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Twitter, a popular social media outlet, has evolved into a vast source oflinguistic data, rich with opinion, sentiment, and discussion. Due to theincreasing popularity of Twitter, its perceived potential for exerting socialinfluence has led to the rise of a diverse community of automatons, commonlyreferred to as bots. These inorganic and semi-organic Twitter entities canrange from the benevolent (e.g., weather-update bots, help-wanted-alert bots)to the malevolent (e.g., spamming messages, advertisements, or radicalopinions). Existing detection algorithms typically leverage meta-data (timebetween tweets, number of followers, etc.) to identify robotic accounts. Here,we present a powerful classification scheme that exclusively uses the naturallanguage text from organic users to provide a criterion for identifyingaccounts posting automated messages. Since the classifier operates on textalone, it is flexible and may be applied to any textual data beyond theTwitter-sphere.

Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter