Higher contagion and weaker ties mean anger spreads faster than joy in social media (1608.03656v3)

Abstract: Increasing evidence suggests that, similar to face-to-face communications, human emotions also spread in online social media. However, the mechanisms underlying this emotional contagion, for example, whether different feelings spread in unlikely ways or how the spread of emotions relates to the social network, is rarely investigated. Indeed, because of high costs and spatio-temporal limitations, explorations of this topic are challenging using conventional questionnaires or controlled experiments. Because they are collection points for natural affective responses of massive individuals, online social media sites offer an ideal proxy for tackling this issue from the perspective of computational social science. In this paper, based on the analysis of millions of tweets in Weibo, surprisingly, we find that anger is more contagious than joy, indicating that it can spark more angry follow-up tweets. Moreover, regarding dissemination in social networks, anger travels easily along weaker ties than joy, meaning that it can infiltrate different communities and break free of local traps because strangers share such content more often. Through a simple diffusion model, we reveal that greater contagion and weaker ties function cooperatively to speed up anger's spread. The diffusion of real-world events with different dominant emotions provides further testimony to the findings. To the best of our knowledge, this is the first time that quantitative long-term evidence has been presented that reveals a difference in the mechanism by which joy and anger are disseminated. Our findings shed light on both personal anger management in human communications and on controlling collective outrage in cyberspace.

• The paper demonstrates that anger’s rapid spread is driven by higher emotional contagion and effective diffusion through weak social ties.
• It employs computational analysis of over 11 million Weibo tweets and a diffusion model similar to the Susceptible-Infected framework to quantify emotion propagation.
• Findings suggest that addressing anger’s swift dissemination can help mitigate negative collective behavior and improve online discourse.

Overview of "Higher contagion and weaker ties mean anger spreads faster than joy in social media"

The paper by Fan, Xu, and Zhao investigates emotional contagion in online social media, focusing specifically on the differential propagation of anger and joy on platforms such as Weibo, which serves as a proxy for global social media environments like Twitter. The authors argue that anger spreads more rapidly through social networks than joy, attributing this phenomenon to anger's higher contagion rate and its preference for dissemination through weaker ties.

Key Findings

1. Emotional Contagion Metrics: The paper reveals that anger has a distinct advantage over joy in emotional contagion metrics. Anger incites more follow-up tweets than joy when users are exposed to tweets expressing these emotions. This greater emotional impact suggests that anger is more contagious than joy, both in terms of eliciting emotional responses and influencing subsequent user behavior.
2. Dissemination Channels: Contrary to joy, anger is observed to propagate more effectively along weaker social ties. The paper highlights that weak ties, often bridging disparate communities, enable anger to bypass local traps and achieve broader coverage within the social network.
3. Speed of Spread: Utilizing a diffusion model parallel to the Susceptible-Infected framework, the authors simulate the dynamics of emotional spread. Their findings indicate that anger spreads faster due to its dual capacity to increase infection rates and leverage weak ties to penetrate diverse community clusters.

Methodology

The authors analyzed over 11 million Weibo tweets and employed computational techniques to categorize these emotional expressions. By examining user interactions and emotional states over time, they established quantitative metrics assessing both contagion significance and tendency. The bridge between empirical data and theoretical model lies in a simple yet effective simulation that demonstrates how varying parameters influence the speed and diffusion pattern of emotional contagions.

Implications

1. Personal Anger Management: The high contagion and rapid spread of anger underscore the need for individuals to understand the potential repercussions of sharing negative emotions online, particularly regarding the inadvertent influence on strangers.
2. Collective Behavior: In the context of social media-mediated collective intelligence, anger can impair judgment and rational discourse. Strategies to mitigate anger's swift proliferation, such as reducing weak tie interactions, could foster a more balanced online environment.
3. Future Research Directions: The exploration of how demographics or cultural contexts influence emotional contagion invites deeper examination into the propagation mechanisms across varied populations and platforms like Twitter. Moreover, gender-specific studies could elucidate differential susceptibility to emotional contagion.

In conclusion, the presented research provides significant insights into the mechanics of emotional contagion, highlighting the differential impact of specific emotions within social networks. The implications address both theoretical understanding in social data mining and practical strategies for social media users in managing emotional outputs and their broader influence.