Note(s): Table compares words in our LDA-based topics to words identified through an XGBoost random forest machine learning model (Chen et al., 2016). The XGBoost model tells us which words in the entire tweet corpus distinguish bots from other speakers, and Table 6 shows which of the top 200 words from the XGBoost model are also found in our 25 LDA-based topic words (numbers in parentheses indicate XGBoost feature rank). The results allow a comparison of the intersection of the word-level and topic-level analysis of what distinguishes bot from non-bot speaking patterns. For the topics that do not contain a word-level influential feature (e.g. Topic 8), we can assume that it is the more holistic level topic that drives Table 3 results rather than a specific word(s)

Source(s): Authors' own work