Background/Context: As a major area of civic decision making, public education is a central arena for advocacy groups seeking to influence policy debates. An emerging body of research examines advocates’ use of social media. While debates about policy can be thought of as a clash of large ideas contained within frames, cognitive linguists note that framing strategies are activated by the particular words that advocates choose to convey their positions.
Purpose/Objective/Research Question/Focus of Study: This study examined the vociferous debate surrounding the Common Core State Standards on Twitter during the height of state adoption in 2014 and 2015. Combining social network analysis and natural language processing techniques, we first identified the organically forming factions within the Common Core debate on Twitter and then captured the collective psychological sentiments of these factions.
Research Design: The study employed quantitative statistical comparisons of the frequency of words used by members of different factions around the Common Core on Twitter that are associated in prior research with four psychological characteristics: mood, motivation, conviction, and thinking style.
Data Collection and Analysis: Data were downloaded from Twitter from November 2014 to October 2015 using at least one of three hashtags: #commoncore, #ccss, or #stopcommoncore. The resulting data set consisted of more than 500,000 tweets and retweets from more than 100,000 distinct actors. We then ran a community detection algorithm to identify the structural subcommunities, or factions. To measure the four psychological characteristics, we adapted Pennebaker and colleagues’ Linguistic Inquiry and Word Count libraries. We then connected the individual tweet authors to their faction based on the results of the social network analysis community detection algorithm. Using these groups, and the standardized results for each psychological characteristic/dimension, we performed a series of analyses of variance with Bonferroni corrections to test for differences in the psychological characteristics among the factions.
Findings/Results: For each of the four psychological characteristics, we found different patterns among the different factions. Educators opposed to the Common Core had the highest level of drive motivation, use of sad words, and use of words associated with a narrative thinking style. Opponents of the Common Core from outside education exhibited an affiliative drive motivation, a narrative thinking style, high levels of anger words, and low levels of conviction in their choice of language. Supporters of the Common Core used words that represented a more analytic thinking style, stronger levels of conviction, and words associated with a higher level of achievement orientation.
Conclusions/Recommendations: Individuals on Twitter, mostly strangers to each other, band together to form fluid communities as they share positions on particular issues. On Twitter, these bonds are formed by behavioral choices to follow, retweet, and mention others. This study reveals how like-minded individuals create a collective sentiment through their specific choice of words to express their views. By analyzing the underlying psychological characteristics associated with language, we show the distinct pooled psychologies of activists as they engaged together in political activity in an effort to influence the political environment.