Social media’s dominant economic model relies on curating content that appeals to users’ emotions to capture and hold their attention, resulting in experiences that are primarily affective. User behavior drives content propagation, which is the core process underlying major social media phenomena—both beneficial and harmful. While IS research recognizes the importance of affective factors in content propagation, this research is marked by diverse theoretical foundations, a focus on content-level factors, and a complex landscape of findings. To unpack the affective factors underlying user behavior in content propagation and integrate insights from prior research, we develop an affective model of social media content propagation grounded in the affective response model (ARM) and extend ARM’s propositions to the omnibus context of social media. We examine two highly relevant, theoretically nuanced, and countervailing affective forces—humor and politics—by developing testable hypotheses in the discrete context of humorous and political memes. Specifically, we consider the role of human factors (mood, ideology), affective dispositions (sense of humor, affinity for political humor), content factors (political nature), and induced affective states (mirth) to predict propagation intentions. We test the model using an online experiment involving 289 participants, balanced across gender and political orientation, and 48 memes, balanced across presence and partisan leaning of political content. Using cross-classified mixed-effects regression, we find general support for our hypotheses and uncover interesting differences in politically relevant factors. The broader proposed model, supported by the study’s findings, offers a general multilevel framework to illuminate the role of affective processes in social media’s promise and peril, situate prior IS research, and support future studies of social media user behavior. We contribute to the broader social media literature by identifying emotionality as a contextual stimulus, and to ARM by explicating its multilevel nature, unpacking indirect effects, and contextualizing it to social media content propagation.
Mullins et al. (Wed,) studied this question.