Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events.Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19.Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic.Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant “rebound effect” by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003).Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

Daily social media use, social ties, and emotional well-being in later life

Older American adults are increasingly utilizing communication technologies, but research has seldom explored older adults’ daily social media use and its interface with other “offline” social ties. To explore a complementary and/or compensatory function of social media in later life, this study employed data from the Daily Experiences and Well-Being Study (2016–2017) to examine associations between daily social media use, daily social encounters, social network structure, and daily mood. Community-dwelling older adults ( N = 310; Mage = 73.96) reported on their overall social network structure (diversity in types of social ties and size of network), their daily social encounters in-person and by phone, social media use, and emotional well-being for 5 to 6 days. Multilevel models revealed that daily social media use was associated with daily mood in the context of daily social encounters and the size of the social network. Individuals reported less negative mood on days with more social media use and more in-person encounters. More daily social media use was associated with more positive mood for individuals with a relatively small social network but not for their counterparts with larger social networks. Findings suggest that social media is a distinct form of social resource in later life that may complement the emotional benefits of daily social encounters and compensate for the age-related reduction in social network size. Future research should consider how socially isolated older adults might use computer-mediated communication such as social media to foster a sense of social connection.

Computational Propaganda in Hashtag Activism

The hashtag (#) has an important role in gathering Internet users' support for opinion and value. Computational propaganda has an important role in hashtag activism. This study wants to examine the role of computational propaganda actors such as anonymous political accounts, fake accounts and bot in social media that is able to mobilize the public and also increase the impression of Twitter audiences. The trend of Twitter hashtag activism #BebaskanIBHRS and #NegaraDamaiTanpaFPI began with the arrest of the chairman of the Islamic Defenders Front (FPI) Habib Rizieq Shihab (HRS); the two trending hashtags massively influenced public opinion on Twitter on December 13-14 2020. This study uses a sample of 1000 tweets or conversations on each hashtags and uses Social Network Analysis (SNA) with the Netlytic tool which is able to provide quantitative values of communication networks, through the social network structure of #BebaskaniBHRS and #NegaraDamaiTanpaFPI. This study reveals how the network structure and what factors are carried out by anonymous political actors in carrying out computational propaganda. The results of this study reveal the hashtags activism #BebaskaniBHRS is much more capable of mobilizing the public and is able to generate greater impressions than #NegaraDamaiTanpaFPI. This is because #BebaskaniBHRS has a computational propaganda message in the form of a loaded language with a clear frame and the choice of words directly invites the Twitter public to get involved through a retweet another finding in this research shows computational propaganda movement in hashtag activism was carried out by large groups consisting of anonymous accounts and bot accounts on other side online media coverage about the trending of these hashtag's activism was also able to increasing public attention. Tagar (#) memiliki peran penting dalam mengumpulkan dukungan pengguna Internet terhadap suatu opini dan nilai. Komputasi propaganda memiliki peran penting dalam aktivisme tagar. Penelitian ini ingin mengkaji peran aktor komputasi propaganda seperti akun anonim politik, akun palsu dan bot di media sosial yang mampu memobilisasi publik dan juga meningkatkan kesan khalayak Twitter. Tren aktivisme tagar Twitter #BebaskanIBHRS dan #NegaraDamaiTanpaFPI dimulai dengan penangkapan ketua Front Pembela Islam (FPI) Habib Rizieq Shihab (HRS); kedua tagar yang sedang trending tersebut secara masif memengaruhi opini publik di Twitter pada 13-14 Desember 2020. Penelitian ini menggunakan sampel 1000 tweet atau percakapan pada masing-masing tagar serta menggunakan Social Network Analysis (SNA) dengan alat Netlytic yang mampu memberikan nilai kuantitatif jaringan komunikasi. Melalui struktur jejaring sosial #BebaskaniBHRS dan #NegaraDamaiTanpaFPI, kajian ini mengungkap seperti apa struktur jaringan komunikasi dan hal apa saja yang dilakukan oleh aktor politik anonim dalam melakukan komputasi propaganda. Hasil penelitian ini mengungkapkan bahwa aktivisme tagar #BebaskaniBHRS jauh lebih mampu memobilisasi publik dan mampu menghasilkan impresi yang lebih besar dibandingkan #NegaraDamaiTanpaFPI. Hal ini dikarenakan #BebaskaniBHRS memiliki pesan komputasi propaganda dalam bentuk bahasa yang sarat dengan bingkai yang jelas dan pilihan kata secara langsung mengajak publik Twitter untuk terlibat melalui retweet.Temuan lain dalam penelitian ini menunjukkan gerakan komputasi propaganda dalam aktivisme tagar dilakukan oleh kelompok besar yang terdiri dari akun anonim dan akun bot di sisi lain liputan media daring tentang tren aktivisme tagar ini juga mampu meningkatkan atensi publik.

Polarized information ecosystems can reorganize social networks via information cascades

The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively “unimportant” story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others’ political identities. Observational follow network data collected on Twitter support this prediction: We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be “unimportant” news at the expense of missing out on subjectively “important” news far more frequently. This suggests that “echo chambers”—to the extent that they exist—may not echo so much as silence.

Social Network Structure as a Moderator of the Relationship between Psychological Capital and Job Satisfaction: Evidence from China

Although networking is reported to be a job search strategy in the literature, research on the interaction between social networking and other personal resources and its effect on job satisfaction is scarce. In the perspective of social networks, the present study explored whether the social network structure, which consists of network size and tie strength, moderates the relationship between psychological capital and job satisfaction. By using a two-wave longitudinal design, we collected the quantitative data (survey of 344 undergraduate students who were about to graduate soon) from 19 universities in Beijing city, Shandong Province, and Jiangsu Province in Eastern China. Factor analysis and hierarchical regression analysis were adopted to analyze the data of the survey. We found that psychological capital has a positive impact on job seekers’ job satisfaction. Furthermore, smaller networks and weaker ties in social networks both render the positive effect of psychological capital on job satisfaction even stronger.

Research on the Relationship between Social Support and Employment Quality of Chinese Athletes from the Perspective of Social Network Structure

Based on the theories of social network, social support, and retirement process, this study analyzes the source and composition of social support for Chinese athletes on the basis of constructing the social support network. Subsequently, we analyze the impact of social support on employment quality of Chinese athletes from different dimensions and further explore the mechanism of social support on the employment quality of athletes from the moderating role of athletes’ self-employment cognition. The study found that the social support network of athletes showed a clear tendency toward “strong ties,” and the social support they received mainly came from family members, teammates, and sports team managers. These kinds of social support will directly promote the employment quality of athletes after retirement. When athletes have full knowledge of their future employment status, the effect of social support in promoting employment quality will be further expanded.

Differences in social activity increase efficiency of contact tracing

Digital contact tracing has been suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we use smartphone proximity data to explore how social structure affects contact tracing of COVID-19. We model the spread of COVID-19 and find that the effectiveness of contact tracing depends strongly on social network structure and heterogeneous social activity. Contact tracing is shown to be remarkably effective in a workplace environment and the effectiveness depends strongly on the minimum duration of contact required to initiate quarantine. In a realistic social network, we find that forward contact tracing with immediate isolation can reduce an epidemic by more than 70%. In perspective, our findings highlight the necessity of incorporating social heterogeneity into models of mitigation strategies. Graphic abstract

A Social Network Structure Analysis of Former Smokers in Emerging Markets Using China’s Twitter

Objectives: Social networks are widely used for proping the process of tobacco control in emerging markets, but their formation and effects are not well understood. Using the micro blogging platform Sina (Sina Weibo, China’s Twitter) as an example, this article conducts a multi-agent simulation analysis of the Netlogo platform to analyze the micro-level behavioral characteristics of former smokers and macro-evolutionary law in the formation of social networks in emerging markets. The results show that the tobacco control in use of social networks have two characteristics: limitations on the size of the network and the in-degree and out-degree of its nodes as well as heterogeneous attributes of the nodes. This kind of network is better at simulating a real social network than small-world and scale-free networks.

Association between tuberculosis in men and social network structure in Kampala, Uganda

Background Globally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men’s contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases. Methods We compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household). Results The observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children. Conclusions This analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.

Effective population size for culturally evolving traits

ABSTRACTPopulation size has long been considered an important driver of cultural diversity and complexity. Results from population genetics, however, demonstrate that in populations with complex demographic structure or mode of inheritance, it is not the census population size, N, but the effective size of a population, Ne, that determines important evolutionary parameters. Here, we examine the concept of effective population size for traits that evolve culturally, through processes of innovation and social learning. We use mathematical and computational modeling approaches to investigate how cultural Ne and levels of diversity depend on (1) the way traits are learned, (2) population connectedness, and (3) social network structure. We show that one-to-many and frequency-dependent transmission can temporally or permanently lower effective population size compared to census numbers. We caution that migration and cultural exchange can have counter-intuitive effects on Ne. Network density in random networks leaves Ne unchanged, scale-free networks tend to decrease and small-world networks tend to increase Ne compared to census numbers. For one-to-many transmission and different network structures, effective size and cultural diversity are closely associated. For connectedness, however, even small amounts of migration and cultural exchange result in high diversity independently of Ne. Our results highlight the importance of carefully defining effective population size for cultural systems and show that inferring Ne requires detailed knowledge about underlying cultural and demographic processes.AUTHOR SUMMARYHuman populations show immense cultural diversity and researchers have regarded population size as an important driver of cultural variation and complexity. Our approach is based on cultural evolutionary theory which applies ideas about evolution to understand how cultural traits change over time. We employ insights from population genetics about the “effective” size of a population (i.e. the size that matters for important evolutionary outcomes) to understand how and when larger populations can be expected to be more culturally diverse. Specifically, we provide a formal derivation for cultural effective population size and use mathematical and computational models to study how effective size and cultural diversity depend on (1) the way culture is transmitted, (2) levels of migration and cultural exchange, as well as (3) social network structure. Our results highlight the importance of effective sizes for cultural evolution and provide heuristics for empirical researchers to decide when census numbers could be used as proxies for the theoretically relevant effective numbers and when they should not.