{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:49:02Z","timestamp":1767923342269,"version":"3.49.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T00:00:00Z","timestamp":1524528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61403390, 61772528, U1435221"],"award-info":[{"award-number":["61403390, 61772528, U1435221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2016YFB1001000"],"award-info":[{"award-number":["2016YFB1001000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>With the rapid growth of social media, massive misinformation is also spreading widely on social media, e.g., Weibo and Twitter, and brings negative effects to human life. Today, automatic misinformation identification has drawn attention from academic and industrial communities. Whereas an event on social media usually consists of multiple microblogs, current methods are mainly constructed based on global statistical features. However, information on social media is full of noise, which should be alleviated. Moreover, most of the microblogs about an event have little contribution to the identification of misinformation, where useful information can be easily overwhelmed by useless information. Thus, it is important to mine significant microblogs for constructing a reliable misinformation identification method. In this article, we propose an attention-based approach for identification of misinformation (AIM). Based on the attention mechanism, AIM can select microblogs with the largest attention values for misinformation identification. The attention mechanism in AIM contains two parts: content attention and dynamic attention. Content attention is the calculated-based textual features of each microblog. Dynamic attention is related to the time interval between the posting time of a microblog and the beginning of the event. To evaluate AIM, we conduct a series of experiments on the Weibo and Twitter datasets, and the experimental results show that the proposed AIM model outperforms the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3173458","type":"journal-article","created":{"date-parts":[[2018,4,25]],"date-time":"2018-04-25T12:22:17Z","timestamp":1524658937000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Mining Significant Microblogs for Misinformation Identification"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9233-3827","authenticated-orcid":false,"given":"Qiang","family":"Liu","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences 8 University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Feng","family":"Yu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences 8 University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Shu","family":"Wu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences 8 University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences 8 University of Chinese Academy of Sciences"}]}],"member":"320","published-online":{"date-parts":[[2018,4,24]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Ba Jimmy","year":"2015","unstructured":"Jimmy Ba , Volodymyr Mnih , and Koray Kavukcuoglu . 2015 . Multiple object recognition with visual attention . In Proceedings of the International Conference on Learning Representations. Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. 2015. Multiple object recognition with visual attention. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . Neural machine translation by jointly learning to align and translate . In Proceedings of the International Conference on Learning Representations. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963500"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_5_1","unstructured":"Kan Chen Jiang Wang Liang-Chieh Chen Haoyuan Gao Wei Xu and Ram Nevatia. 2015. ABC-CNN: An attention based convolutional neural network for visual question answering. arXiv:1511.05960.  Kan Chen Jiang Wang Liang-Chieh Chen Haoyuan Gao Wei Xu and Ram Nevatia. 2015. ABC-CNN: An attention based convolutional neural network for visual question answering. arXiv:1511.05960."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems. 577--585","author":"Chorowski Jan K.","year":"2015","unstructured":"Jan K. Chorowski , Dzmitry Bahdanau , Dmitriy Serdyuk , Kyunghyun Cho , and Yoshua Bengio . 2015 . Attention-based models for speech recognition . In Proceedings of the Conference on Neural Information Processing Systems. 577--585 . Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio. 2015. Attention-based models for speech recognition. In Proceedings of the Conference on Neural Information Processing Systems. 577--585."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the International Conference on Machine Learning. 2067--2075","author":"Chung Junyoung","year":"2015","unstructured":"Junyoung Chung , Caglar G\u00fcl\u00e7ehre , Kyunghyun Cho , and Yoshua Bengio . 2015 . Gated feedback recurrent neural networks . In Proceedings of the International Conference on Machine Learning. 2067--2075 . Junyoung Chung, Caglar G\u00fcl\u00e7ehre, Kyunghyun Cho, and Yoshua Bengio. 2015. Gated feedback recurrent neural networks. In Proceedings of the International Conference on Machine Learning. 2067--2075."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1168"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1920331.1920416"},{"key":"e_1_2_1_10_1","unstructured":"Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv:1308.0850.  Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv:1308.0850."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487788.2488033"},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the 3rd International Conference on Document Analysis and Recognition","volume":"1","author":"Ho Tin Kam","year":"1995","unstructured":"Tin Kam Ho . 1995 . Random decision forests . In Proceedings of the 3rd International Conference on Document Analysis and Recognition , Vol. 1 . IEEE, Los Alamitos, CA, 278--282. Tin Kam Ho. 1995. Random decision forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Vol. 1. IEEE, Los Alamitos, CA, 278--282."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.730558"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.91"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2972--2978","author":"Jin Zhiwei","year":"2016","unstructured":"Zhiwei Jin , Juan Cao , Yongdong Zhang , and Jiebo Luo . 2016 . News verification by exploiting conflicting social viewpoints in microblogs . In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2972--2978 . Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. 2016. News verification by exploiting conflicting social viewpoints in microblogs. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2972--2978."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1086"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883085"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.61"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the International Conference on Machine Learning","volume":"14","author":"Quoc","unstructured":"Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents . In Proceedings of the International Conference on Machine Learning , Vol. 14 . 1188--1196. Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the International Conference on Machine Learning, Vol. 14. 1188--1196."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0135"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2661760"},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI\u201916)","author":"Liu Qiang","year":"2016","unstructured":"Qiang Liu , Shu Wu , Liang Wang , and Tieniu Tan . 2016 . Predicting the next location: A recurrent model with spatial and temporal contexts . In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI\u201916) . 194--200. Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI\u201916). 194--200."},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the Conference on Empirical Methods on Natural Language Processing. 1412--1421","author":"Luong Thang","unstructured":"Thang Luong , Hieu Pham , and Christopher D. Manning . 2015. Effective approaches to attention-based neural machine translation . In Proceedings of the Conference on Empirical Methods on Natural Language Processing. 1412--1421 . Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the Conference on Empirical Methods on Natural Language Processing. 1412--1421."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence. 3818--3824","author":"Ma Jing","year":"2016","unstructured":"Jing Ma , Wei Gao , Prasenjit Mitra , Sejeong Kwon , Bernard J. Jansen , Kam-Fai Wong , and Meeyoung Cha . 2016 . Detecting rumors from microblogs with recurrent neural networks . In Proceedings of the International Joint Conference on Artificial Intelligence. 3818--3824 . Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. In Proceedings of the International Joint Conference on Artificial Intelligence. 3818--3824."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806607"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2010-343"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947611"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS\u201913)","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov , Ilya Sutskever , Kai Chen , Greg S. Corrado , and Jeff Dean . 2013 . Distributed representations of words and phrases and their compositionality . In Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS\u201913) . 3111--3119. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS\u201913). 3111--3119."},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 27th Conference on Neural Information Processing Systems (NIPS\u201914)","author":"Mnih Volodymyr","year":"2014","unstructured":"Volodymyr Mnih , Nicolas Heess , Alex Graves , and Koray Kavukcuoglu . 2014 . Recurrent models of visual attention . In Proceedings of the 27th Conference on Neural Information Processing Systems (NIPS\u201914) . 2204--2212. Volodymyr Mnih, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu. 2014. Recurrent models of visual attention. In Proceedings of the 27th Conference on Neural Information Processing Systems (NIPS\u201914). 2204--2212."},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the Conference on Empirical Methods on Natural Language Processing. 1589--1599","author":"Qazvinian Vahed","year":"2011","unstructured":"Vahed Qazvinian , Emily Rosengren , Dragomir R. Radev , and Qiaozhu Mei . 2011 . Rumor has it: Identifying misinformation in microblogs . In Proceedings of the Conference on Empirical Methods on Natural Language Processing. 1589--1599 . Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, and Qiaozhu Mei. 2011. Rumor has it: Identifying misinformation in microblogs. In Proceedings of the Conference on Empirical Methods on Natural Language Processing. 1589--1599."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-7373(87)80053-6"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 8th International Conference on Weblogs and Social Media.","author":"Rieh Soo Young","year":"2014","unstructured":"Soo Young Rieh , Grace YoungJoo Jeon , Ji Yeon Yang , and Christopher Lampe . 2014 . Audience-aware credibility: From understanding audience to establishing credible blogs . In Proceedings of the 8th International Conference on Weblogs and Social Media. Soo Young Rieh, Grace YoungJoo Jeon, Ji Yeon Yang, and Christopher Lampe. 2014. Audience-aware credibility: From understanding audience to establishing credible blogs. In Proceedings of the 8th International Conference on Weblogs and Social Media."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1044"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1123"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP\u201916)","author":"Wang Yequan","unstructured":"Yequan Wang , Minlie Huang , Li Zhao , and Xiaoyan Zhu . Attention-based LSTM for aspect-level sentiment classification . In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP\u201916) . Yequan Wang, Minlie Huang, Li Zhao, and Xiaoyan Zhu. Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP\u201916)."},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence. 4403--4404","author":"Wu Shu","year":"2016","unstructured":"Shu Wu , Qiang Liu , Yong Liu , Liang Wang , and Tieniu Tan . 2016 . Information credibility evaluation on social media . In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 4403--4404 . Shu Wu, Qiang Liu, Yong Liu, Liang Wang, and Tieniu Tan. 2016. Information credibility evaluation on social media. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 4403--4404."},{"key":"e_1_2_1_37_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 842--850","author":"Xiao Tianjun","year":"2015","unstructured":"Tianjun Xiao , Yichong Xu , Kuiyuan Yang , Jiaxing Zhang , Yuxin Peng , and Zheng Zhang . 2015 . The application of two-level attention models in deep convolutional neural network for fine-grained image classification . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 842--850 . Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, and Zheng Zhang. 2015. The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 842--850."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_28"},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the International Conference on Machine Learning. 2048--2057","author":"Xu Kelvin","year":"2015","unstructured":"Kelvin Xu , Jimmy Ba , Ryan Kiros , Kyunghyun Cho , Aaron Courville , Ruslan Salakhudinov , Rich Zemel , and Yoshua Bengio . 2015 . Show, attend and tell: Neural image caption generation with visual attention . In Proceedings of the International Conference on Machine Learning. 2048--2057 . Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the International Conference on Machine Learning. 2048--2057."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2350190.2350203"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.10"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1174"},{"key":"e_1_2_1_43_1","unstructured":"Wenpeng Yin Sebastian Ebert and Hinrich Sch\u00fctze. 2016. Attention-based convolutional neural network for machine comprehension. arXiv:1602.04341.  Wenpeng Yin Sebastian Ebert and Hinrich Sch\u00fctze. 2016. Attention-based convolutional neural network for machine comprehension. arXiv:1602.04341."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741637"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2034"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3173458","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3173458","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:02:45Z","timestamp":1750215765000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3173458"}},"subtitle":["An Attention-Based Approach"],"short-title":[],"issued":{"date-parts":[[2018,4,24]]},"references-count":45,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018,9,30]]}},"alternative-id":["10.1145\/3173458"],"URL":"https:\/\/doi.org\/10.1145\/3173458","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,24]]},"assertion":[{"value":"2017-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-04-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}