![The number of likes and retweets (log+1 transformed) as a function of the emotions expressed in the tweets (very negative to very positive). We downloaded ~1.5 million random tweets from Twitter API. We then conducted sentiment analysis of the tweets using SentiStrength [112]. The analysis of each text using SentiStrength provides two scores (discrete numbers) ranging from 1-5, one score for positive intensity and one score for negative intensity. We combined the two for -4 (very negative) to 4 (very positive) number. As most tweets do not receive any likes or retweets, we conducted a log+1 transformation to the likes and retweets data. We then fit both a linear and a quadratic functions to the data. Results suggested that the quadratic function was a better predictor of the data, indicating that participants tend to like and retweet emotional tweets compared to non-emotional ones, but also that likes and retweets tend to be higher for positive compared to negative emotions.](https://www.researchgate.net/profile/Amit-Goldenberg/publication/339348731/figure/fig2/AS:888228886347789@1588781677947/The-number-of-likes-and-retweets-log-1-transformed-as-a-function-of-the-emotions_Q320.jpg)
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Digital Emotion Contagion
Abstract and Figures
People spend considerable time on digital media, and are thus often exposed to expressions of emotion by other people. This exposure can lead their own emotion expressions becoming more similar to those of others, a process we refer to as 'digital emotion contagion'. This article reviews the growing literature on digital emotion contagion. After defining emotion contagion, we suggest that one unique feature of digital emotion contagion is that it is mediated by digital media platforms that are motivated to upregulate user emotions. We then turn to measurement, and consider the challenges of demonstrating that digital emotion contagion has occurred, and how these challenges have been addressed. Finally, we call for a greater focus on understanding when emotion contagion effects are likely to be strong versus weak or nonexistent.
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... Propagation evolves as a result of interacting psychological, social network, and algorithmic factors 40,41 . The resulting collective emotions can be more intense, and evolve over time differently compared to emotions that are experienced by individuals or in small groups 42,43 . The 2004 floods in Valencia (Spain) offer an illustration of this phenomenon (Box 2). ...
... People-recommender systems (like "People You May Know" on Facebook or "Who to Follow" on X/Twitter) affect how digital social networks evolve over time, and as a result, affect the type of information users encounter online 40 . Content-recommender systems are known to play an important role in amplifying emotionally charged content online 20,42,55 . Social bots, as another example, seem to further amplify emotive climate content online. ...
... 100-102) offer a promising approach to systematically assess the medium-term collective effects of emotion propagation, however, noting that access to such data from social media companies has become more limited lately (e.g., Twitter/X). Such approaches could be complemented with other context-sensitive methodological approaches and data, such as controlled experiments and pre-registered surveys 42 , and hypothesis testing and generation using artificial agents which may include emotion dynamics 103 . Prioritization of highly affective digital content Affective forecasting can emerge as the result of being exposed to such material. ...
Emotions relate to climate change action in various ways. Here we elaborate on how the expansion of digital social networks and advances in artificial intelligence, ranging from recommender systems to generative AI, may affect the way people perceive and engage emotionally on climate change. We develop a simple framework that links individual and collective emotions, AI, and climate action, and suggest three critical areas in need of further investigation.
... There were 24 studies that addressed two levels in their discussion of solutions. From these, individual solutions included emotional management techniques (Fernbach & Van Boven, 2022) Organizational solutions in articles that included two of the four ecological levels included adjusting incentives for the sharing of fake news or other polarizing content (George, Gerhart, & Torres, 2021;Van Bavel et al., 2023), ethical platform design (Iandoli, Primario & Zollo, 2021;Kossowska et al., 2023), more and different approaches to organizational research into the causes of polarization (Goldenberg & Gross, 2020;Lenway et al., 2022;Zhuravskaya, Petrova, & Enikolopov, 2020;Barth et al., 2023), developing educational interventions and encouraging their use (Johansson, Biglan, & Embry, 2020), combatting misperceptions about polarization by media institutions (Heltzel & Laurin, 2020), acknowledging expert disagreement in key issues communication (Williams et al, 2023), expanded representation of diverse voices in the media (Ramírez-Dueñas, & Vinuesa-Tejero, 2021), increase opportunities to contribute to individual material security (Siltala, 2020), and finally, creating messaging that appeals to shared moral convictions (Skitka et al., 2021). ...
... , the adoption of new social norms, public support for changes to policy(Van Bavel et al, 2023;Van Lange & Rand, 2022), correcting misperceptions(Iyengar et al., 2019;Judge et al., 2023), ensuring consistent messages from influential people (Van Boven & Sherman, 2021), self-regulation regarding social media use and content sharing(George, Gerhart & Torres, 2021;González- Bailón, & Lelkes, 2023), calls for individual researchers to work differently or explore new avenues of understanding the problem(Goldenberg & Gross, 2020;Lenway, Schuler, Marens, Werner, & Green, 2022;Johnston, & Ollerenshaw, 2020;Barth et al, 2023). Adopting a narrative approach to correct misperceptions(McLaughlin, Velez, & Dunn, 2019), expanding individual media diets to encompass more diverse points of view Ramírez-Dueñas & Vinuesa-Tejero, 2021). ...
... Previous literature has focused on linguistic features driving consumption (J. M. Stubbersfield 2022; Gligorić et al. 2023;Robertson et al. 2023), sharing and engagement (Goldenberg and Gross, 2020;J. M. Stubbersfield, 2022), the features favoured in production, and the features that catch readers' attention (Kuiken et al. 2017;D. ...
As the written word has moved online, new technological affordances and pressures – such as accelerated cycles of production and consumption – have changed how news headlines are produced and selected. Previous literature has linked certain strategies (e.g., clickbait) and linguistic features (e.g., length, negativity) to the success of text online (e.g., clicks). We tracked changes in the prevalence of those features in a sample of ca. 40 million news headlines across the last two decades from English-language outlets worldwide, focusing on the period in which the headline format adapted to the online context. We drew from a broad set of lexical, syntactic and semantic features from the literature to find the signature of the transition to online formats in the journalistic output of the last two decades. Many – but not all – of these features have become more prevalent over time, such as length and negativity. This systematic shift appeared across news outlets from different countries, political leanings, and of different journalistic quality. This may indicate an adaptation to the new affordances and pressures of the digital, online environment, and raises questions for the design of online environments in the future.
... Emotions can be contagious to other social media users and are considered a main driver for information sharing [33]. In this regard, misinformation is more likely to express anger on social media compared to true information [86]. ...
Reducing the spread of misinformation is challenging. AI-based fact verification systems offer a promising solution by addressing the high costs and slow pace of traditional fact-checking. However, the problem of how to effectively communicate the results to users remains unsolved. Warning labels may seem an easy solution, but they fail to account for fuzzy misinformation that is not entirely fake. Additionally, users' limited attention spans and social media information should be taken into account while designing the presentation. The online experiment (n = 537) investigates the impact of sources and granularity on users' perception of information veracity and the system's usefulness and trustworthiness. Findings show that fine-grained indicators enhance nuanced opinions, information awareness, and the intention to use fact-checking systems. Source differences had minimal impact on opinions and perceptions, except for informativeness. Qualitative findings suggest the proposed indicators promote critical thinking. We discuss implications for designing concise, user-friendly AI fact-checking feedback.
... Viewers consciously use reasoning, analysis, and imagination to understand others' true feelings, thus deciding whether to follow or reject them. Streamers amplify the frequency and intensity of emotional exposure to increase user engagement and activity, leading to emotional contagion [70]. ...
The emotional expressions in live streaming e-commerce possess a strong contagious effect, enabling viewers to easily resonate with the specific emotions conveyed by the streamers and consciously build an empathy transmission chain. This study constructs a regression model based on the emotional contagion theory and explores the impact of empathy between streamers and viewers on sales performance. Using data from 30 live streams, totaling 22,707 min, from one of China’s most popular live streaming rooms, “East Buy”, between February and April 2024, we demonstrate the significant positive impact of empathy between streamers and viewers on sales. Additionally, product type positively moderates this relationship. The unexpected thing is that live streaming time does not significantly affect the relationship between empathy and sales. This study employs text sentiment analysis methods to extract emotional features from the streamers’ speech and real-time comments from viewers. Our research extends the application of emotional contagion theory to the context of live-streaming e-commerce, enriches the literature on emotional interaction in service marketing, and provides practical insights for live-streaming platforms and streamers. Streamers can optimize marketing strategies and achieve sales goals by creating a more engaging and empathetic live-streaming experience.
Rumours in online social media pose significant risks to modern society, motivating the need for better understanding of how they develop. We focus specifically on the interface between emotion and rumours in threaded discourses, building on the surprisingly sparse literature on the topic which has largely focused on emotions within the original rumour posts themselves, and largely overlooked the comparative differences between rumours and non-rumours. In this work, we provide a comprehensive analytical emotion framework, contrasting rumour and non-rumour cases using existing NLP datasets to further understand the emotion dynamics within rumours. Our framework reveals several findings: rumours exhibit more negative sentiment and emotions, including anger, fear and pessimism, while non-rumours evoke more positive emotions; emotions are contagious in online interactions, with rumours facilitate negative emotions and non-rumours foster positive emotions; and based on causal analysis, surprise acts as a bridge between rumours and other emotions, pessimism is driven by sadness and fear, optimism by joy and love.
Purpose
The persistence and virality of conspiracy theories online have raised significant concerns. This study revisits Rogers’ Diffusion of Innovations theory to examine the spread of conspiracy theories on social media, specifically focusing on how factors influencing their diffusion evolve over time.
Design/methodology/approach
The study analyzes over 1.18 million COVID-19-related tweets using a combination of natural language processing, social network analysis and machine learning techniques. It explores the dynamic roles of novelty, content negativity, influencers, echo chamber members and social bots in the diffusion of conspiracy theories.
Findings
The results indicate that novelty, influencers, echo chamber members and social bots are positively associated with the spread of conspiracy theories. The initial dissemination of conspiracy theories is primarily driven by content novelty and influencer involvement. Over time, the perpetuation of these theories becomes increasingly influenced by content negativity and the involvement of echo chamber members and social bots. Social bots serve as important connectors within echo chambers and their removal significantly reduces network cohesion.
Practical implications
The findings provide practical guidance for social media platforms and policymakers in monitoring diffusion patterns and applying targeted interventions.
Originality/value
This study introduces a time-sensitive approach to understanding the spread of conspiracy theories on social media. By identifying the key drivers at different stages of the diffusion process, this study offers valuable insights for developing effective strategies to counteract the proliferation of conspiracy theories at various points in their lifecycle.
This study uses the Health Belief Model (HBM) to analyze the narrative constructions
and sentiments of the most retweeted tweets about COVID-19 vaccines. Large-scale
data and qualitative textual analysis revealed that tweet dissemination is influenced
by perceived risks and benefits, while the tweet format (narrative vs. non-narrative)
had no significant effect. Personal stories gained more shares than cultural stories,
and high-benefit narratives received more retweets than low-risk ones. Tweets
with highly perceived benefits elicited positive sentiments, highlighting implications
for effective health communication on social media. Theoretical implications for the
HBM model and crafting effective intervention health messages were discussed.
Our social media newsfeeds are filled with a variety of content all battling for our limited attention. Across 3 studies, we investigated whether moral and emotional content captures our attention more than other content and if this may help explain why this content is more likely to go viral online. Using a combination of controlled lab experiments and nearly 50,000 political tweets, we found that moral and emotional content are prioritized in early visual attention more than neutral content, and that such attentional capture is associated with increased retweets during political conversations online. Furthermore, we found that the differences in attentional capture among moral and emotional stimuli could not be fully explained by differences in arousal. These studies suggest that attentional capture is 1 basic psychological process that helps explain the increased diffusion of moral and emotional content during political discourse on social media, and shed light on ways in which political leaders, disinformation profiteers, marketers, and activist organizations can spread moralized content by capitalizing on natural tendencies of our perceptual systems. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
What accounts for the prevalence of negative news content? One answer may lie in the tendency for humans to react more strongly to negative than positive information. “Negativity biases” in human cognition and behavior are well documented, but existing research is based on small Anglo-American samples and stimuli that are only tangentially related to our political world. This work accordingly reports results from a 17-country, 6-continent experimental study examining psychophysiological reactions to real video news content. Results offer the most comprehensive cross-national demonstration of negativity biases to date, but they also serve to highlight considerable individual-level variation in responsiveness to news content. Insofar as our results make clear the pervasiveness of negativity biases on average, they help account for the tendency for audience-seeking news around the world to be predominantly negative. Insofar as our results highlight individual-level variation, however, they highlight the potential for more positive content, and suggest that there may be reason to reconsider the conventional journalistic wisdom that “if it bleeds, it leads.”
It is well established that people often express emotions that are similar to those of other group members. However, people do not always express emotions that are similar to other group members, and the factors that determine when similarity occurs are not yet clear. In the current project, we examined whether certain situations activate specific emotional motives that influence the tendency to show emotional similarity. To test this possibility, we considered emotional responses to political situations that either called for weak (Studies 1 and 3) or strong (Study 2 and 4) negative emotions. Findings revealed that the motivation to feel weak emotions led people to be more influenced by weaker emotions than their own, whereas the motivation to feel strong emotions led people to be more influenced by stronger emotions than their own. Intriguingly, these motivations led people to change their emotions even after discovering that others' emotions were similar to their initial emotional response. These findings are observed both in a lab task (Studies 1-3) and in real-life online interactions on Twitter (Study 4). Our findings enhance our ability to understand and predict emotional influence processes in different contexts and may therefore help explain how these processes unfold in group behavior. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Emotional contagion has long been conceptualized as the automatic transfer of affective states between people, similar to the spread of diseases. New evidence, however, has challenged this view by demonstrating that emotions, contrary to diseases, spread selectively rather than blindly because their transfer is controlled by social factors. Here, we take a closer look at this top-down social control of emotional contagion. We review literature on the moderating role of social factors in emotional contagion and emotional mimicry, a process often considered a basic mechanism of emotional contagion. We argue that top-down social processes controlling emotional mimicry may be explained by the correction hypothesis formulated to account for contrast effects in priming research. We also analyze whether similar corrective processes may be involved in less automatic mechanisms of emotional contagion, such as social appraisal. Finally, we propose that the modulating effects of social factors on emotional contagion and its mechanisms, similar to priming effects, may be interpreted within the framework of dual-process theories.
Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network’s structure and communicative properties. Here, we study how external events are associated with a network’s change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. We also analyze the communication dynamics among specialized teams in the organization. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness such as the activation of weak ties to obtain novel information. Rather, the network “turtles up.” It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider and within-role vs. between-role communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.
Why do connected users in online social networks express similar emotions? Past approaches have suggested situational emotion transfers (i.e., contagion) and the phenomenon that emotionally similar users flock together (i.e., homophily). We analyze these mechanisms in unison by exploiting the hierarchical structure of YouTube through multilevel analyses, disaggregating the video- and channel-level effects of YouTuber emotions on audience comments. Dictionary analyses using the National Research Council emotion lexica were used to measure the emotions expressed in videos and user comments from 2,083 YouTube vlogs selected from 110 vloggers. We find that video- and channel-level emotions independently influence audience emotions, providing evidence for both contagion and homophily effects. Random slope models suggest that contagion strength varies between YouTube channels for some emotions. However, neither average channel-level emotions nor number of subscribers significantly moderate the strength of contagion effects. The present study highlights that multiple, independent mechanisms shape emotions in online social networks.
Online communication differs from face-to-face communication in multiple ways. Because of these differences, there is an ongoing debate regarding whether people disclose their true selves on social media, or whether they present an idealized or socially appropriate version of themselves. Whereas some features of online communication (such as anonymity and reduced information richness) have been argued to increase self-disclosure, there are other features (such as asynchronicity, multiple audiences, and audience feedback) that favor self-presentation. In this article, I review the literature on the effect of these five medium characteristics on whether individuals self-disclose or manage impressions on social media, and if managed, in what way.
Leveraging the wealth of research insights generated over the past 25 years, we develop a model of emotional contagion in organizational life. We begin by defining emotional contagion, reviewing ways to assess this phenomenon, and discussing individual differences that influence susceptibility to emotional contagion. We then explore the key role of emotional contagion in organizational life across a wide range of domains, including (1) team processes and outcomes, (2) leadership, (3) employee work attitudes, (4) decision-making, and (5) customer attitudes. Across each of these domains, we present a body of organizational behavior research that finds evidence of the influence of emotional contagion on a variety of attitudinal, cognitive, and behavioral/performance outcomes as well as identify the key boundary conditions of the emotional contagion phenomenon. To support future scholarship in this domain, we identify several new frontiers of emotional contagion research, including the need to better understand the “tipping point” of positive versus negative emotional contagion, the phenomenon of counter-contagion, and the influence of computer mediated communication and technology within organizations and society on emotional contagion. In closing, we summarize our model of emotional contagion in organizations, which we hope can serve as a catalyst for future research on this important phenomenon and its myriad effects on organizational life.
While previously polarization was primarily seen only in issue-based terms, a new type of division has emerged in the mass public in recent years: Ordinary Americans increasingly dislike and distrust those from the other party. Democrats and Republicans both say that the other party’s members are hypocritical, selfish, and closed-minded, and they are unwilling to socialize across party lines. This phenomenon of animosity between the parties is known as affective polarization. We trace its origins to the power of partisanship as a social identity, and explain the factors that intensify partisan animus. We also explore the consequences of affective polarization, highlighting how partisan affect influences attitudes and behaviors well outside the political sphere. Finally, we discuss strategies that might mitigate partisan discord and conclude with suggestions for future work. Expected final online publication date for the Annual Review of Political Science Volume 22 is May 11, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.