Reactive and Asymmetric Communication Flows: Social Media Discourse and Partisan News Framing in the Wake of Mass Shootings

Abstract

Marked by both deep interconnectedness and polarization, the contemporary media system in the United States features news outlets and social media that are bound together, yet deeply divided along partisan lines. This article formally analyzes communication flows surrounding mass shootings in the hybrid and polarized U.S. media system. We begin by integrating media system literature with agenda setting and news framing theories and then conduct automated text analysis and time series modeling. After accounting for exogenous event characteristics, results show that (a) sympathy and gun control discourses on Twitter preceded news framing of gun policy more than the other way around, and (b) conservatives on Twitter and conservative media reacted to progressive discourse on Twitter, without their progressive counterparts exhibiting a similar reactiveness. Such results shed light on the influence of social media on political communication flows and confirm an asymmetry in the ways partisan media ecosystems respond to social events.

When the Personal Becomes Political: Unpacking the Dynamics of Sexual Violence and Gender Justice Discourses Across Four Social Media Platforms

“…Existing studies show that even when controlling for real-world events, the relationship between networked acknowledgment and activism is significant (Suk et al, 2021) in #MeToo on Twitter, and the same applies for the interrelations between Twitter discourses and news media coverage in the case of mass shootings (Zhang et al, 2022). Additionally, we performed supplementary time series analyses based on a subset of the entire time frame (from October 2017 to April 2020), using the available event data (list of major accusations and external events) that our research team compiled.…”

“…Those are key motivators of calls to action to address underlying problems because they organize what would otherwise remain personal experiences and individual emotions (Stewart & Schultze, 2019). For example, the immediate affective response to mass shootings on social media in the form of “thoughts and prayers” can spark calls to tighten gun access and prevent future tragedies (Zhang et al, 2022). Bennett and Segerberg (2013) note that such affective connections between individuals can greatly lower the cost of participation.…”

“…In the context of sexual violence and gender justice activism, calls to action to address such issues are presumably more likely to align with liberal causes that demand changes in workplace practices, legislation, and culture (Mason, 2018). Evidence shows that conservatives are more likely to react to liberals than the other way around (Zhang et al, 2022), suggesting that conservatives, participating in this contention, might push back against liberals’ calls to action and intensify feminism contention. Given the competing arguments, we propose a question: What is the relationship between calls to action and feminism contention (RQ1)?…”

Digital Rage: Testing “the Obama Effect” on Internet-Based Expressions of Racism

“…Methodologically, research focused on correlation is common, and there is certainly a precedent for aggregate data being used to predict seemingly unconnected events. Some examples include Google searches for the n-word by county being used to predict voting patterns, or media agenda-setting research examining the correlation between media coverage and X discussion of the same topic (Neuman et al, 2014; Stephens-Davidowitz, 2017; Vargo et al, 2014; Zhang et al, 2022). In agenda-setting studies, X discussions are not necessarily responding to a specific news article, but the media coverage has some agenda-setting impact on the public, who might think a certain issue is important and therefore tend to talk about the topic more often on social media.…”

“Flattening the curve”: Communication, risk and COVID-19 pandemic in Turkey

“…We used the emerging themes to code the rest of the data using Nvivo software. Using a keyword approach to track framing devices ( Guggenheim et al, 2015 ; Zhang et al, 2022 ), we conducted another layer of analysis through Nvivo to identify key words associated with various themes and clusters of narratives (see below). Three major or “master” frames ( Snow et al, 1986 ) emerged from the final analysis related to COVID-19: “public health risk,” “economic risk” and “national security risk (See Table 2 ).” In the “public health risk” frame, news articles depicted COVID-19 pandemic as presenting an imminent threat to the health of Turkish citizens.…”