Heidari, MaryamJones, James H Jr.2022-03-112022-03-112022-03https://hdl.handle.net/1920/12756Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event, or product. However, this use raises an important question: what percentage of the information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a ``bot" instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. This paper introduces a new model that uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features for the social media bot detection model. Using a Natural Language Processing approach to derive topic-independent features for the new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data set Cresci \cite{cresci-etal-2017-paradigm}as generated by a bot or a human, where the most accurate prior work achieved an accuracy of 92\%.Attribution-NonCommercial-ShareAlike 3.0 United StatesBot detectionNatural Language ProcessingNeural NetworkSocial MediaWorking Paper