Abstract
Spammers distributing adult content are becoming an apparent and yet intrusive problem with the increasing prevalence of online social networks among users. For improving user experience and especially preventing exposure to users of lower age groups, these accounts need to be detected efficiently. In this work, a model is proposed in which a lexicon-based approach is used to label users with their values. This study is based on the fact that users behave according to the values they possess. The amalgamation of content-based features like values, the entropy of words, lexical diversity, and context-based word embeddings are found to be robust. Among several machine learning models, XGboost performs exceedingly well with accuracy (92.28 ± 1.28%) for all features. Feature importance and their discriminative power have also been shown. A comparative study is also done with one of the latest approaches and our approach is found to be more efficient.
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