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在“SpringerLink电子期刊”中，命中：194条，耗时：0.0139931 秒

在所有数据库中总计命中：328条

1. Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection

作者： Long Song ; Raymond Yiu Keung Lau ; Ron Chi-Wai Kwok…

关键词：Social spam ; Spam detection ; Topic modeling ; Incremental learning ; Machine learning ; Big data

刊名：Electronic Commerce Research

出版年：2017

2. A hybrid spam detection method based on unstructured datasets

作者： Yeqin Shao ; Marcello Trovati ; Quan Shi ; Olga Angelopoulou…

关键词：Image spam ; Text spam ; Semantic networks ; Classification ; Subclass discriminant analysis ; Feature selection ; Sparse representation

刊名：Soft Computing

出版年：2017

3. A Multi-criteria Decision Making Approach for the Assessment of Information Credibility in Social Media

关键词：Credibility assessment ; Opinion spam detection ; Social Media ; Multi ; Criteria Decision Making ; OWA aggregation operators

刊名：Lecture Notes in Computer Science

出版年：2017

4. If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

作者： Abdullah Almaatouq ; Erez Shmueli…

关键词：Online social networks ; Microblogging ; Account abuse ; Spam detection ; Spam analysis

刊名：International Journal of Information Security

出版年：2016

5. LSSL-SSD: Social Spammer Detection with Laplacian Score and Semi-supervised Learning

关键词：Social networks ; Spammer detection ; Laplacian score ; Feature selection ; Semi ; supervised learning

刊名：Lecture Notes in Computer Science

出版年：2016

关键词：Spammer detection ; Health ; Chinese ; Weibo ; Deep belief network

刊名：Lecture Notes in Computer Science

出版年：2016

7. Term Space Partition Based Ensemble Feature Construction for Spam Detection

关键词：Term space partition (TSP) ; Ensemble term space partition (ETSP) ; Feature construction ; Spam detection ; Text categorization

刊名：Lecture Notes in Computer Science

出版年：2016

8. User Preference-Based Spamming Detection with Coupled Behavioral Analysis

关键词：PageRank ; Multi ; class classification ; Support vector machines ; Spam detection ; Behavioral analysis

刊名：Lecture Notes in Computer Science

出版年：2016

9. An Ensemble Learning Approach for Addressing the Class Imbalance Problem in Twitter Spam Detection

关键词：Online social networks ; Twitter spam ; Machine learning ; Class imbalance

刊名：Lecture Notes in Computer Science

出版年：2016

10. A Social Spam Detection Framework via Semi-supervised Learning

关键词：Semi ; supervised learning ; Social spam ; Co ; training ; k ; medoids

刊名：Lecture Notes in Computer Science

出版年：2016

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