中国微博数据下主题对明星活跃粉丝数的影响

IF 1.5 4区 物理与天体物理 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Modern Physics C Pub Date : 2024-05-28 DOI:10.1142/s0129183124420038
Gaogao Dong, Wenqi Shi, Zhipeng Sun, Fan Wang, Jianguo Liu
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引用次数: 0

摘要

网络社交媒体平台已成为促进社交互动不可或缺的渠道,名人利用这些平台与其粉丝群互动,并培养了大量粉丝。粉丝群体通常被称为 "活跃粉丝",他们积极与名人交流,并积极参与名人活动的相关讨论。对于名人来说,留住并增加活跃粉丝数量的任务意义重大,因为这可以显著提升他们的社会影响力和商业价值。在此,我们利用中国最大的社交媒体平台--新浪微博上 2021 年的数据,构建了动态加权活跃粉丝网络。通过对活跃粉丝的网络结构、增长率和持续时间进行比较分析,我们深入研究了代言、综艺、公益、体育、音乐和国家事务这六个不同主题类别的影响力。分析对象包括演员、歌手、网络影响者、主播和运动员等五个不同领域的九位名人。名人粉丝的成长轨迹和生命周期在上述主题内和主题间都表现出明显的差异。这些动态变化还受到每个名人的个人粉丝网络固有结构属性的影响。我们采用 K 型时间序列聚类算法,对活跃粉丝中观察到的爆发增长模式进行了深入探讨,并通过比较分析确定聚类数量的最佳值为 k=4。我们的研究结果表明,代言和公益主题呈现出所有四种增长模式,即双峰、振荡、单峰和持续增长模式。与此相反,如果把所有主题放在一起考虑,则它们共同呈现出单峰衰减增长模式。本研究得出的启示不仅对渴望提升和扩大社会影响力的不同领域的名人具有重要的参考和指导意义,而且还能为蓬勃发展的粉丝经济做出贡献。此外,本研究还为仔细研究粉丝增长模式及其相应动态提供了新的视角和见解。
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The effect of theme on the number of celebrity active fans under China Weibo data

Online social media platforms have emerged as integral channels for facilitating social interactions, with celebrities utilizing these platforms to engage with their fan base and cultivate a substantial following. The group of engaged fans, commonly referred to as “active fans”, represents individuals who actively communicate with celebrities and actively participate in discussions pertaining to the celebrities’ endeavors. For celebrities, the task of retaining and augmenting the count of active fans holds immense significance, as it significantly amplifies their social impact and commercial value. Here, we construct dynamic weighted active fan networks by leveraging data from 2021 on Sina Weibo, which stands as China’s largest social media platform. Through a comparative analysis encompassing the network’s structure, the growth rate and the duration of active fans, we delve into the influence wielded by six distinct thematic categories, endorsement, variety, public welfare, sports, music and national affairs. This analysis covers a cohort of nine celebrities spanning five diverse domains, including actors, singers, online influencers, anchors and athletes. The growth trajectory and life cycle exhibited by celebrity fans exhibit notable variations, both within and across the aforementioned themes. These dynamics are further influenced by the inherent structural attributes of the personal fan network belonging to each celebrity. Employing the K-Shape time series clustering algorithm, we have undertaken an in-depth exploration of outburst growth patterns observed in active fans and determined the optimal value of the number of clusters to be k=4 through comparative analysis. Our findings underscore that the themes of endorsement and public welfare exhibit all four growth patterns, namely Double-Peak, Oscillatory, Single-Peak and Continuous Growth Patterns. In contrast, when considering all themes collectively, they collectively demonstrate a single-peaked decaying growth pattern the insights gleaned from this study not only serve as a valuable reference and guide for celebrities across diverse domains who aspire to bolster and augment their social influence but also contribute to the burgeoning fan economy. Moreover, this research introduces novel perspectives and insights for scrutinizing patterns of fan growth and their corresponding dynamics.

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来源期刊
International Journal of Modern Physics C
International Journal of Modern Physics C 物理-计算机:跨学科应用
CiteScore
3.00
自引率
15.80%
发文量
158
审稿时长
4 months
期刊介绍: International Journal of Modern Physics C (IJMPC) is a journal dedicated to Computational Physics and aims at publishing both review and research articles on the use of computers to advance knowledge in physical sciences and the use of physical analogies in computation. Topics covered include: algorithms; computational biophysics; computational fluid dynamics; statistical physics; complex systems; computer and information science; condensed matter physics, materials science; socio- and econophysics; data analysis and computation in experimental physics; environmental physics; traffic modelling; physical computation including neural nets, cellular automata and genetic algorithms.
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