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This paper offers an overview of the status of and emerging trends in environmental, social, and governance (ESG) research through a bibliometric approach using CiteSpace. In particular, our study aimed to elucidate the overall intellectual structure of the environmental, social, and governance academic field. To this end, we performed a topic search related to the environmental, social, and governance field and gathered published articles (2007–2021) from the Web of Science. Subsequently, we identified productive authors, institutes, and countries/regions to determine main research forces in the environmental, social, and governance field. Additionally, we conducted a co-citation analysis to identify highly cited authors, journals, and literatures in the environmental, social, and governance field. Furthermore, we performed a literature-co-citation-based cluster analysis and literature citation burst analysis to confirm the main themes and hotspots of the environmental, social, and governance field. These analyses can contribute to the investigations of key contributing forces in the environmental, social, and governance field at the author, institution, country/region, and journal levels and provide insights into the knowledge structures and orientations of the environmental, social, and governance field for future research.

Purpose This study aims to examine how the Chinese climate nongovernmental organization “Chinese Weather Enthusiasts” engaged youth through video strategies. Design/methodology/approach The research proposed a framework grounded in the 5W model and message sensation value (MSV) to analyze the relationship between video content and user interaction. It categorized Bilibili videos into outer and inner features and introduced rhetorical strategies as content elements. A hybrid video coding framework was used, combining machine learning and deep learning (computer vision) for analyzing formal features, while manual coding was used for content features. Findings The results revealed that video length, long shots and the number of scenes positively influenced coins and favorites, whereas personification had a negative impact. In addition, tone and language intensity were positively correlated with user engagement. Originality/value This study offers insights regarding video production for climate communication, broadening the focus from text and images to video content and providing evidence-based guidance for practitioners.

In light of China’s mandatory garbage sorting policy, residents’ engagement in waste sorting tends to be short-term. To address this issue, this study proposes a conceptual framework to examine the relationships among motivation, green self-perception, and proactive garbage sorting behavior (PGSB). A total of 1550 questionnaires were collected across 31 provinces in China. Confirmatory factor analysis (CFA) was conducted to assess measurement quality, and hierarchical regression combined with bootstrapping was employed to test the parallel mediating effects of green self-perception and its three dimensions (green self-identity, green self-efficacy, and green self-connection). The results indicate that both obligation-based and enjoyment-based intrinsic motivations are positively associated with PGSB and its sub-dimensions. Green self-perception shows a statistical indirect effect in the relationship between intrinsic motivation and behavior. Specifically, green self-identity and green self-efficacy serve as consistent and significant mediators across all behavioral outcomes. In contrast, the mediating role of green self-connection varies across behavioral types. For obligation-based motivation, it only acts as a significant negative mediator for constrained proactive garbage sorting, with no significant effect on other behaviors. For enjoyment-based motivation, it exerts a positive mediating effect on self-development PGSB but suppresses participatory and constrained PGSBs. These findings suggest that fostering green self-perception may be an important pathway associated with PGSB. The study provides policy-relevant insights for shifting residents from compliance-driven to more self-initiated participation in waste sorting.

This study examines the longitudinal evolution of media framing of TikTok in South Korean news coverage from 2020 to 2024. As a global digital platform increasingly embedded in geopolitical and regulatory controversies, TikTok provides an instructive case for understanding how media frames shift over time in response to external political pressures. Moving beyond static framing analyses and Western-centric perspectives, this study conceptualizes framing as a dynamic process shaped by both short-term events and longer-term structural change. Using 5660 TikTok-related news articles from the BIGKinds database, we apply large language model-assisted frame classification and construct a frame shift index (FSI) to measure temporal changes in dominant frames. Interrupted time series (ITS) analysis is employed to test short-term framing responses to discrete international political and policy events, while the Bai–Perron breakpoint test (BPT) is used to identify long-term structural breaks. The results show that significant frame shifts are closely associated with transnational policy disputes and international political conflicts. While ITS reveals clear event-driven short-term framing adjustments, BPT identifies a statistically significant structural breakpoint in late 2022, indicating a longer-term reorganization of media narratives under sustained geopolitical and regulatory pressures.

Objective: This study aims to identify content variables that theoretical research suggests should be considered as strategic approaches to facilitate science communication with the public and to assess their practical effects on user engagement metrics. Methods: Data were collected from the official Chinese TikTok account (Douyin) of the Nutrition Research Institute of China National Cereals, Oils and Foodstuffs Corporation, China’s largest state-owned food processing conglomerate. Dependent variables included likes, shares, comments, subscription increases. Independent variables encompassed explanation of jargon (metaphor, personification, science visualization), communication remarks (conclusion presence, recommendation presence), and content themes. Descriptive analysis and negative binomial regression were employed, with statistical significance set at 0.05. Results: First, subscription increases were positively associated with personification (p < 0.05, 0.024) and science visualization (p < 0.01, 0.000). Second, a positive relationship existed between comments and communicator recommendations (p < 0.01, 0.000), while presenting conclusions negatively correlated with shares (p < 0.05, 0.012). Conclusions: Different strategies yielded improvements in various engagement metrics. This can provide practical guidance for communicators, emphasizing the need to balance scholarly presentation with sustaining appealing statistics.

The impact of corporate crisis communication strategies on users’ attitudes across different platforms has emerged as a new focal point in crisis management. This study is rooted in Social Media Crisis Communication (SMCC) and employs the case of Trump’s sanctions on TikTok to analyze variations in the effects of ByteDance’s crisis communication strategies on different social media platforms. We initally identified five announcements that generated significant discussions on Toutiao and Weibo and collected the corresponding user comments (a total of 50,702). Subsequently, we utilized two approaches, machine learning and deep learning, to conduct sentiment classification tests on the text to identify the best-performing model. This model was then applied on the entire dataset for sentiment classification, followed by semantic network analysis based on the sentiment classification results. The results demostrated that the pre-trained ERNIE model outperformed the other tested models (F1 = 82.40%). Following the fourth crisis communication event, users on Toutiao and Weibo exhibited contrasting sentimental tendencies. Theoretically, we observed that users on different social media platforms relying on distinct information sources, expressed different sentimental responses to the same crisis. Social media users have a tendency to anthropomorphize corporate personality traits. In practical terms, we recommend that companies engage in crisis communication on multiple social media platforms and do not overlook the most influential platforms in the market.

AimThe accessibility of social media data has allowed researchers to measure official–public interactions during COVID-19. However, previous work analyzing official posts or public comments has failed to explore the link between the two. Therefore, this study investigates the relationship between the communication strategies of public health agencies (PHAs) on TikTok and public emotional/sentiment tendencies in COVID-19 normalization.Subject and methodsThis study uses the 2022 Shanghai city closure event as a public health communication case study in the context of COVID-19 normalization, using TikTok as a data source. We first analyze the communication strategies adopted by the PHA based on the Crisis and Emergency Risk Communication (CERC) model. Then, we classify the sentiment of public comments using the Large-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation (ERNIE) pre-training model. Finally, we explore the connection between PHA communication strategies and public sentiment tendencies.ResultsFirst, the public’s sentiment tendencies differ at different stages. Therefore, appropriate communication strategies should be developed stage-by-stage. Second, the public’s emotional disposition to different communication strategies varies: government statements, vaccines, and prevention and control programs are more likely to produce a friendly comment environment, while policy and new cases per day are more likely to produce unfavorable comment content. However, this does not mean that policy and new cases per day should be avoided; the judicious use of these two strategies can help PHAs understand the current issues causing public dissatisfaction. Third, videos with celebrity appearances can significantly increase positive public sentiment and, thereby, public participation.ConclusionWe propose an improved CERC guideline for China based on the Shanghai lockdown case.

Environmental, social, and governance (ESG) disclosure has become an important source of information for external stakeholders. As sustainability reporting has expanded, distinguishing disclosure quantity from disclosure quality has become increasingly important. This study examines how sustainability disclosure quality is associated with external ESG evaluation outcomes among Standard & Poor’s (S&P) 500 Energy Sector firms. ESG-related claims were identified and classified from sustainability reports using large language model (LLM)-assisted structured content analysis. Based on the resulting corpus, three disclosure quality indicators were constructed: the Quantitative Evidence Ratio (QER), the Target Accountability Ratio (TAR), and the Reporting Infrastructure Score (RIS). These indicators were integrated into a composite Disclosure Quality Index (DQI) and examined in relation to S&P Global ESG Scores using Spearman’s rank correlation. The results indicated limited positive associations for the individual indicators, whereas the composite DQI showed a more pronounced positive relationship. Disclosure–rating divergence patterns were also observed, indicating that relatively favorable disclosure quality positions do not consistently correspond to higher ESG Score rankings. Overall, the findings suggested that sustainability disclosure quality may be multidimensional and that its association with external ESG evaluation outcomes became more apparent when disclosure characteristics were considered in combination. However, because the analysis is restricted to a small sample of S&P 500 Energy Sector firms, the findings should be interpreted as exploratory sector-specific evidence with limited generalizability.

Chatbots have shown great potential for language learning. However, previous studies have reported mixed results on the efficiency of chatbot-assisted language learning (CALL). This study integrated the results of previous experimental studies on CALL by using meta-analysis to explore its effectiveness. A total of 61 samples from 18 studies were examined. The results showed that CALL had a moderate average effect (g = .527). In addition, nine potential moderating variables (educational level, target language, language domain, learning outcome, instruction duration, chatbot interface, chatbot development, task dominance, and interaction way) were identified and discussed. The results of this study provided insights into the use and design of chatbots for language learning.

AimThis study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown.Subject and MethodsAfter dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results.ResultsFirst, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government’s anti-epidemic policy.ConclusionOur results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.