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In recent years, social bots have been using increasingly more sophisticated,challenging detection strategies. While many approaches and features have beenproposed, social bots evade detection and interact much like humans making itdifficult to distinguish real human accounts from bot accounts. For detectionsystems, various features under the broader categories of account profile,tweet content, network and temporal pattern have been utilised. The use oftweet content features is limited to analysis of basic terms such as URLs,hashtags, name entities and sentiment. Given a set of tweet contents with noobvious pattern can we distinguish contents produced by social bots from thatof humans? We aim to answer this question by analysing the lexical richness oftweets produced by the respective accounts using large collections of differentdatasets. Our results show a clear margin between the two classes in lexicaldiversity, lexical sophistication and distribution of emoticons. We found thatthe proposed lexical features significantly improve the performance ofclassifying both account types. These features are useful for training astandard machine learning classifier for effective detection of social botaccounts. A new dataset is made freely available for further exploration.

Lexical analysis of automated accounts on Twitter