Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 291-296.doi: 10.11896/jsjkx.210800011
• Big Data & Data Science • Previous Articles Next Articles
XIE Bai-lin, LI Qi, KUANG Jiang
CLC Number:
[1] YE S,WU S F.Measuring message propagation and social influence on Twitter.com[C]//Proceedings of the Second International Conference on Social Informatics.2010:216-231. [2] GUILLE A,HACID H,FAVRE C,et al.Information diffusion in online social networks:A survey[J].ACM Sigmod Record,2013,42(2):17-28. [3] WESTERMAN D,SPENCE P R,VAN DER HEIDE B.A social network as information:The effect of system generated reports of connectedness on credibility on Twitter[J].Computers in Human Behavior,2012,28(1):199-206. [4] HONG L,DAN O,DAVISON B D.Predicting popular messages in twitter[C]//Proceedings of the 20th International Conference Companion on World Wide Web.ACM,2011:57-58. [5] BANDARI R,ASUR S,HUBERMAN B A.The pulse of news in social media:Forecasting popularity[C]//Sixth International AAAI Conference on Weblogs and Social Media.2012:26-33. [6] NAVEED N,GOTTRON T,KUNEGIS J,et al.Bad news travel fast:A content-based analysis of interestingness on twitter[C]//Proceedings of the 3rd International Web Science Confe-rence.ACM,2011:1-7. [7] PENG H K,ZHU J,PIAO D,et al.Retweet modeling using conditional random fields[C]//2011 IEEE 11th International Conference on Data Mining Workshops.IEEE,2011:336-343. [8] GAO S,MA J,CHEN Z.Popularity prediction in microblogging network[C]//Asia-Pacific Web Conference.Cham:Springer,2014:379-390. [9] ZHU H L,YUN X C,HAN Z S.Weibo Popularity PredictionMethod Based on Propagation Acceleration[J].Journal of Computer Research and Development,2018,55(6):1282-1293. [10] BAO P,SHEN H W,HUANG J,et al.Popularity prediction in microblogging network:a case study on sina weibo[C]//Proceedings of the 22nd International Conference on World Wide Web.ACM,2013:177-178. [11] GAO S,MA J,CHEN Z.Modeling and predicting retweeting dynamics on microblogging platforms[C]//Proceedings of the Eighth ACM International Conference on Web Search and Data Mining.ACM,2015:107-116. [12] CAO Q,SHEN H,GAO H,et al.Predicting the popularity of online content with group-specific models[C]//Proceedings of the 26th International Conference on World Wide Web Compa-nion.International World Wide Web Conferences Steering Committee.2017:765-766. [13] GAO X,CAO Z,LI S,et al.Taxonomy and Evaluation for Microblog Popularity Prediction[J].ACM Transactions on Know-ledge Discovery from Data(TKDD),2019,13(2):15-54. [14] WANG X M,FANG B X,ZHANG H L,et al.TSL:predicting popularity of Facebook content based on tie strength[J].Journal on Communications,2019,40(10):1-9. [15] XIE J Y,ZHU Y C,ZHANG Z B,et al.A Multimodal Variational Encoder-Decoder Framework for Micro-video Popularity Prediction[C]//Proceedings of the Web Conference 2020.2020:2542-2548. [16] YU S Z.Hidden semi-Markov models[J].Artificial intelligence,2010,174(2):215-243. [17] YU S Z,KOBAYASHI H.An efficient forward-backward algorithm for an explicit-duration hidden Markov model[J].IEEE Signal Processing Letters,2003,10(1):11-14. [18] RABINER L R.A tutorial on hidden Markov models and selec-ted applications in speech recognition[J].Proceedings of the IEEE,1989,77(2):257-286. [19] ZHOU F,XU X,TRAJCEVSKI G,et al.A survey of information cascade analysis:Models,predictions,and recent advances[J].ACM Computing Surveys(CSUR),2021,54(2):1-36. [20] LIU Y,ZHAO J,XIAO Y.C-RBFNN:A user retweet behavior prediction method for hotspot topics based on improved RBF neural network[J].Neurocomputing,2018,275:733-746. [21] YIN H,YANG S,SONG X,et al.Deep fusion of multimodal features for social media retweet time prediction[J].World Wide Web,2020,24(4):1027-1044. [22] ROY S,SUMAN B K,CHANDRA J,et al.Forecasting the Future:Leveraging RNN based Feature Concatenation for Tweet Outbreak Prediction[C]//Proceedings of the 7th ACM IKDD CoDS and 25th COMAD.2020:219-223. [23] LYMPEROPOULOS I N.RC-Tweet:Modeling and predictingthe popularity of tweets through the dynamics of a capacitor[J].Expert Systems with Applications,2021,163:113785. [24] XIAO C,LIU C,MA Y,et al.Time sensitivity-based popularity prediction for online promotion on Twitter[J].Information Sciences,2020,525:82-92. [25] ZHANG Z,YIN Z,WEN J,et al.DeepBlue:Bi-layered LSTM for tweet popularity Estimation[J/OL].IEEE Transactions on Knowledge and Data Engineering,2021.https://ieeexplore.ieee.org/abstract/document/9314897. [26] XIE Y.An efficient algorithm for parameterizing HsMM withGaussian and Gamma distributions[J].Information Processing Letters,2012,112(19):732-737. [27] CHEN L,DENG H.Predicting User Retweeting Behavior in Social Networks With a Novel Ensemble Learning Approach[C]//IEEE Access.2020:148250-148263. [28] SHANG J,HUANG S,ZHANG D,et al.RNe2Vec:information diffusion popularity prediction based on repost network embedding[J].Computing,2021,103(2):271-289. [29] CAO Q,SHEN H,GAO J,et al.Popularity prediction on social platforms with coupled graph neural networks[C]//Proceedings of the 13th International Conference on Web Search and Data Mining.2020:70-78. [30] ZHOU F,YU L,XU X,et al.Decoupling Representation andRegressor for Long-Tailed Information Cascade Prediction[C]//Proceedings of the 44thInternational ACM SIGIR Conference on Research and Development in Information Retrieval.2021:1875-1879. |
[1] | XU Jian-min, SUN Peng, WU Shu-fang. Microblog Rumor Detection Method Based on Propagation Path Tree Kernel Learning [J]. Computer Science, 2022, 49(6): 342-349. |
[2] | SHI Wei, FU Yue. Microblog Short Text Mining Considering Context:A Method of Sentiment Analysis [J]. Computer Science, 2021, 48(6A): 158-164. |
[3] | LIU Zhong-hui, ZHAO Qi, ZOU Lu, MIN Fan. Heuristic Construction of Triadic Concept and Its Application in Social Recommendation [J]. Computer Science, 2021, 48(6): 234-240. |
[4] | HAN Li-feng, CHEN Li. User Cold Start Recommendation Model Integrating User Attributes and Item Popularity [J]. Computer Science, 2021, 48(2): 114-120. |
[5] | WANG Xiao-han, TAN Chen-chen, XIANG Yan, YU Zheng-tao. Aspect Extraction of Case Microblog Based on Double Embedded Convolutional Neural Network [J]. Computer Science, 2021, 48(12): 319-323. |
[6] | ZHANG Zhi-yang, ZHANG Feng-li, TAN Qi, WANG Rui-jin. Review of Information Cascade Prediction Methods Based on Deep Learning [J]. Computer Science, 2020, 47(7): 141-153. |
[7] | LIU Yu-dong, SUN Hao, JIANG Yun-cheng. Personalized Microblog Recommendation Model Integrating Content Similarity and Multi-feature Computing [J]. Computer Science, 2020, 47(10): 97-101. |
[8] | WANG Xin-sheng,MA Shu-zhang. Method of Weibo User Influence Calculation Integrating Users’ Own Factors and Interaction Behavior [J]. Computer Science, 2020, 47(1): 96-101. |
[9] | LUO Jian-zhen,CAI Jun ,LIU Yan,ZHAO Hui-min. Caching and Replacing Strategy in Information-centric Network Based on Content Popularity and Community Importance [J]. Computer Science, 2018, 45(7): 116-121. |
[10] |
HE Ji-xing,CHEN Wen-bin,MOU Bin-hao.
Coordination Filtering Personalized Recommendation Algorithm Considering Average Preference Weight and Popularity Division [J]. Computer Science, 2018, 45(6A): 493-496. |
[11] | CHEN Zhi-xiong, WANG Shi-hui and GAO Rong. Recognition Model of Microblog Opinion Leaders Based on Sentiment Orientation Analysis [J]. Computer Science, 2018, 45(5): 168-175. |
[12] | XIA Chong-huan, LI Hua-kang, SUN Guo-zi. Microblogging Malicious User Identification Based on Behavior Characteristic Analysis [J]. Computer Science, 2018, 45(12): 111-116. |
[13] | HUANG Xian-ying, YANG Lin-feng, LIU Xiao-yang. Information Dissemination and Mathematical Modeling of Microblog under Graded Opinion Leader [J]. Computer Science, 2018, 45(11): 261-266. |
[14] | WANG Zhen-fei, LIU Kai-li, ZHENG Zhi-yun and WANG Fei. Research on Evolution Model of Microblog Topic Based on Time Sequence [J]. Computer Science, 2017, 44(8): 270-273. |
[15] | WANG Zhen-fei, ZHU Jing-yang, ZHENG Zhi-yun and SONG Yu. Analysis of Microblog Community Users’ Influence Based on R-C Model [J]. Computer Science, 2017, 44(3): 254-258. |
|