Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection
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DOI: 10.1007/s10660-016-9244-5
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- Yindalon Aphinyanaphongs & Lawrence D. Fu & Zhiguo Li & Eric R. Peskin & Efstratios Efstathiadis & Constantin F. Aliferis & Alexander Statnikov, 2014. "A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(10), pages 1964-1987, October.
- Sara Owsley Sood & Elizabeth F. Churchill & Judd Antin, 2012. "Automatic identification of personal insults on social news sites," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(2), pages 270-285, February.
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Cited by:
- Guo Li & Na Li, 2019. "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, Springer, vol. 19(4), pages 779-800, December.
- Martin Reisenbichler & Thomas Reutterer, 2019. "Topic modeling in marketing: recent advances and research opportunities," Journal of Business Economics, Springer, vol. 89(3), pages 327-356, April.
- Martin Kuchta & Linda Vaskova & Andrej Miklosik, 2020. "Facebook Explore Feed: Perception and Consequences of the Experiment," The Review of Socionetwork Strategies, Springer, vol. 14(1), pages 93-107, April.
- Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
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Keywords
Social spam; Spam detection; Topic modeling; Incremental learning; Machine learning; Big data;All these keywords.
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