  In recent years, social bots have been using increasingly more sophisticated,
challenging detection strategies. While many approaches and features have been
proposed, social bots evade detection and interact much like humans making it
difficult to distinguish real human accounts from bot accounts. For detection
systems, various features under the broader categories of account profile,
tweet content, network and temporal pattern have been utilised. The use of
tweet content features is limited to analysis of basic terms such as URLs,
hashtags, name entities and sentiment. Given a set of tweet contents with no
obvious pattern can we distinguish contents produced by social bots from that
of humans? We aim to answer this question by analysing the lexical richness of
tweets produced by the respective accounts using large collections of different
datasets. Our results show a clear margin between the two classes in lexical
diversity, lexical sophistication and distribution of emoticons. We found that
the proposed lexical features significantly improve the performance of
classifying both account types. These features are useful for training a
standard machine learning classifier for effective detection of social bot
accounts. A new dataset is made freely available for further exploration.
