期刊
INFORMATION SCIENCES
卷 332, 期 -, 页码 72-83出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2015.10.039
关键词
Account classification; Wavelets; Online social networks; Multilayer perceptrons; Random forests
资金
- CNPq [479821/2013-5, 306811/2014-6]
- FAP [064/2013 - 33785]
We developed a wavelet-based approach for account classification that detects textual dissemination by bots on an Online Social Network (OSN). Its main objective is to match account patterns with humans, cyborgs or robots, improving the existing algorithms that automatically detect frauds. With a computational cost suitable for OSNs, the proposed approach analyses the distribution of key terms. The descriptors, a wavelet-based feature vector for each user's account, work in conjunction with a new weighting scheme, called Lexicon Based Coefficient Attenuation (LBCA) and serve as inputs to one of the classifiers tested: Random Forests and Multilayer Perceptrons. Experiments were performed using a set of posts crawled during the 2014 FIFA World Cup, obtaining accuracies within the range from 94 to 100%. (C) 2015 Elsevier Inc. All rights reserved.
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