Discovery of Topic Derivative Relationship in Social Networks
Author:
Affiliation:
1. School of Computer and Software Engineering, Xihua University, Chengdu, China
Funder
National Natural Science Foundation
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3438872.3439096
Reference16 articles.
1. Arkaitz Zubiaga Ahmet Aker Kalina Bontcheva Maria Liakata and Rob Procter. 2018. Detection and Resolution of Rumours in Social Media: A Survey.ACM Comput. Surv.51 2 Article 32 (June 2018) 36 pages. DOI:https://doi.org/10.1145/3161603 Arkaitz Zubiaga Ahmet Aker Kalina Bontcheva Maria Liakata and Rob Procter. 2018. Detection and Resolution of Rumours in Social Media: A Survey.ACM Comput. Surv.51 2 Article 32 (June 2018) 36 pages. DOI:https://doi.org/10.1145/3161603
2. Garg Muskan & Kumar Mukesh. 2018. Identifying Influential Segments from Word Co-occurrence Networks using AHP. Cognitive Systems Research. 28--41 DOI:https://doi.org/10.1016/j.cogsys.2017.07.003. Garg Muskan & Kumar Mukesh. 2018. Identifying Influential Segments from Word Co-occurrence Networks using AHP. Cognitive Systems Research. 28--41 DOI:https://doi.org/10.1016/j.cogsys.2017.07.003.
3. Research on Topic Detection and Tracking for Online News Texts
4. Wang Hongning Zhai ChengXiang. 2017 Generative Models for Sentiment Analysis and Opinion Mining. A Practical Guide to Sentiment Analysis 107- 134. DOI: 10.1007/978-3-319-55394-8_6. 10.1007/978-3-319-55394-8_6
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