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This study argues that online user comments on social media platforms provide feedback and evaluation functions. These functions can provide services for the relevant departments of organizations or institutions to formulate corresponding public opinion response strategies.

This study considers Chinese universities’ public opinion events on the Weibo platform as the research object. It collects online comments on Chinese universities’ network public opinion governance strategy texts on Weibo, constructs the sentiment index based on sentiment analysis and evaluates the effectiveness of the network public opinion governance strategy adopted by university officials.

This study found the following: First, a complete information release process can effectively improve the effect of public opinion governance strategies. Second, the effect of network public opinion governance strategies was significantly influenced by the type of public opinion event. Finally, the effect of public opinion governance strategies is closely related to the severity of punishment for the subjects involved.

The theoretical contribution of this study lies in the application of image repair theory and strategies in the field of network public opinion governance, which further broadens the scope of the application of image repair theory and strategies.

This study expands online user comment research to network public opinion governance and provides a quantitative method for evaluating the effect of governance strategies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0269

The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

The purpose of this paper is to use Weibo as a window to examine the Chinese netizens’ online attitudes and responses to two sets of population policy: the Selective Two-Child Policy (Phase 2) and the Universal Two-Child Policy. The population policy change from the rigid One-Child Policy to the Selective Two-Child Policy then to the Universal Two-Child Policy aroused great attention of the Chinese people.

This research uses the crawler technique to extract data on the Sina Weibo platform. Through opinion mining of Weibo posts on two sets of population policy, the Weibo users’ online opinions on the Two-Child Policy are analyzed from two perspectives: their attention intensity and sentiment tendency. The research also use the State Bureau of Statistics of China’s national population data between 2011 and 2016 to examine the Chinese people’s actual birth behaviors after implementing the two different sets of the Two-Child Policy.

The research findings indicate that the Selective Two-Child Policy (Phase 2) and the Universal Two-Child Policy are good examples of thematic public sphere of Weibo. Weibo posts on the two sets of the Two-Child Policy have undergone different opinion cycles. People from economically developed regions and populous regions have paid more attention to both sets of Two-Child Policy than their counterparts in the less developed and less populated regions. Men pay more attention to the Two-Child Policy than women do. Despite people’s huge attention to the new population policy, the population growth after the policy is not sustainable.

The new population policy alone is difficult to boost China’s population within a short period of time. The Chinese Government must provide its people with enough incentives and supporting welfare to make the population growth happen.

These findings have important implications for understanding the dynamics of online opinion formation and changing population policy in China.

The purpose of this paper is to explore factors influencing social media usage effectiveness of publishing-related entities and discuss the differences between publishing-related individuals and organizations in their usage of social media and the differences between commercial organizations and public service organizations (such as libraries).

This paper studied 546 publishing-related entities’ accounts on the leading Chinese social media and built a theoretical model for the usage of social media by publishing-related entities. Furthermore, it examined the influencing factors from two aspects: power of influence of an entity (entity influence) and the relational interaction of a publishing-related entity with its audience (interactive relationship).

The study found that for publishing-related individual users of social media, entity influence has a greater positive effect on the effectiveness of social media usage than on the effect of interactive relationship. For publishing-related commercial organizations, the entity influence and interactive relationship have equal impacts on the effectiveness of social media usage. It is also found that interactive relationship has a stronger positive effect on the usage effectiveness of social media, especially for publishing-related public service organizations.

This research fills the gap in the research of comparative analysis in the understanding of social media usage by individuals and organizations related to publishing activities. Moreover, it has tried to propose a theoretical model that can help promote the effectiveness of social media usages by various publishing-related entities and their business strategies.

Information opacity in donation crowdfunding activities has constrained the healthy development of China’s public welfare activities. Addressing the trust crisis and enhancing public engagement warrants further investigation. This study aims to uncover the moderating effect of activity transparency by utilizing data from 1,029 donation crowdfunding projects on the Sina Weibo Public Welfare Social Platform. In this way, we seek to elucidate the impact of donation crowdfunding events on fundraising ability.

This study selects text complexity, number of supporters, creator experience, and social capital as explanatory variables; innovatively selects the number of updates of online crowdfunding activities and total reading volume as moderating variables; selects the number of shares of crowdfunding activities as a mediating variable; and constructs a moderated mediation multiple regression model for fundraising ability.

Our findings indicate that independent variables, such as text complexity, number of supporters, and social capital, can significantly affect the dependent variable, fundraising ability. However, creator experience does not influence fundraising ability. Furthermore, social interaction has a mediating effect, whereas activity transparency has a reverse moderating effect. These results indicate that social interaction can enhance the fundraising ability of donation crowdfunding events. However, with an increase in information transparency, the fundraising ability of social media decreases.

The originality of this research is in clarifying the internal factors affecting fundraising ability through induction, making bold assumptions, and focusing on how social media’s effective interaction and activity transparency will affect public welfare crowdfunding fundraising ability.

Following the growing adoption of social media, many entrepreneurs are launching personal social media channels. This study focuses on the effect of entrepreneurs' shared information on We Media platforms on the value relevance of their earnings.

Using entrepreneurs' We Media data collected from A-share-listed companies on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2018, this study investigates the effect of the data on the value relevance of earnings using the modified Ohlson model. The authors applied textual analysis to retrieve entrepreneurial We Media data acquired manually from Weibo.

We Media platforms can increase the value relevance of earnings. Entrepreneurs can enhance investor trust by establishing social ties with investors. Investors are more likely to recognize earnings information publicized by enterprises, owing to internal consistency. Particularly, value relevance improves significantly with more personal information being posted and more “likes” being acquired on entrepreneurs' We Media accounts. This positive effect is more obvious in privately owned and highly marketized regions.

The findings extend the research on the economic consequences of We Media as an important information channel, enrich the research on the social media posting behavior of entrepreneurs and provide a reference for enterprises to instill trust using new information disclosure methods and for governments to establish a safe internet environment to promote the sustainable development of the capital market.

This study explores whether and how four main factors of short-branded video content (content matching, information relevance, storytelling and emotionality) facilitate consumer engagement (likes, comments and shares), as well as the moderating effect of the release time (morning, afternoon and evening) in such relationships.

This study uses Python to write programs to crawl relevant data information, such as consumer engagement and short video release time. It combines coding methods to empirically analyze the impact of short-branded video content characteristics on consumer engagement. A total of 10,240 Weibo short videos (total duration: 238.645 h) from 122 well-known brands are utilized as research objects.

Empirical results show that the content characteristics of short videos significantly affected consumer engagement. Furthermore, the release time of videos significantly moderated the relationship between the emotionality of short videos and consumer engagement. Content released in the morning enhanced the positive impact of warmth, excitement and joy on consumer engagement, compared to that released in the afternoon.

The findings provide new insights for the dissemination of products and brand culture through short videos. The authors suggest that enterprises that use brand videos consider content matching, information relevance, storytelling and emotionality in their design.

From a broader perspective, this study constructs a new method for comprehensively evaluating short-branded video content, based on four dimensions (content matching, information relevance, storytelling and emotionality) and explores the value of these dimensions for creating social media marketing success, such as via consumer engagement.

Employing big data analysis tools, this study examines the significance of supply chain integration affecting online financial consumption, analyzes the online financial consumption demand of mobile phone consumers, promotes the optimization of supply chain services with consumers as the focus and proposes full integration of a mobile phone supply chain in terms of product, logistics and marketing, in order to improve the supply and demand relationship between consumers and suppliers; the overall objective is to promote further development of online financial consumption.

In this study, TF-IDF (term frequency–inverse document frequency) and cosine similarity text analysis are used for analyzing online demand for mobile phone products, studying the influence of supply chain services on consumption demand and identifying strategies for promoting overall optimization of the supply chain to meet online financial consumption demands of consumers; the study analyzes online reviews on mobile phone topics from the JingDong (JD) platform and Weibo platform.

Research results show that online demand for mobile phone products is greatly influenced by supply chain links such as product design, logistics transportation and marketing promotion. The consumption demand for different mobile phone products has different emphases, but the differences are not significant. The overall improvement of the supply chain should focus on product research and development, logistics layout optimization and marketing promotion, in order to meet and guide the online financial demand of consumers and improve the effectiveness of supply chain management.

This study only considered data from China's largest online mobile phone sales platform and Weibo text data owing to the data sensitivity involved.

There are few supply chain optimization studies based on online financial consumption reviews from customers. Therefore, this study integrates online consumption trends into a supply chain analysis framework to explore strategies for promoting supply chain optimization according to customer demands, improving the benign interaction of participants in the supply chain and promoting the development of online financial consumption.

Social media brand platforms have become a popular means for engaged customers to share information and experiences with brands and other customers. However, empirical research on how customer engagement (CE) relates to customers’ sharing intentions with the brand is limited. This study aims to investigate causal patterns of four CE dimensions – focused attention, absorption, enthusiasm and interaction – together with two cognitive structure properties in stimulating sharing intentions with the brand.

Using data from 782 Chinese customers of brand pages on the social media platform Weibo, this paper is the first to use both finite mixture partial least squares (FIMIX-PLS) analysis and fuzzy-set qualitative comparative analysis (fsQCA) to empirically assess the impact of CE configurations on sharing intentions.

The findings imply that not all of the CE dimensions co-occur necessarily and that different configurations of them can produce superior sharing intentions, conditional on the cognitive structure of customers, including their level of brand knowledge and avant-gardism.

Although restricted to customers on Weibo, the results inform practice about how social media technology can facilitate different CE configurations and customer sharing intentions.

The results inform brand managers’ segmentation efforts and CE content marketing initiatives that can induce different CE configurations and customer sharing intentions with customers that possess high avant-gard and brand knowledge characteristics.

This study is the first to substantiate how different CE configurations (as gestalts) affect sharing intentions in social media and to challenge conventional net-effects thinking about CE dimensions. Understanding how such conditional configurations foster sharing via a social media platform is advantageous because it can improve segmentation efforts to strengthen brand relationships.