Davis C A, Varol O, Ferrara E, et al. BotOrNot: A system to evaluate social bots. In: Proceedings of the 25th International Conference Companion on World Wide Web. Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2016: 273–274.
    [15]
    Yang X, Lyu Y, Tian T, et al. Rumor detection on social media with graph structured adversarial learning. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) . Virtual, Japan: IJCAI, 2020: 1417–1423.
    [16]
    Ferrara E, Varol O, Davis C, et al. The rise of social bots. Communications of the ACM, 2016, 59 (7): 96–104. doi: 10.1145/2818717
    [17]
    Zhao J, Cao N, Wen Z, et al. # FluxFlow: Visual analysis of anomalous information spreading on social media. IEEE Transactions on Visualization and Computer Graphics, 2014, 20 (12): 1773–1782. doi: 10.1109/TVCG.2014.2346922
    [18]
    Sun S, Liu H, He J, et al. Detecting event rumors on Sina Weibo automatically. In: Web Technologies and Applications. APWeb 2013. Berlin: Springer, 2013.
    [19]
    Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding. https:// arxiv.org/abs/1810.04805.
    [20]
    Yang Z, Dai Z, Yang Y, et al. XLNet: Generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Red Hook, NY: Curran Associates Inc., 2019, 32: 5753–5763.
    [21]
    Ke G, Meng Q, Finley T, et al. LightGBM: A highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems 30 (NIPS 2017). Red Hook, NY: Curran Associates Inc., 2017, 30: 3149–3157.
    [22]
    Song C, Yang C, Chen H, et al. CED: Credible early detection of social media rumors. IEEE Transactions on Knowledge and Data Engineering, 2021, 33 (8): 3035–3047. doi: 10.1109/TKDE.2019.2961675
    [23]
    Shu K, Mahudeswaran D, Wang S, et al. FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, 2020, 8 (3): 171–188. doi: 10.1089/big.2020.0062
    [24]
    Shu K, Wang S, Liu H. Exploiting tri-relationship for fake news detection. https://arxiv.org/abs/1712.07709.
    [25]
    Wang Y, Yang W, Ma F, et al. Weak supervision for fake news detection via reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (01): 516–523. doi: 10.1609/aaai.v34i01.5389
    [26]
    Rubin V L, Conroy N, Chen Y, et al. Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection. San Diego, California: Association for Computational Linguistics, 2016: 7–17.
    [27]
    Mihalcea R, Strapparava C. The lie detector: Explorations in the automatic recognition of deceptive language. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers. Suntec, Singapore: Association for Computational Linguistics, 2009: 309–312.
    [28]
    Castillo C, Mendoza M, Poblete B. Information credibility on Twitter. In: Proceedings of the 20th International Conference on World Wide Web. New York: ACM, 2011: 675–684.
    [29]
    Potthast M, Kiesel J, Reinartz K, et al. A stylometric inquiry into hyperpartisan and fake news. https://arxiv.org/abs/1702.05638.
    [30]
    Zhang X, Cao J, Li X, et al. Exploiting emotions for fake news detection on social media. https://arxiv.org/abs/1903.01728.
    [31]
    Przybyla P. Capturing the style of fake news. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (01): 490–497. doi: 10.1609/aaai.v34i01.5386
    [32]
    Popat K. Assessing the credibility of claims on the web. In: Proceedings of the 26th International Conference on World Wide Web Companion. Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2017: 735–739.
    [33]
    Potthast M, Kiesel J, Reinartz K, et al. A stylometric inquiry into hyperpartisan and fake news. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics, 2018: 231–240.
    [34]
    Chen T, Li X, Yin H, et al. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. In: Trends and Applications in Knowledge Discovery and Data Mining. Cham, Switzerland: Springer, 2018: 40–52.
    [35]
    Yang F, Liu Y, Yu X, et al. Automatic detection of rumor on Sina Weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. New York: ACM, 2012: 1-7.
    [36]
    Shu K, Zhou X, Wang S, et al. The role of user profiles for fake news detection. In: 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM, 2019: 436–439.
    [37]
    Liu Y, Wu Y F B. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 354–361.
    [38]
    Qian F, Gong C, Sharma K, et al. Neural user response generator: Fake news detection with collective user intelligence. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18). Stockholm, Sweden: IJCAI, 2018: 3834–3840.
    [39]
    Wu L, Liu H. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. New York: ACM, 2018: 637–645.
    [40]
    Monti F, Frasca F, Eynard D, et al. Fake news detection on social media using geometric deep learning. https://arxiv.org/abs/ 1902.06673.
    [41]
    Shu K, Mahudeswaran D, Wang S, et al. Hierarchical propagation networks for fake news detection: Investigation and exploitation. Proceedings of the International AAAI Conference on Web and Social Media, 2020, 14: 626–637.
    [42]
    Shu K, Wang S, Liu H. Beyond news contents: The role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. New York: ACM, 2019: 312–320.
    [43]
    Yuan C, Ma Q, Zhou W, et al. Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In: 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019: 796–805.
    [44]
    Sampson J, Morstatter F, Wu L, et al. Leveraging the implicit structure within social media for emergent rumor detection. In: Proceedings of the 25th ACM international on Conference on Information and Knowledge Management. New York: ACM, 2016: 2377–2382.
    [45]
    Friedman J H. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 2001, 29 (5): 1189–1232. doi: 10.1214/aos/1013203451
    [46]
    Hassan S, Rafi M, Shaikh M S. Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment. In: 2011 IEEE 14th International Multitopic Conference. IEEE, 2011: 31–34.
    [47]
    Li S, Zhao Z, Hu R, et al. Analogical reasoning on Chinese morphological and semantic relations. https://arxiv.org/abs/ 1805.06504.
    [48]
    Yu F, Liu Q, Wu S, et al. A convolutional approach for misinformation identification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia: IJCAI, 2017: 3901–3907.

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