{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T05:14:30Z","timestamp":1751951670516,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,4,20]]},"DOI":"10.1145\/3366424.3382184","type":"proceedings-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T08:10:56Z","timestamp":1588579856000},"page":"331-336","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Using Deep Learning for Temporal Forecasting of User Activity on Social Media: Challenges and Limitations"],"prefix":"10.1145","author":[{"given":"Anthony","family":"Hernandez","sequence":"first","affiliation":[{"name":"University of South Florida"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kin","family":"Ng","sequence":"additional","affiliation":[{"name":"University of South Florida"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriana","family":"Iamnitchi","sequence":"additional","affiliation":[{"name":"University of South Florida"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature03459"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems -","volume":"1","author":"Bengio Samy","year":"2015","unstructured":"Samy Bengio , Oriol Vinyals , Navdeep Jaitly , and Noam Shazeer . 2015 . Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks . In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 (Montreal, Canada) (NIPS\u201915). MIT Press, Cambridge, MA, USA, 1171\u20131179. Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 (Montreal, Canada) (NIPS\u201915). MIT Press, Cambridge, MA, USA, 1171\u20131179."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_3_2_1_4_1","unstructured":"Meeyoung Cha Hamed Haddadi Fabricio Benevenuto and Krishna\u00a0P Gummadi. 2010. Measuring user influence in twitter: The million follower fallacy. In fourth international AAAI conference on weblogs and social media.  Meeyoung Cha Hamed Haddadi Fabricio Benevenuto and Krishna\u00a0P Gummadi. 2010. Measuring user influence in twitter: The million follower fallacy. In fourth international AAAI conference on weblogs and social media."},{"key":"e_1_3_2_1_5_1","unstructured":"Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR.  Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. ICLR."},{"key":"e_1_3_2_1_6_1","volume-title":"NIPS 2014 Workshop on Deep Learning","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , Kyunghyun Cho , and Yoshua Bengio . 2014 . Empirical evaluation of gated recurrent neural networks on sequence modeling . In NIPS 2014 Workshop on Deep Learning , December 2014. Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157527"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2942853"},{"key":"e_1_3_2_1_10_1","volume-title":"Kingma and Jimmy Ba","author":"P.","year":"2014","unstructured":"Diederik\u00a0 P. Kingma and Jimmy Ba . 2014 . Adam : A Method for Stochastic Optimization. CoRR abs\/1412.6980(2014). Diederik\u00a0P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR abs\/1412.6980(2014)."},{"key":"e_1_3_2_1_11_1","volume-title":"Kipf and Max Welling","author":"N.","year":"2017","unstructured":"Thomas\u00a0 N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks . Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks."},{"volume-title":"Proceedings of the 2014 SIAM International Conference on Data Mining. 289\u2013297","author":"Li Xiaoyi","key":"e_1_3_2_1_12_1","unstructured":"Xiaoyi Li , Nan Du , Hui Li , Kang Li , Jing Gao , and Aidong Zhang . [n.d.]. A Deep Learning Approach to Link Prediction in Dynamic Networks . In Proceedings of the 2014 SIAM International Conference on Data Mining. 289\u2013297 . Xiaoyi Li, Nan Du, Hui Li, Kang Li, Jing Gao, and Aidong Zhang. [n.d.]. A Deep Learning Approach to Link Prediction in Dynamic Networks. In Proceedings of the 2014 SIAM International Conference on Data Mining. 289\u2013297."},{"key":"e_1_3_2_1_13_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR \u201918)","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li , Rose Yu , Cyrus Shahabi , and Yan Liu . 2018 . Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR \u201918) . Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR \u201918)."},{"key":"e_1_3_2_1_14_1","volume-title":"Retrieved","author":"S.","year":"2020","unstructured":"U. S. \u00a0Department of Commerce. 2020. Introduction to Bayesian Statistics . Retrieved February 16, 2020 from https:\/\/nvd.nist.gov\/ U.S.\u00a0Department of Commerce. 2020. Introduction to Bayesian Statistics. Retrieved February 16, 2020 from https:\/\/nvd.nist.gov\/"},{"key":"e_1_3_2_1_15_1","volume-title":"DeepInf. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD \u201918(2018)","author":"Qiu Jiezhong","year":"2018","unstructured":"Jiezhong Qiu , Jian Tang , Hao Ma , Yuxiao Dong , Kuansan Wang , and Jie Tang . 2018 . DeepInf. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD \u201918(2018) . Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018. DeepInf. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD \u201918(2018)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_2_1_18_1","volume-title":"How Syria\u2019s White Helmets became victims of an online propaganda machine. The Guardian (Dec","author":"Solon Olivia","year":"2017","unstructured":"Olivia Solon . 2017. How Syria\u2019s White Helmets became victims of an online propaganda machine. The Guardian (Dec 2017 ). Olivia Solon. 2017. How Syria\u2019s White Helmets became victims of an online propaganda machine. The Guardian (Dec 2017)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359229"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems -","volume":"2","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc\u00a0 V. Le . 2014 . Sequence to Sequence Learning with Neural Networks . In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS\u201914). MIT Press, Cambridge, MA, USA, 3104\u20133112. Ilya Sutskever, Oriol Vinyals, and Quoc\u00a0V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS\u201914). MIT Press, Cambridge, MA, USA, 3104\u20133112."},{"key":"e_1_3_2_1_21_1","unstructured":"Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang and Philip\u00a0S. Yu. 2019. A Comprehensive Survey on Graph Neural Networks. (2019). arXiv:arXiv  Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang and Philip\u00a0S. Yu. 2019. A Comprehensive Survey on Graph Neural Networks. (2019). arXiv:arXiv"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783401"},{"key":"e_1_3_2_1_24_1","unstructured":"Jie Zhou Ganqu Cui Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li and Maosong Sun. 2018. Graph Neural Networks: A Review of Methods and Applications. (2018). arXiv:arXiv  Jie Zhou Ganqu Cui Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li and Maosong Sun. 2018. Graph Neural Networks: A Review of Methods and Applications. (2018). arXiv:arXiv"}],"event":{"name":"WWW '20: The Web Conference 2020","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Taipei Taiwan","acronym":"WWW '20"},"container-title":["Companion Proceedings of the Web Conference 2020"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3366424.3382184","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3366424.3382184","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:11Z","timestamp":1750199591000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3366424.3382184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":24,"alternative-id":["10.1145\/3366424.3382184","10.1145\/3366424"],"URL":"https:\/\/doi.org\/10.1145\/3366424.3382184","relation":{},"subject":[],"published":{"date-parts":[[2020,4,20]]},"assertion":[{"value":"2020-04-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}