  Twitter, a popular social media outlet, has evolved into a vast source of
linguistic data, rich with opinion, sentiment, and discussion. Due to the
increasing popularity of Twitter, its perceived potential for exerting social
influence has led to the rise of a diverse community of automatons, commonly
referred to as bots. These inorganic and semi-organic Twitter entities can
range from the benevolent (e.g., weather-update bots, help-wanted-alert bots)
to the malevolent (e.g., spamming messages, advertisements, or radical
opinions). Existing detection algorithms typically leverage meta-data (time
between tweets, number of followers, etc.) to identify robotic accounts. Here,
we present a powerful classification scheme that exclusively uses the natural
language text from organic users to provide a criterion for identifying
accounts posting automated messages. Since the classifier operates on text
alone, it is flexible and may be applied to any textual data beyond the
Twitter-sphere.
