DC Accepted Papers Paper:
An Exploration of Strategies to Boost Public Engagement with the Federal Government on Social Media
*Names in bold indicate Presenter
More Americans are using social media on a frequent basis (Pew Research Center 2019b), but government social media is not being used to its full potential. For instance, although 72% of U.S. adults (approximately 150 million) use some types of social media, the median number of followers of federal executive departments is 782,900 on Twitter and 139,538 on Facebook. The relatively low number of followers suggests a low level of public engagement with these organizations. Public engagement with a social media site is defined as a reiterated behavior process of public connecting and reconnecting with the site (O’Brien and Toms 2008). The low public engagement with the federal government on social media means a missed opportunity to reap the benefits of robust two-way communication for organizations such as facilitating the exchange of information with clients, forging clients’ trust, and increasing clients’ participation in offline coproduction (Calder, Malthouse, and Schaedel 2009; Paek et al. 2013). Given the low public trust in the federal government (Pew Research Center 2019a), public managers must learn how to engage the public better and forge trust in government on social media. This study aims to contribute to public understanding of what contents enhance public engagement with federal executive departments on Twitter.
We draw on Carpenter and Krause’s (2012) conceptual framework to construct key independent variables. We categorize tweets into five groups: (1) tweets aiming to enhance performative reputation, (2) to enhance moral reputation, (3) to enhance procedural reputation, (4) to enhance technical reputation (5) others. In addition, we measure the ambiguity of each tweet to capture the tendency of public organizations to simultaneously satisfy multiple audiences by projecting ambiguity. We follow the coding method in Anastasopoulos and Whitford (2019) and use the gradient boosting to create the key independent variables. We use social media engagement framework to generate dependent variables (Jiang, Luo, and Kulemeka 2016). We gauge the dimension of intimacy through the number of likes and sentiment scores of replies and the dimension of influence through the number of retweet and mention.
The initial double fixed effect model (agencies and dates of the tweets) shows that tweets aiming to enhance moral reputation is associated with more likes and positive sentiment; tweets aiming to enhance the performative reputation and moral reputation correlate with more retweets and mentions. In addition, the positive effect of building a moral reputation is amplified if the content is concrete.
We draw on Carpenter and Krause’s (2012) conceptual framework to construct key independent variables. We categorize tweets into five groups: (1) tweets aiming to enhance performative reputation, (2) to enhance moral reputation, (3) to enhance procedural reputation, (4) to enhance technical reputation (5) others. In addition, we measure the ambiguity of each tweet to capture the tendency of public organizations to simultaneously satisfy multiple audiences by projecting ambiguity. We follow the coding method in Anastasopoulos and Whitford (2019) and use the gradient boosting to create the key independent variables. We use social media engagement framework to generate dependent variables (Jiang, Luo, and Kulemeka 2016). We gauge the dimension of intimacy through the number of likes and sentiment scores of replies and the dimension of influence through the number of retweet and mention.
The initial double fixed effect model (agencies and dates of the tweets) shows that tweets aiming to enhance moral reputation is associated with more likes and positive sentiment; tweets aiming to enhance the performative reputation and moral reputation correlate with more retweets and mentions. In addition, the positive effect of building a moral reputation is amplified if the content is concrete.
