2015/03/25 by Bo Wang, Wang, Bo, Arkaitz Zubiaga +5
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Spam and Phishing Detection #Text and Document Classification Technologies #cs.IR #cs.SI
paper · pdf · doi:10.48550/arxiv.1503.07405
arxiv created 2015/03/25 · openalex publication_date 2015/03/25 · arxiv updated 2015/03/26 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Social spam produces a great amount of noise on social media services such as Twitter, which reduces the signal-to-noise ratio that both end users and data mining applications observe. Existing techniques on social spam detection have focused primarily on the identification of spam accounts by using extensive historical and network-based data. In this paper we focus on the detection of spam tweets, which optimises the amount of data that needs to be gathered by relying only on tweet-inherent features. This enables the application of the spam detection system to a large set of tweets in a timely fashion, potentially applicable in a real-time or near real-time setting. Using two large hand-labelled datasets of tweets containing spam, we study the suitability of five classification algorithms and four different feature sets to the social spam detection task. Our results show that, by using the limited set of features readily available in a tweet, we can achieve encouraging results which are competitive when compared against existing spammer detection systems that make use of additional, costly user features. Our study is the first that attempts at generalising conclusions on the optimal classifiers and sets of features for social spam detection over different datasets.