Efficient decision support for detecting content polluters on social networks: an approach based on automatic knowledge acquisition from behavioral patterns

Byung Joon Park () and Jin Seop Han ()
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Byung Joon Park: Kwangwoon University
Jin Seop Han: Kwangwoon University

Information Technology and Management, 2016, vol. 17, issue 1, No 9, 95-105

Abstract: Abstract With the rise of social networking services such as Facebook and Twitter, the problem of spam and content pollution has become more significant and intractable. Using social networking services, users are able to develop relationships and share messages with others in a very convenient manner; however, they are vulnerable to receiving spam messages. The automatic detection of spammers or content polluters on the network can effectively reduce the burden on the service provider in making a decision on appropriate counteractions. Content polluters can be automatically identified by using the supervised learning technique of artificial intelligence. To build a classification model with high accuracy automatically from the training data set, it is important to identify a set of useful features that can classify polluters and non-polluters. Moreover, because we deal with a huge amount of raw data in this process, the efficiency of data preparation and model creation are also critical issues that need to be addressed. In this paper, we present an efficient method for detecting content polluters on Twitter. Specifically, we propose a set of features that can be easily extracted from the messages and behaviors of Twitter users and construct a new breed of classifiers based on these features. The proposed approach requires only a minimal number of feature values per Twitter user and thus adds considerably less time to the overall mining process compared to other methods. Experiments confirm that the proposed approach outperforms previous approaches in both classification accuracy and processing time.

Keywords: Decision support; Automatic knowledge acquisition; Social network; Content polluters; Machine learning (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10799-015-0250-4

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