Search Results (365)

Social media is a vital platform for policy communication, yet research rarely integrates content and structural features to evaluate communication effects multidimensionally. This study proposes a convergent framework combining text mining and social network analysis to assess policy communication effects and underlying mechanisms. Using China’s upgraded Mass Entrepreneurship and Innovation policy on Sina Weibo as a case, we analyze communication breadth, depth, audience sentiment, thematic focus, network topology, key nodes, and community characteristics. The results reveal that (1) communication breadth is dominated by official communicators, while audiences drive interactive depth, reflecting a “centralized broadcasting, decentralized engagement” model; (2) influential users express more positive attitudes than ordinary audiences; (3) discussions diversify from core innovation themes to micro-level concerns like regional development and talent policies; (4) the network shows loose global structure but strong local clustering, with bridging nodes posting less polarized, broader content. Theoretically, this study offers behavioral-level observations that align with key corollaries of the Spiral of Silence Theory—the tendency for individuals with deviating views to shift toward lower-visibility participation. These pattern-level findings offer a complementary empirical perspective on opinion expression in digital policy contexts. Practically, the findings offer preliminary insights that may inform adaptive, decentralized strategies for enhancing policy diffusion in similar social media contexts. Full article

Socio-emotional skills are essential for teachers’ wellbeing, classroom functioning, and promotion of students’ social–emotional development. Digital game-based learning (DGBL) represents a promising approach to promote these competencies through engaging and experiential learning processes. Empathy Quest is a DGBL experience—aimed at fostering emotional intelligence—designed by a multidisciplinary group involving game design, educational psychology, media communication, and teaching expertise. An exploratory evaluation involved 83 teachers in training (74 female; mean age = 46.11 years). Our research questions inquired about teachers’ perceptions of Empathy Quest in terms of game enjoyment, user experience, motivation toward DGBL, and interest in emotional intelligence. After completing a gameplay session, participants filled in the individual interest questionnaire, situational motivation scale, intrinsic motivation inventory, user experience questionnaire, and ad hoc items. Descriptive analyses indicated high levels of motivation, enjoyment, and interest in emotional intelligence. Participants also reported positive user experiences in terms of attractiveness, novelty, and stimulation, alongside good ratings for dependability, perspicuity, and efficiency. Although the post-test-only design does not allow for causal conclusions, the findings suggest that Empathy Quest is a promising tool for engaging future teachers with emotional intelligence and may support the integration of socio-emotional learning into teacher education and professional development contexts. Full article

Human-like virtual influencers (VIs) have become an increasingly important component of social media marketing. Their human-like appearance can simultaneously attract users and, meanwhile, evoke discomfort associated with the uncanny valley. This study utilized quantitative research via partial least squares structural equation modeling. A total of 845 Instagram users contributed to the dataset. The findings demonstrated that informative content did not significantly influence Instagram users’ engagement intentions, whereas entertaining content exerted a favorable influence. Simultaneously perceived innovativeness was the strongest antecedent, while perceived personalization was also significant. In addition, both cognitive and affective empathy significantly strengthen parasocial relationships, which subsequently increase users’ engagement intentions toward human-like VIs. This study contributes in three ways. First, it identifies the relative importance of content, social relationships, and personal gratifications in explaining engagement intentions with human-like VIs. Second, it extends research by distinguishing the complementary roles of cognitive and affective empathy in fostering parasocial relationships with human-like VIs. Third, the findings suggest that cognitive and affective empathy may help explain why users form meaningful social relationships with highly human-like VIs despite concerns associated with perceived artificiality, thereby offering a more nuanced understanding of engagement intentions in the context of virtual influencer marketing. Full article

Environmental degradation is no longer a distant threat; it now permeates social media feeds, casual conversations, and daily digital content. However, whether incidental exposure to environmental news leads to meaningful behavioral change remains poorly understood. This study examines how incidental environmental news exposure (IENE) relates to pro-environmental behavior (PEB) among Chinese social media users, with particular attention to the sequential chain-mediating roles of environmental belief (EB) and perceived environmental risk (PER). A theoretical model was developed and tested using survey data from 468 respondents collected in November and December 2024, and analyzed using structural equation modeling (SEM). The results indicate that IENE is significantly and positively associated with EB, PER, and PEB. Notably, EB was not directly associated with PEB; its association operated almost entirely through PER, which accounted for 87.6% of the total EB–PEB association. PER emerged as the principal psychological pathway linking passive news exposure to behavioral engagement, and the structural model explained 42.1% of the variance in PEB. Because the design is cross-sectional, the results establish covariance patterns consistent with the proposed sequence rather than temporal or causal order. These findings indicate that cognitive risk appraisal, rather than environmental belief alone, constitutes the proximal mediating mechanism within the modelled pathway to pro-environmental action. Policymakers and media practitioners should accordingly design communication strategies that extend beyond general awareness-raising to link environmental threats explicitly to perceived personal and societal risk. Full article

Online toxicity has become a defining feature of contemporary digital discourse, permeating all forms of online discussion, from news websites and discussion forums to social media platforms. While prior research has examined toxic behaviour across these domains, less attention has been paid to the interplay among broader socio-political polarization, platform architectures, and psychological mechanisms that jointly sustain this discursive paradigm. This literature review proposes a multi-level integrative framework to conceptualize the toxicity lifecycle. At the macro level, affective polarization fosters societal antagonism, reinforcing entrenched in-group and out-group dynamics. However, the intensity and expression of these societal cleavages are shaped by distinct media–political system configurations, including varying degrees of political parallelism, and journalistic professionalization. At the meso level, platform affordances such as algorithmic amplification and engagement-based metrics promote emotionally charged, conflict-driven content. At the micro level, disinhibition lowers normative constraints on expression, often leading to verbal aggression that undermines the principles of healthy public deliberation. Addressing these challenges requires a comprehensive response that brings together users, platforms, and policymakers to safeguard the normative democratic values of constructive public deliberation. Full article

Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory to systematically elucidate the thematic configurations, framework dynamics, and ecological activation mechanisms underlying the international image of Chinese EVs. By integrating unsupervised BERTopic modeling, Large Language Model (LLM) semantic mapping, the Entropy Weight Method (EWM), and Social Network Analysis (SNA), this inquiry operationalizes a comprehensive computational communication framework to mine large-scale behavioral and textual data from YouTube. The empirical findings unveil that: (1) international audience perceptions have broken through the traditional “low-cost manufacturing” stereotype, spontaneously giving rise to a multidimensional, composite cognitive schema centered on smart ecosystems and design experiences; (2) driven by the interplay of rational and irrational user feedback loops, the ecological activation efficiencies across diverse discursive dimensions exhibit pronounced nonlinear variances, characterized by a “strong activation of intelligent ecosystems versus a long-tail stagnation of cost-effectiveness salience”; and (3) positive technological frameworks and negative geopolitical or regulatory risks engage in fierce, adversarial contestation and structural hybridization within a highly volatile network topology, culminating in a unique “dual-core” configuration. Theoretically, this study enriches the scholarly understanding of country-of-origin and corporate brand images through a complex systems lens; methodologically and practically, it offers a high-fidelity, actionable quantitative paradigm for global brand empowerment and targeted cross-border public opinion governance. Full article

Many politicians heavily leverage social media platforms to further their interests and professional goals. TikTok, the widely popular social network established in 2016, has amassed a vast user following, especially among the younger demographic known as Generation Z. This study conceptualizes it as part of a platformized political communication environment that enables direct-to-audience communication beyond traditional journalistic gatekeeping. The current study aims to analyze high-visibility TikTok content produced by active Israeli politicians by scrutinizing the best-performing videos created by politicians representing diverse political ideologies. High-visibility content refers to videos that generated relatively high engagement, exposure, and platform presence, and therefore had greater potential to influence or impact audiences compared with less visible posts. The goal is to characterize and understand the content patterns and visibility dynamics of these successful TikTok videos posted by politicians. The findings suggest that the popularity of TikTok videos posted by politicians is associated with gender, age, and political affiliation. Men, older individuals, and those with right-wing affiliations tend to create content that garners the highest levels of popularity. Moreover, a considerable proportion of the sampled high-visibility videos do not engage directly with political subject matters. This suggests that, among best-performing political TikTok posts, visibility and engagement may sometimes outweigh explicit political or ideological discourse. This implies that politicians feel compelled to participate on the platform, even when their most visible content does not specifically revolve around promoting political messages. Consequently, the findings lend cautious support to the view that platform logic shapes political visibility and communication practices, even in politicians’ use of TikTok. In doing so, this study contributes to understanding how commercial social media platforms such as TikTok shape political visibility, engagement, and direct-to-audience communication. Full article

Prolonged short-video engagement in the generative-AI era may be shaped by interface cues that encourage or interrupt repeated continuation decisions in algorithmic feeds. This study examines whether AI-generated content disclosure functions as interface-level digital friction for prolonged short-video engagement among late-adolescent and emerging-adult TikTok users. Prolonged watching intention is treated as a cognitive-behavioral proximal tendency relevant to problematic social media use (PSMU), rather than as a clinical diagnosis or an emotional-disturbance outcome. Drawing on the heuristic-systematic model, we tested a dual-pathway risk-trust model in which disclosure directly affects prolonged watching intention, while perceived risk and content trust operate as mediators and AI literacy operates as a person-level boundary condition. An online between-subjects experiment was conducted with 720 valid participants aged 18–24. Disclosure had a positive direct effect on prolonged watching intention, suggesting that AI labels can initially work as salient curiosity and novelty cues. At the same time, disclosure increased perceived risk and reduced content trust, generating negative indirect pathways that constrained prolonged watching intention. AI literacy strengthened both appraisal pathways. The findings reposition AI disclosure from a mere transparency notice to a behavioral cue that can simultaneously attract attention and activate protective appraisal. They contribute to developmental and media-psychological research on prolonged engagement and PSMU-relevant mechanisms without overstating clinical implications. Full article

The increasing influence of social media has amplified the risks associated with automated accounts that spread misinformation, manipulate public opinion and carry out malicious activities. To address this challenge, this study presents an explainable, feature-based approach for detecting social bots on $\mathbb{X}$ (formerly Twitter) using user-profile information derived from account metadata and content characteristics. We consolidate and extend existing research by bringing together one of the most comprehensive feature sets explored to date, combining raw attributes, features proposed in the literature, and newly introduced credibility and engagement indicators, together with a previously unexploited profile-personalisation signal. Through a feature engineering and selection process that integrates Mutual Information, Random Forest Importance, and SHAP values, we evaluate the contribution of each feature category and assess its generalisation capacity across three benchmark datasets. Our experiments demonstrate that classical machine learning models enriched with the selected features can match or surpass several state-of-the-art approaches while preserving interpretability. Furthermore, we propose and validate, on the more recent and challenging TwiBot-22 dataset, three categories of features (universal, common, and dataset-specific) that provide a transparent and adaptable basis for generalisable bot detection. Full article

Background/Objectives: Large language models have created new pathways for patients to access health information, yet little is known about how the general population uses these conversational artificial intelligence (AI) tools for oral health concerns. This study investigated patterns of AI use as a source of oral health information, the nature of data shared with these systems, users’ perceptions, and the impact on dental care-seeking behavior. Methods: A cross-sectional study was conducted among adults from Bihor County, Romania, using a structured 16-item online questionnaire distributed via social media, with eligibility restricted to individuals who had previously used conversational AI for oral health information. The final sample comprised 393 valid responses from this self-selected group of users. Fisher’s exact test and Z-tests with Bonferroni correction were applied (α = 0.05). Results: Most participants were female (68.2%), university-educated (52.9%), and lived in an urban setting (88.3%). Significant differences in patterns of AI use for oral health information were identified according to age, sex, and living environment (p < 0.001). Younger participants used AI more frequently, while older individuals perceived the information as less clear. The vast majority used AI for informational or preliminary guidance purposes, with very few treating it as a substitute for professional opinion. A relevant subset shared visual data (intraoral photographs or radiographs) with AI systems, raising data privacy concerns. Rural participants more frequently delayed dental visits and less often discussed AI-derived information with their dentist compared to urban participants. Conclusions: When used by the public as a source of oral health information, AI is increasingly adopted, with adoption shaped by age, sex, and socioeconomic context. These findings concern only this informational use and do not extend to other applications of AI in dentistry, such as diagnostic support, image analysis, or clinical decision-making. Dental professionals should proactively engage patients about their use of AI for oral health information to ensure that digitally obtained content is appropriately contextualized. Full article

Consumer resistance remains a major barrier to the effectiveness of AI-enabled social media marketing despite advances in content personalisation, influencer marketing, and intelligent recommendation systems. This study investigates how content personalisation, influencer trust, and platform interactivity influence consumer resistance, user engagement, and purchase intention by proposing a behaviourally informed Deep Reinforcement Learning (DRL) framework that integrates empirical behavioural modelling with adaptive optimisation. Survey data were collected from 619 higher education students in Saudi Arabia and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), Multi-Group Analysis (MGA), and a Deep Q-Network (DQN)-based optimisation framework. The results show that content personalisation, influencer trust, and platform interactivity significantly increase user engagement while reducing consumer resistance. User engagement positively influences purchase intention, whereas consumer resistance negatively affects purchasing behaviour. Multi-Group Analysis revealed that Generation Z responded more strongly to personalisation and platform interactivity, whereas Generation Y showed greater responsiveness to influencer trust. The proposed behaviourally informed DQN framework incorporated latent behavioural constructs and statistically validated structural relationships into the reinforcement learning environment to generate adaptive marketing policies. Compared with conventional static and rule-based strategies, the proposed framework achieved approximately 36% higher optimisation performance across repeated behavioural simulations. The study contributes by positioning consumer resistance as the central behavioural construct, introducing an integrated behavioural–computational framework that embeds empirical behavioural relationships into the DRL state representation, reward mechanism, and policy-learning process, and providing practical guidance for developing transparent, trust-sensitive, and adaptive social media marketing strategies that enhance user engagement, reduce consumer resistance, and improve purchase intention in digital commerce environments. Full article

Social media platforms play a central role in shaping whose voices gain visibility during moments of crisis. This study examines how platform-mediated dynamics influence the distribution of voice in online discourse, focusing on racialized and migrant communities in Canada during the COVID-19 pandemic. Using a large-scale computational analysis of X (formerly Twitter) data, we analyze participation patterns, dominant narratives, and how attention is distributed across actors. We identify four established forms of Othering—hostile, cultural, sympathetic, and silencing—and introduce a fifth: algorithmic Othering. We define algorithmic Othering as the platform-mediated structuring of visibility through which institutional and elite actors disproportionately shape discourse, while marginalized users remain comparatively under-amplified. Our findings show that even when racialized and migrant users actively participate in online discussions, their visibility is systematically constrained by engagement-driven amplification systems. As a result, marginalized communities are more often spoken about than heard directly. These findings suggest that social media platforms do not simply reflect existing inequalities but actively organize them through the distribution of attention and visibility. By identifying a structural mechanism through which voice is unevenly amplified, this study contributes to broader understandings of inequality, representation, and participation in digital environments. Full article

Background: Research on social media use, fear of missing out (FoMO), sleep disturbance, and health complaints has been dominated by survey-based studies, particularly among adolescents and university students. Less is known about how users spontaneously describe these experiences in naturalistic online settings. This exploratory pilot study examined how publicly available Reddit discussions narrate the relationship between social media use, FoMO-related concern, sleep disruption, and self-reported physical complaints. Methods: A total of 30 publicly available English-language Reddit posts and comments were purposively sampled from 11 threads dated August 2022 to March 2026. The study used exploratory qualitative content analysis supported by reflexive thematic interpretation. Structured indicators were used to describe whether each unit contained explicit FoMO language, implicit FoMO-related concern, sleep disturbance, physical health complaints, and nighttime use or sleep loss. Thematic coding was used to identify dominant discourse patterns. All counts and percentages are reported only to characterize the analytic corpus and should not be interpreted as prevalence estimates. Results: Within the corpus, sleep disturbance appeared in 16 of 30 units, nighttime use or sleep loss in 15, physical health complaints in 11, explicit FoMO language in 6, and implicit FoMO-related concern in 3. The dominant themes were delayed sleep and bedtime displacement, somatic and cognitive overload, self-regulation and recovery, and compulsive monitoring and comparison. Sleep-related complaints were usually described alongside bedtime scrolling, delayed disengagement, or lost sleep opportunity. FoMO-related concern was less often expressed through formal terminology and more often appeared through everyday descriptions of checking, comparison, and difficulty disconnecting. Conclusions: This small exploratory corpus suggests that Reddit users often describe social media-related strain through practical behavioral language, such as late-night scrolling, inability to stop, lost sleep, next-day fatigue, headache, and brain fog. The findings are descriptive, discourse-focused, and hypothesis-generating. They do not estimate population prevalence or establish causal health effects. To improve transparency, the revised study provides a de-identified analytic matrix of all 30 coded Reddit units and reports a strengthened coding procedure with independent second-coder checking. Naturally occurring online discourse may complement survey-based digital-health research by showing how users themselves frame the embodied experience of digital over-engagement. Full article

The increasing availability of large language models (LLMs) is transforming how users create and share content on social media platforms. Beyond enabling text generation, LLMs introduce a new paradigm in which content is deliberately optimized for engagement through algorithmically suggested phrasing, structure, and tone. This paper investigates the emerging shift from authentic self-expression toward engagement-driven optimization in LLM-assisted social media use. It examines whether and how LLM-generated or LLM-assisted posts systematically outperform human-authored content in engagement metrics and at what cost to informational quality, diversity, and authenticity. Using a mixed-methods approach, controlled experiments with human participants are combined with large-scale analysis of social media posts to compare organic and LLM-optimized content. Differences in engagement outcomes (e.g., likes, shares, comments), linguistic features, and perceived credibility and informativeness are evaluated. The findings suggest that while LLM-assisted content consistently increases short-term engagement, it tends to reduce informational depth and perceived authenticity while exhibiting changes in stylistic characteristics associated with engagement-oriented optimization. This creates a potential feedback loop in which users increasingly rely on optimization strategies that privilege attention over substance. The findings suggest that widespread adoption of LLM-driven optimization could contribute to changes in the dynamics of the social media attention economy. Future research is needed to determine whether these effects emerge at scale and persist over longer periods of platform use. Implications are discussed for platform design, content moderation, and the future of human–AI co-creation in digital communication. Full article

Social media platforms and user-generated content (UGC) have become central to how travelers discover and evaluate cultural destinations, yet lesser-known second-tier heritage sites remain substantially underrepresented in digital tourism research. This study investigates how Chinese tourists perceive and engage with the intangible cultural heritage (ICH) of Lamphun, Thailand, through UGC collected from three major Chinese social media platforms (WeChat, Douyin, and Rednote) spanning the period from 2019 to 2023. A total of 642 relevant posts were analyzed using a mixed-methods analytical framework comprising SnowNLP-based Chinese-language sentiment analysis, rule-based tourism intention classification, and TF-IDF-driven K-means thematic clustering. Results indicate an overall predominance of positive sentiment, with sentiment score emerging as the strongest predictor of tourism intention. Thematic clustering revealed three distinct experiential dimensions, with culinary heritage and contemporary local lifestyle and cafe exploration generating the highest sentiment distribution and within-cluster tourism intention rate. These findings demonstrate the analytical value of integrated UGC data mining for underrepresented ICH destinations and offer empirical insights to support data-driven destination marketing strategies and destination management organization (DMO) decision-making for the promotion of secondary cultural heritage destinations. Full article