Detection of human, legitimate bot, and malicious bot in online social networks based on wavelets
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2018-02-01
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Social interactions take place in environments that influence people's behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a waveletbased model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users' textual content.We create the feature vector from the DiscreteWavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classificationmodel using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: Single theme and miscellaneous one.
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ACM Transactions on Multimedia Computing, Communications and Applications, v. 14, n. 1s, 2018.
