DATA ANALYSIS WITH TOPIC MODELS FOR COMMUNICATIONS RESEARCHERS FREDERICK BOEHM (FRED.BOEHM@WISC.EDU), DEPARTMENT OF STATISTICS, UNIVERSITY OF WISCONSIN-MADISON ABSTRACT We introduce topic modeling as a tool when analyzing textual data. We illustrate our methods with analyses of New York Times transcripts and tweets from three days in March 2016. We argue that such analyses will be useful in mass communications and journalism research. These methods are especially useful for identifying topics, or themes, in large collections of texts, when reading each piece individually is impractical. INTRODUCTION Social media users ﬂood us with tweets, status updates, and blog posts. Data analysis with topic models enable re- searchers to identify themes, or topics, in a collection of texts. We present results from separate analyses of 1) New York Times articles from March 19, 20, and 21, 2016 and 2) a collection of tweets (from Twitter) from the same three days. Our manuscript contains computing code to repro- duce our ﬁndings. REFERENCES [1] David M Blei. Build, compute, critique, repeat: Data analysis with latent variable models. Annual Review of Statistics and Its Applica- tion, 1:203–232, 2014. METHODS 1. Fit latent Dirichlet allocation (LDA) models, sepa- rately, to 1) our collection of tweets and 2) our collec- tion of print media articles from the New York Times 2. Visualized the topic modeling results with word clouds 3. Assessed resulting topics for coherence RESULTS video music march song tonight live justin check listen album fly show reaction love night pm ft day bieber watch playlist nowplaying fans radio star today added movie official priceless book la playing np soundcloud stage drake dj de years rihanna house tour young talks feat batman coming band list Figure 1: Word cloud for one topic (from a 20-topic model) of tweets from March 19, 20, and 21, 2016. game city win league season man team leicester today goal points fans united play good back football mufc rashford manchester time half top games state st great year goals player lead players chelsea point penalty west basketball years won marchmadness match derby day big iowa demichelis final premier home live Figure 2: Word cloud for one topic (from a 20-topic model) of tweets from March 19, 20, and 21, 2016. music show film theater production movie song songs musical work director played dance ballet company album play movies set performance life role stage york star country broadway story williams band opera character festival love series sound altman works record shows orchestra plays films center season drama bowie concert dancing singer Figure 3: Word cloud for one topic (from a 20-topic model) of New York Times articles from March 19, 20, and 21, 2016. water baalsrud island men ship arctic boat ships cruise islands expedition sea kelen miles ocean small feet river franklin genz inuit crew story found trip hawaii cruises north food wind norwegian di head escape baalsruds huth snow open pacific frozen joel haug local van coast william lake deck cabin canoe Figure 4: Word cloud for one topic (from a 20-topic model) of New York Times articles from March 19, 20, and 21, 2016. DISCUSSION From the word clouds above, and those not shown, we ﬁnd differences between the topics of discussion on Twitter and the topics in the New York Times. For instance, we ﬁnd in the tweets an entire topic that deals with sports (with an emphasis on soccer) and another topic that involves music and entertainment. The New York Times analysis yields topics that match many of the newspaper sections. The two topics above might be called "Music" and "Travel & Adventure".