Search results

Traditionally, the main goal of empirical research has been to test theories. Yet, theory-testing is problematical in the social sciences. Findings from empirical studies have proven hard to replicate and there is a lack of progress in creating a coherent and cumulative knowledge base. There are both practical and epistemological issues that prevent effective empirical tests. It is difficult to operationalize constructs and design decisive tests of theories. The laws and regularities posited in theories in the natural sciences are independent of human actors, while theories in the social sciences describe systems and structures that are created and maintained by human actors. Nonetheless, human actors are sometimes guided by theories. They may change their behavior or make different decisions based on academically produced knowledge. This relationship is usually mediated by the use of tools of various sorts (i.e., design principles, diagrams, or stories). I discuss why scholars should conduct empirical research to test the pragmatic validity of tools that are derived from theories rather than testing the scientific validity of the theories themselves.

This study aims to examine the role of hope and hate in political leaders’ messages in influencing liberals versus conservatives’ social-distancing behavior during the COVID-19 pandemic. Given the increasing political partisanship across the world today, using the appropriate message framing has important implications for social and public policy.

The authors use two Natural Language Processing (NLP) methods – a pretrained package (HateSonar) and a classifier built to implement our supervised neural network-based model architecture using RoBERTa – to analyze 61,466 tweets by each US state’s governor and two senators with the goal of examining the association between message factors invoking hate and hope and increased or decreased social distancing from March to May 2020. The authors examine individuals’ social-distancing behaviors (the amount of nonessential driving undertaken) using data from 3,047 US counties between March 13 and May 31, 2020, as reported by Google COVID-19 Community Mobility Reports and the New York Times repository of COVID-19 data.

The results show that for conservative state leaders, the use of hate increases nonessential driving of state residents. However, when these leaders use hope in their speech, nonessential driving of state residents decreases. For liberal state leaders, the use of hate displays a directionally different result as compared to their conservative counterparts.

Amid the emergence of new analytic techniques and novel data sources, the findings demonstrate that the use of global positioning systems data and social media analysis can provide valuable and precise insights into individual behavior. They also contribute to the literature on political ideology and emotion by demonstrating the use of specific emotion appeals in targeting specific consumer segments based on their political ideology.

The findings have significant implications for policymakers and public health officials regarding the importance of considering partisanship when developing and implementing public health policies. As partisanship continues to increase, applying the appropriate emotion appeal in messages will become increasingly crucial. The findings can help marketers and policymakers develop more effective social marketing campaigns by tailoring specific appeals given the political identity of the consumer.

Using Neural NLP methods, this study identifies the specific factors linking social media messaging from political leaders and increased compliance with health directives in a partisan population.