Dataset Information

Emotions and virality: Social transmission of political messages on Twitter.

ABSTRACT: Drawing on previous literature that valence and arousal constitute the fundamental properties of emotions and that emotional content is a determinant of social transmission, this study examines the role of valence and arousal in the social transmission of politicians' messages on Twitter. For over 3,000 tweets from five Austrian party leaders, the discrete emotion that the message intended to elicit in its recipients was captured by human coders and then classified on its valence (positive or negative) and arousal (low or high). We examined the effects of valence and arousal on the retweet probability of messages. Results indicate that tweets eliciting a negative (vs. positive) valence decreased retweet probability, whereas tweets eliciting a high (vs. low) arousal increased retweet probability. The present research replicates previous findings that arousal constitutes a determinant of social transmission but extends this mechanism to the realm of political communication on Twitter. Moreover, in contrast to the frequently mentioned negativity bias, positive emotions increased the likelihood of a message being shared in this study.

SUBMITTER: Pivecka N

PROVIDER: S-EPMC9692101 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Emotions and virality: Social transmission of political messages on Twitter.

Pivecka Niklas N Ratzinger Roja Alexandra RA Florack Arnd A

Frontiers in psychology 20221111

Drawing on previous literature that valence and arousal constitute the fundamental properties of emotions and that emotional content is a determinant of social transmission, this study examines the role of valence and arousal in the social transmission of politicians' messages on Twitter. For over 3,000 tweets from five Austrian party leaders, the discrete emotion that the message intended to elicit in its recipients was captured by human coders and then classified on its valence (positive or ne ...[more]

PMID: 36438335

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Project description:The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear circadian (24 h) and circaseptan (7-day) patterns. But to this point, such findings could be instrument-dependent as they rely exclusively on coding using the Linguistic Inquiry Word Count. Further, research has shown that self-referential content has higher relevance and meaning for individuals, compared with other types of content. Investigating the specificity of self-referential material in temporal patterns of emotional expression in tweets is of interest, but current research is based upon generic textual productions. The temporal variations of emotions shared in tweets through emojis have not been compared to textual analyses to date. This study hence focuses on several comparisons: (i) between Self-referencing tweets versus Other topic tweets, (ii) between coding of textual productions versus coding of emojis, and finally (iii) between coding of textual productions using different sentiment analysis tools (the Linguistic Inquiry and Word Count-LIWC; the Valence Aware Dictionary and sEntiment Reasoner-VADER and the Hu Liu sentiment lexicon-Hu Liu). In a collection of more than 7 million Self-referencing and close to 18 million Other topic content-coded tweets, we identified that (i) similarities and differences in terms of shape and amplitude can be observed in temporal trajectories of expressed emotions between Self-referring and Other topic tweets, (ii) that all tools feature significant circadian and circaseptan patterns in both datasets but not always, and there is often a correspondence in the shape of circadian and circaseptan patterns, and finally (iii) that circadian and circaseptan patterns obtained from the coding of emotional expression in emojis sometimes depart from those of the textual analysis, indicating some complementarity in the use of both modes of expression. We discuss the implications of our findings from the perspective of the literature on emotions and well-being.

Project description:The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from the politicians in the U.S., including the ex-president, members of Congress, and state governors, we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.Supplementary informationThe online version contains supplementary material available at 10.1140/epjds/s13688-021-00308-4.

Dataset Information

Emotions and virality: Social transmission of political messages on Twitter.

Publications

Emotions and virality: Social transmission of political messages on Twitter.

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