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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

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Triggered by the extensive use of social media brand communities (SMBCs) in interactive marketing, this article aims to explore how brand connectedness (BC) affects consumer engagement behavior (CEB) in SMBCs.

The research model was verified with the partial least squares structural equation modeling applied to the actual data collected from the web crawling largest microblogging platform in China (Sina Weibo).

Results indicate that BC may positively influence consumer emotions (CEs), eventually leading to engagement behavior in SMBCs. In addition, gender and duration of membership act as vital moderators in the model. One of the most interesting findings is the differences between posting and commenting, although both are CEBs. BC has a more significant effect on commenting than posting, and the mediating effect of CEs between BC and posting behavior is not significant.

This research contributes to the literature on interactive marketing by examining BC in the context of SMBCs, which is under-researched in the literature but is highly pertinent to social media contexts. Moreover, we measure BC through social network analysis for the first time, which not only supports the empirical work but also expands the social network theory and social capital theory. This research also extends the body of knowledge on consumer engagement by investigating the differences between posting and commenting behaviors.

This exploratory study aims to investigate the use of Facebook as a dialogic accounting tool for promoting citizen engagement in Italian regions.

This study adopts a mixed methodology. Indeed, it first collects some quantitative data to construct an engagement index for the Facebook pages of the Italian regions, and then it performs a content analysis of some posts while also examining the tenor of the related comments and the level of interaction between regions and citizens.

The Italian regions have mainly used their Facebook pages for public communication purposes rather than for public participation. Therefore, they have conceived social pages more as an instrument of self-legitimisation and thus monologic accounting and have rarely considered them as a tool for engaging citizens who, in turn, showed low interest in participating in online debates. Nature and environment, tourism promotion and sport were the most engaging content types. Findings also confirm that posting many messages does not automatically increase engagement.

This study is one of the first to investigate the potential of social media from a dialogic accounting perspective, especially in the public sector. Additionally, it focuses on regions which are understudied in the literature, although they are critical actors in implementing public policies. Last but not least, this study offers a framework that integrates the literature on the use of social media for citizen engagement and research on such platforms as dialogic accounting tools.

The study seeks to contribute to a deeper understanding of the relationship between remediations and participation in new media. By lending some transparency, the analysis hopes to contribute toward generating a critical optics aware of the potentials and pitfalls of emergent media.

The methodology is visual semiotic analysis. The author make no claim for one, true interpretation or critical judgment about the images.

In demonstrating some shortfalls of Instagram affordances, the analysis shows how social media sites can develop tools that encourage users to engage in civic consciousness and respectful political debate. The study makes clear that new media tools can hamper or aid participatory logics.

To author’s knowledge, no other study that has analyzed remediated images related to the controversial confirmation of Brett Kavanaugh to the U.S. Supreme Court. It is also important to place these images in the contexts of “iconicity” in emergent media (a concept increasingly being eroded in new media environment).

Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.

In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.

Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.

The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.

The authors explore how the sentiment expressed by emojis in comments on stocks is associated with the stocks' subsequent returns.

By applying our own analyzer, the authors find a sentiment effect of emojis on stocks returns separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic.

The authors document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment posts over the previous hour is associated with an 0.06% (annualized: 109.2%) one-hour abnormal stock return compared to a mean of 0.03% (annualized: 54.6%). If the stock exhibits a higher intra-hour volatility, a proxy for uninformed noise trading, this relation is more pronounced and even stronger compared to stock return's relation to plain text sentiment.

The authors are not able to show causation that is open to future research. It also remains an open question how emojis impact market price efficiency.

Emojis are positively related to stock returns in addition to plain text-expressed content if they are discussed heavily by retail investors in Internet boards such as Reddit.

Shared emotions expressed by emojis might have an influence on how disconnected individuals make homogeneous decisions. This argument might explain our found relation of emojis and stock returns.

So, the study findings provide empirical evidence that emojis in Reddit posts convey information on future short-term stocks returns distinct from information expressed in plain text, in the case of volatile stocks, with a higher magnitude.

Social media sites contribute significantly to nature conservation in, that they enlighten and educate those members of the public who would ordinarily not be in a position, or would not be fortunate enough to visit the park and experience the various aspects first-hand. The purpose of this paper is to showcase social media pages related to a national game reserve in South Africa. This game reserve is the largest in the country and has a wide variety of conserved fauna and flora.

The theoretical framework used is the SCOPE framework that streamlines strategy development, content choice, refinement of online engagement, choice of social media platform and evaluations of social media campaigns.

The findings relate to the content found on these social media pages, as well as how members of the public interact with each other and officials from the game reserve in sharing experiences related to this wilderness area.

The research is related to the Kruger National Park in South Africa and is limited to three social media sites.

Through its social media presence, this South African game reserve is able to share experiences from what is effectively a living museum, as well as from its library and archives, with members of the public and allowing individual members to share their encounters with wildlife and their historical memories of this wilderness area.

This paper aims to examine sites of dissonance or consensus between global investor responses to the draft standards, International Financial Reporting Standards S1 (IFRS) (General Requirements for Disclosure of Sustainability-related Financial Information) and IFRS S2 (Climate-related Disclosures), issued by the International Sustainability Standards Board (ISSB).

A thematic content analysis was used to capture investor views expressed in their comment letters submitted in the consultation period (March to July 2022) in comparison to the ex ante position (issue of draft standards, March 2022) and ex post summary feedback (ISSB staff papers, September 2022) of the ISSB.

There was investor consensus in support of the ISSB and the development of the draft standards. However, there were sites of dissonance between investors and the ISSB, notably regarding the basis and focus of reporting (double or single/financial materiality and enterprise value); definitional clarity; emissions reporting; and assurance. Incrementally, the research further highlights that investors display heterogeneity of opinion.

The ISSB standards will provide a framework for future sustainability reporting. This research highlights the significance of such reporting to investors through their responses to the draft standards. The findings reveal sites of dissonance in the development and alignment of draft standards to user needs. The views of investors, as primary users, should help inform the development of sustainability-related standards by a global standard-setting body apposite to current policy and future reporting requirements, and their usefulness to users in practice.

To the best of the authors’ knowledge, this paper makes an original contribution to the comment letter literature, hitherto focused on financial reporting with a relative lack of investor engagement. Using thematic analysis, sites of dissonance are examined between the views of investors and the ISSB on their development of sustainability reporting standards.