团队

社交媒体盛行，不少人都会在平台上分享新闻，令新闻的传播效果更加显著，企业亦因此十分留心社交媒体上的新闻讨论热点，以便更贴近「民情」，亦慎防公关危机的发生。 港大经管学院市场学、创新及资讯管理学副教授潘光俊，早前就大众阅读新闻和分享社交媒体的行为发表研究报告，发现文章内容对大众阅读新闻和社交媒体分享的行为有影响。他指出，读者倾向在新闻网站阅读负面新闻，但在社交媒体上，却倾向分享正面报道，「目的是为『推广』自己，予人一个正面形象，并非侧重所分享的新闻本身。」研究并发现，较深的文章亦倾向多人分享，但实际阅读的人数却很少

As coronavirus rages across the globe, online business is still booming, with data and analytics driving this trend. People now marooned at home for the foreseeable future are finding the daily goods they need from online stores, solace in conferencing apps, and entertainment provided by streaming platforms. The world is revolving increasingly online with lockdowns in place, and data is being even further highlighted as an undisputable source of wealth.

潘博士是一位学者，商业顾问，企业家和标准舞舞者。作为人工智能和金融科技领域的专家，他最近加入了香港大学，并相信香港将会在中美的科技革命中的历史时刻中扮演着关键的角色。

With the rapid growth of online social network sites (SNSs), it has become imperative for platform owners and online marketers to quantify what factors drive content production on these platforms. Previous research identified challenges in modeling these factors statistically using observational data, where the key difficulty is the inability of conventional methods to disentangle the effects of network formation and network influence on content generation from the subsequent feedback effect of newly generated content on network structure. In this paper, we adopt and enhance an actor-oriented continuous-time statistical model that enables the joint estimation of the coevolution of the users’ social network structure and of the amount of content they produce, using a Markov chain Monte Carlo–based simulation approach. Specifically, we offer a method to analyze nonstationary and continuous-time behavioral data, typically recorded in social media ecosystems, in the presence of network effects and other observable and unobservable user-specific covariates. The proposed method can help disentangle network effects of interest from feedback effects on the network. We apply our model to social network and public posting data over six months to find that (1) users tend to connect with others that have similar posting behavior; (2) however, after doing so, these users tend to diverge in their posting behavior, and (3) peer influence effects are sensitive to the strength of the posting behavior. More broadly, the proposed method provides researchers and practitioners with a statistically rigorous approach to analyze network effects in observational data. Our results lead to insights and recommendations for SNS platform owners on how to sustain an active and viable community.