The Collective Sentiments of Education Advocacy Groups

• In Event: 64.057 - Using Social Analysis Methods to Understand Education Interest Group Political Activity

Abstract

Objectives. In democratic systems, advocacy groups play central roles in shaping the conditions for policy making (Andrews & Edwards, 2004). As a major realm of policy, public education is a central arena for advocacy groups seeking to influence policy debates (Labaree, 2007). Researchers have long examined the strategies and influence of advocacy groups in a variety of education areas including teacher unions (Poole, 2001); charter school activists (DeBray-Pelot, Lubienski & Scott, 2007), and foundations (Scott, 2011). An emerging body of research has also begun exploring advocacy organizations’ use of social media (Edwards & Hoefer, 2010). Since social media are more personalized, messages can be targeted, potentially broadening advocacy participation (Bennett 2012).

Data. The analysis examined over 500,000 tweets about the Common Core State Standards (CCSS) by more than 100,000 distinct actors between November 2014 and October 2015.

Methods. Analysis of advocacy group messaging has mostly focused on the ways in which groups frame their communications and the particular language that they use to enact these frames. 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 advocates choose to convey their perspective (Fillmore, 1976; Lakoff, 2008). Specific word choices have been shown to indicate several psychological characteristics, including (1) mood (Stirman & Pennebaker, 2001), (2) conviction (Bond, & Lee, 2005; Pennebaker, 2011), (3) thinking style (Pennebaker & King (1999), and (4) drive motivation (McClelland, 1985; Winter 1987).

To understand the inner structure and clustering of the interactions of the overall network of, we ran a community detection algorithm to identify and represent structural sub-communities, or factions. The algorithm sorts people into groups based on the extent of their ties to other actors. We identified three major sub-communities in the Twitter debate about the CCSS. Next, to analyze the four psychological characteristics of each of the CCSS sub-communities we developed a customized version of Pennebaker et al.’s (2007) word libraries and contrasted each actor’s tweets to those in the libraries. Then, by grouping participants into the three sub-communities, we ran ANOVAs with multiple test corrections, and identified statistically significant differences in the socio-psychological profiles of the different sub-communities.

Results & Implications. Our results show distinctly different profiles for each of the sub-communities. Supporters of the standards had the highest levels of conviction, achievement motivation, and an analytic and formal thinking style. They also had the lowest levels of narrative thinking and use of anger words. The educators opposed to the CCSS had the highest level of drive motivation, use of sad words, and a narrative thinking style. Opponents of the CCSS from outside of 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. These results provide insights into the underlying collective psychological sentiments of the different interest groups seeking influence on the CCSS.

Author

• Jonathan A. Supovitz, University of Pennsylvania