Who creates Trends in Online Social Media: The Crowd or Opinion Leaders?

Zhang, Leihan; Zhao, Jichang; Xu, Ke

doi:10.1111/jcc4.12145

Cited by 99 publications

(90 citation statements)

References 47 publications

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“…Cha, Haddadi, Benevenuto, and Gummadi (2010) also examined the dynamics of user influence on topics over time according to three indicators of indegree, retweets, and mentions and came to the similar conclusion. Likewise, L. Zhang et al (2015) identified the opinion leaders on Weibo by measuring the ratio of #Followers and #Followees among the users they harvested. Specifically, through plotting, L. Zhang et al's (2015) study treated the users with fewer followers and tweets as the non-opinion leaders, whereas the users with more followers and tweets as influential users.…”

Section: Identifying Opinion Leadersmentioning

confidence: 99%

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Exploring the effect of Weibo opinion leaders on the dynamics of public opinion in China: A revisit of the two-step flow of communication

2019

Global Media and China

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Using a random sample of posts on Weibo, this study content analyzes the extent to which Weibo opinion leaders' tweets are associated with the shifts and variations of public opinion-in terms of topic salience, emotional reactions, agent of responsibility, motivation for condemnation, rhetorical techniques, conviction of blame, and emotional intensity-in the case of the Tianjin Explosion. Building upon a burgeoning research agenda examining the role of Chinese Weibo in the context of two-step flow, this study found that opinion leaders generally reinforce Weibo users' subjective assessment (e.g., attitudes and emotional responses) of the incident. An overwhelming sleeper effect is also observed in that most posts by the opinion leaders did not contribute to peaks until 2-3 days later. All opinion leaders achieved between one and three peaks.

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Section: Identifying Opinion Leadersmentioning

confidence: 99%

“…First and foremost, adapting from L. Zhang et al (2015)'s measure, among all users in our dataset, I plotted the ratios of the followers of followees using Python. Figure 1 plots #Tweets as a function of the ratio between followers and followees.…”

Section: Identifying Opinion Leadersmentioning

confidence: 99%

Exploring the effect of Weibo opinion leaders on the dynamics of public opinion in China: A revisit of the two-step flow of communication

2019

Global Media and China

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“…However, these observational studies are limited by familiar problems of identification that arise from the inability to eliminate the confounding influences of institutional mechanisms. As a result, previous empirical research has been unable to identify the collective dynamics through which social conventions can spontaneously emerge (8,(34)(35)(36).…”

mentioning

confidence: 99%

The spontaneous emergence of conventions: An experimental study of cultural evolution

Centola

Baronchelli

2015

Proc. Natl. Acad. Sci. U.S.A.

193

184

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How do shared conventions emerge in complex decentralized social systems? This question engages fields as diverse as linguistics, sociology, and cognitive science. Previous empirical attempts to solve this puzzle all presuppose that formal or informal institutions, such as incentives for global agreement, coordinated leadership, or aggregated information about the population, are needed to facilitate a solution. Evolutionary theories of social conventions, by contrast, hypothesize that such institutions are not necessary in order for social conventions to form. However, empirical tests of this hypothesis have been hindered by the difficulties of evaluating the real-time creation of new collective behaviors in large decentralized populations. Here, we present experimental results-replicated at several scales-that demonstrate the spontaneous creation of universally adopted social conventions and show how simple changes in a population's network structure can direct the dynamics of norm formation, driving human populations with no ambition for large scale coordination to rapidly evolve shared social conventions.social conventions | spontaneous emergence | complex systems | empirical testing | network science

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“…Basic dynamics of the meme diffusion within the same media has been comprehensively studied from different perspectives. For example, mathematical epidemiology as well as simple log-normal distributions are suggested to profile the growth and decline of diffusion [2,23,34,36], how competition, homogeneity and network cooperatively affect the spread is discussed [8-10, 16, 17, 37], the different roles in diffusion played by different individuals are revealed by [4] and [47] and even simulation models are established to replicate the meme diffusion in Twitter [3,[41][42][43][44]46]. However, except disclosing the common features of successful memes in different online social networks [11,12,33,35], the universal mechanism that essentially drives the propagation of memes in different media still remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Sleeping beauties in meme diffusion

Zhang

Zhao

2017

Scientometrics

Self Cite

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A sleeping beauty in diffusion indicates that the information, can be ideas or innovations, will experience a hibernation before a sudden spike of popularity and it is widely found in citation history of scientific publications. However, in this study, we demonstrate that the sleeping beauty is an interesting and unexceptional phenomenon in information diffusion and even more inspiring, there exist two consecutive sleeping beauties in the entire lifetime of propagation, suggesting that the information, including scientific topics, search queries or Wikipedia entries, which we call memes, will go unnoticed for a period and suddenly attracts some attention, and then it falls asleep again and later wakes up with another unexpected popularity peak. Further explorations on this phenomenon show that intervals between two wake ups follow an exponential distribution and the second wake up generally reaches its peak at a higher velocity. In addition, higher volume of the first wake up will lead to even much higher popularity of the second wake up with great odds.Taking these findings into consideration, an upgraded Bass model is presented to well describe the diffusion dynamics of memes on different media. Our results can help understand the common mechanism behind propagation of different memes and are instructive to locate the tipping point in marketing or find innovative publications in science.

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Who creates Trends in Online Social Media: The Crowd or Opinion Leaders?

Cited by 99 publications

References 47 publications

Exploring the effect of Weibo opinion leaders on the dynamics of public opinion in China: A revisit of the two-step flow of communication

Exploring the effect of Weibo opinion leaders on the dynamics of public opinion in China: A revisit of the two-step flow of communication

The spontaneous emergence of conventions: An experimental study of cultural evolution

Sleeping beauties in meme diffusion

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