Search Results (156)

Gambling disorder is a public health concern, but research on the early detection of gambling-related harm is limited by the scarcity of ethically shareable online conversations. This study presents a framework for generating an entirely synthetic dataset for natural language processing research on gambling-related behavioral signals. Behavioral dimensions from the Online Gambling Disorder Questionnaire were used to construct 2100 risk-aligned user profiles and generate X/Twitter-style monologues and multi-user threads across gambling and non-gambling subtopics. The dataset contains 35,346 monologues and 1716 threads. The generated text was evaluated using automatic metrics, manual target-consistency assessment, and downstream classification under user-disjoint and parent-topic-disjoint protocols. Automatic and manual evaluations indicated acceptable linguistic quality and consistency with predefined behavioral targets. In the stricter parent-topic-disjoint setting, logistic regression achieved a macro-$F_1$ of 0.884 for binary gambling-content detection, whereas four-level risk prediction remained more difficult. These results show that the dataset contains learnable signals for computational detection of gambling-related content without exposing real-user data. However, the dataset was not externally validated against authentic social-media conversations and is not intended for clinical diagnosis. The resulting resource is designed to support reproducible research on gambling disorder and mental health while preserving user privacy. Full article

This article analyses how three leading female figures of the European far right—Marine Le Pen (France), Giorgia Meloni (Italy), and Alice Weidel (Germany)—construct populist discourse on X (formerly Twitter) during recent high-stakes election campaigns. The analysis is based on a corpus of 1053 posts published in the month preceding the 2022 French presidential elections, the 2022 Italian general elections, and the 2025 German federal elections. Methodologically, it combines content analysis (operationalised through a codebook developed in Atlas.ti v23), Critical Discourse Analysis, and comparative analysis to identify dominant issues, campaign strategies, and underlying discursive structures. Adopting a corpus-pragmatic perspective, the study undertakes a pragmatic examination of implicit meaning—presupposition, implication, and implicature—to show how meaning is conveyed in authentic, digitally mediated political data. The findings reveal a shared populist communicative matrix centred on polarisation, simplification, and emotional mobilisation, alongside important national variations. Le Pen and Meloni primarily construct an “internal traitor” among political elites, linking economic and social grievances to elite failure. In contrast, Weidel recentres migration and security, portraying migrants as a direct threat and mainstream parties as responsible for citizens’ insecurity. The article highlights the need for further research on gender and leadership in the contemporary European far right. Full article

Climate change is one of the most serious global challenges, and greenhouse gas emissions continue to rise despite mitigation efforts. Household consumption accounts for approximately 72% of global emissions, indicating the central role of individual behaviors. Traditional measurement instruments, such as surveys and interviews, are costly, time-consuming, and subject to response biases. User-generated textual content provides an alternative source of evidence, and natural language processing (NLP) enables its analysis at scale. Despite this potential, existing reviews have not systematically mapped its use for individual-level climate behaviors. The main goal of this research is to address this gap through a scoping review following PRISMA-ScR guidelines. Systematic searches were performed in Web of Science, Engineering Village, and Google Scholar, covering January 2015 to April 2026. A total of 2580 records were screened, and ten studies met the inclusion criteria. These studies analyzed Twitter/X, Sina Weibo, Reddit, and e-commerce reviews, covering behaviors from green transportation to waste management. The findings demonstrate that topic modeling and transformer-based models are the dominant techniques, typically combined with sentiment analysis. Recent studies extend beyond describing climate discourse toward explaining behavior. NLP-based text analysis constitutes a scalable complement to surveys and a foundation for targeted climate interventions. Full article

Background/Objectives: Endometriosis is a chronic gynecological condition affecting approximately 10% of women of reproductive age and is associated with chronic pain, fatigue, infertility, and reduced quality of life. Beyond its physical burden, delayed diagnosis, healthcare dismissal, and inadequate support may contribute substantially to psychological distress. In line with growing efforts to promote mental health and wellbeing within healthcare settings, this study explored women’s experiences of healthcare services and the psychological impact of living with endometriosis. Methods: A qualitative study was conducted using thematic analysis of 2500 publicly available Twitter/X posts shared by accounts self-presenting as women discussing experiences of endometriosis. Results: Three overarching themes were identified: (1) Institutional Invalidation of Women’s Endometriosis Experiences, (2) Living with the Multidimensional Burden of Endometriosis, and (3) Reimagining Endometriosis Care. Participants described chronic pain, reproductive uncertainty, and significant emotional distress that were frequently intensified by delayed diagnosis and experiences of not being believed or taken seriously within healthcare settings. Accounts suggested that suffering was shaped not only by disease symptoms but also by interactions with healthcare systems that undermined the legitimacy of women’s experiences. Conversely, validation, empathy, and collaborative communication were associated with improved wellbeing and engagement with care. Participants advocated for greater awareness of endometriosis, earlier diagnosis, and more integrated models of support. Conclusions: Endometriosis should be understood as a biopsychosocial condition whose impact extends beyond physical symptoms alone. The narratives analyzed highlight how psychological distress may be amplified by experiences of invalidation, delayed recognition, and fragmented care. Improving outcomes requires timely diagnosis, patient-centered healthcare interactions, and the integration of psychological support within multidisciplinary endometriosis services. Full article

Background/Objectives: Bipolar disorder (BD) is a chronic psychiatric condition associated with substantial emotional and psychosocial burden. Social media platforms provide an opportunity to examine how BD is discussed and emotionally framed outside clinical settings. Although Spanish-language public discourse on BD remains underexplored, this gap is particularly relevant given that linguistic and cultural factors may influence how mental health conditions are expressed, perceived, and communicated. Understanding these differences is essential for developing culturally sensitive public health strategies, improving mental health literacy, and addressing stigma in diverse populations. Methods: A retrospective observational study was conducted using Spanish-language tweets referring to BD posted between 2007 and 2023. After preprocessing, 170,411 unique tweets were analyzed. Latent Dirichlet Allocation (LDA) was applied to identify dominant thematic domains, and emotional tone was classified using a RoBERTa-based model adapted from Ekman’s basic emotions. Statistical analyses were performed to examine associations between topics, emotional categories, and the presence of clinical terminology. Results: Tweet activity related to BD remained low until 2019, followed by a marked increase peaking in 2020 and remaining elevated thereafter. Topic modeling identified seven main themes, predominantly centered on symptoms, illness-related life crises, social anxiety, help-seeking, and the impact of BD on romantic relationships, while diagnosis- and treatment-related topics were less frequent. Emotional analysis showed a clear predominance of sadness across all topics, accounting for approximately 75% of tweets. Anger, joy, and optimism were present at lower frequencies and varied across thematic domains. Clinical terminology was significantly more common in diagnosis- and treatment-related topics, whereas experiential and affective narratives dominated the remaining themes. Conclusions: Spanish-language discourse on BD on Twitter is largely characterized by emotional expression and lived experience rather than biomedical language. These findings highlight cultural relevance of social media as a complementary source for understanding public perceptions of BD and underscore the importance of culturally and linguistically specific analyses in digital mental health research. Full article

The creation and authentication of real users on social media requires the implementation of artificial intelligence-based technologies that can mitigate malicious behavior from automated accounts. This study presents a machine learning-based approach for detecting social bots on Twitter/X, based on the analysis of user profile features and behavioral attributes. Four classification models were evaluated: a neural network (NN), support vector machines (SVM), a random forest classifier (RF), and Extreme Gradient Boosting (XGBoost), using five-fold stratified cross-validation. To improve the performance and robustness of the classification, additional features and data balancing techniques were incorporated. The experimental results show that the neural network achieved the best overall performance, with an average accuracy of 95.6 ± 0.6%, followed by the random forest (95.0 ± 0.6%), the linear SVM (94.1 ± 1.5%) and XGBoost (94.0 ± 1.1%). These results demonstrate that the proposed methodology improves the automated detection of social bots while maintaining the interpretability of the models, which contributes to the development of more reliable and explainable security mechanisms for social media platforms. Full article

Coordinated and bot-like activity on social media is usually studied with supervised detectors that need rich account data such as profiles, timelines, and follower networks, which is increasingly hard to obtain. We ask what can be established about a single account’s reply ecosystem from its publicly visible posts and replies alone, with no profiles, timelines, or follower data. In a case study of the reply ecosystem of an official political party account (23,953 replies by 1985 accounts, December 2025 to January 2026), we compute profile-free behavioral features covering text duplication, character-level entropy, timing regularity, reply latency, and post coverage, complemented by a co-commenting network analysis, and group active accounts with unsupervised density-based clustering. The clustering, combined with two transparent labeling rules, separates three behavioral tiers: templated amplifiers defined by text reuse, persistent responders with human-like text but extreme volume and coverage, and an organic remainder. The two non-organic tiers comprise 5.4% of accounts, yet produce 53.5% of all comments, a composition that is stable under resampling and threshold sensitivity analysis, with a failure mode that is only conservative, since over-strict settings leave a tier unassigned rather than reshaping it. The platform’s own spam flags, never used as input, rise steadily from organic accounts to templated amplifiers, consistent with the behavioral grouping. Full article

Participatory citizenship plays a critical role in strengthening climate change resilience, particularly in the context of natural disasters such as floods and other hydrological extremes. Citizen-generated data shared through social media platforms offer valuable real-time insights that can complement traditional environmental monitoring systems. This study proposes a machine learning-based framework to analyze multilingual news data and global X (formerly known as Twitter) data that can complement street level sensor data for improved detection and understanding of extreme hydrological events: floods and landslides. The approach identifies and filters tweets related to hazards such as floods and contextualizes them with information extracted from news reports to enhance event characterization. In addition, sentiment and emotion analysis are applied to assess public reactions and perceived event intensity. By integrating physical event signals with societal responses, the method provides a broader perspective on disaster impacts and the effectiveness of emergency responses. The results highlight the potential of combining social media analytics and machine learning to support hydrological monitoring, enhance situational awareness, and contribute to more responsive disaster management strategies in the face of increasing climate-related risks. 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: Alzheimer’s disease (AD) and dementia are growing global health challenges, yet public understanding of these conditions remains poorly characterized. Methods: This study analyzed 17,578 English-language tweets from Twitter/X collected throughout 2024 to characterize public discourse using dual sentiment analysis and topic modeling. Sentiment was assessed using two lexicon-based tools, VADER (Valence Aware Dictionary and sEntiment Reasoner) and TextBlob, with inter-method agreement evaluated via Cohen’s Kappa and confusion matrix analysis. Latent Dirichlet Allocation (LDA) with coherence-based model selection (c_v) identified latent discussion topics. Results: All reported sentiment proportions are tool-specific estimates rather than absolute characterisations of public discourse and should be interpreted in light of the fair inter-method agreement observed between VADER and TextBlob. VADER classified 43.3% of tweets as positive, 32.2% as negative, and 24.5% as neutral (mean compound score = 0.065, SD = 0.499; 95% CI: 0.058 to 0.073; note that the large SD relative to the mean reflects the broad, near-zero-centred distribution of compound scores). TextBlob produced a partially divergent distribution (45.9% positive, 36.2% neutral, 17.8% negative) with fair inter-method agreement (κ = 0.297), indicating that exact sentiment proportions depend meaningfully on the choice of classifier. Approximately 14% of tweets contained political content related to the U.S. presidential election cycle, which may represent an important source of noise in health discourse. LDA identified six optimal topics (c_v = 0.407): research studies, blood-based diagnostics, comorbidities and personal loss, research advocacy, clinical developments, and risk factors and treatment. Sentiment differed significantly across topics (Kruskal–Wallis H = 235.92, p < 0.001), with research and advocacy topics showing the most positive VADER-classified sentiment (50.0%) and comorbidity discussions showing the highest negative proportion (41.4%). Positive tweets received significantly higher engagement than negative tweets (Mann–Whitney p < 0.001), though follower count was the strongest predictor of engagement (β = 0.198). Conclusions: These findings, interpreted as tool-conditional estimates, have implications for health communication strategies, public education campaigns, stigma reduction, and the responsible design of social media-based health surveillance systems. 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

Social media has emerged as a crucial environment for examining public sentiment during disasters, providing immediate insights into collective emotions and urgent expectations. This research examines the emotional reactions expressed on Turkish posts shared on the X platform (formerly Twitter) following the 6 February 2023 earthquake by employing an integrated method that combines topic modeling and topic-based sentiment analysis. Data were collected between 10 February 2023 and 28 February 2023. A large dataset consisting of 305,000 tweets was compiled, and 296,836 tweets remained for analysis after preprocessing and filtering procedures. Latent Dirichlet Allocation (LDA), enhanced with term frequency-inverse document frequency weighting and bigram extraction techniques, was applied to identify prominent themes, including rescue operations, appeals for assistance, communication about missing persons, and disaster management. The sentiment polarity within each topic was determined using a hybrid deep learning model incorporating Bidirectional Encoder Representations from Transformers (BERT) embeddings Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) layers, and FastText representations. This model reached a classification accuracy of 94%, with F1-scores of 0.91 and 0.95, recall values of 0.90 and 0.96, and precision values of 0.92 and 0.95, achieving higher performance than the evaluated baseline models. The findings indicate that supportive, solidarity-oriented, and resilience-related communication patterns were among the most frequently observed positive sentiment expressions, whereas negative sentiments appeared more frequently in discussions regarding delays in aid delivery and perceived shortcomings in institutional response. This study presents a scalable and flexible framework for analyzing sentiment in Turkish-language crisis communication, providing insights that may support disaster response monitoring and decision-making processes as well as the development of systems for tracking public reactions in real time. Full article

Background/Objectives: Artificial intelligence (AI) holds significant potential to enhance diagnostic support and access to dermatological care; however, its adoption depends on public trust and acceptance. This study aimed to assess knowledge, attitudes, and acceptance of dermatological AI among Saudi adults, and to identify factors associated with adoption, trust, and preferred system characteristics. Methods: A nationwide cross-sectional online survey was conducted among 668 Saudi adults (≥18 years) between 21 May and 5 June 2025, using convenience and snowball sampling via social media platforms (WhatsApp, Snapchat, Twitter/X, and Telegram). The questionnaire captured demographics, attitudes toward AI (20-item Likert scale), and perceived importance of six AI system features. Data were analyzed using descriptive statistics, one-way ANOVA, and binary logistic regression. The study was approved by the Institutional Review Board of King Faisal University (Approval No. KFU-REC-2025-MAY-ETHICS3443, approval date 19 May 2025). Results: The mean overall AI attitude orientation score was 74.48 ± 10.20 (Cronbach’s α = 0.868), reflecting moderately positive but conditional attitudes toward dermatological AI. Participants strongly preferred physician-supervised AI over fully autonomous systems, with medical oversight receiving the highest agreement (mean 4.27 ± 0.87). Privacy protection and diagnostic accuracy were rated as the most important system features. Age was significantly associated with the overall AI attitude orientation score (p = 0.009), with younger participants demonstrating more favorable orientations. Interest in technology showed the strongest association with both AI attitude orientation and perceived importance (p < 0.001). No demographic variable independently predicted high intention to use AI in multivariate analysis. Conclusions: Saudi adults generally exhibit favorable yet cautious attitudes toward dermatological AI. Implementation strategies should prioritize physician oversight, transparency, data privacy, and culturally responsive design to support responsible integration into clinical practice. Full article

This study investigates digital leadership as a networked social process by analyzing how influential actors operating across professional and institutional domains construct leadership discourse and draw on transformational leadership (TFL) principles within Twitter (X) networks, with particular attention to the skill-transfer gaps that persist between formal academic preparation and workforce demands. Social Network Analysis (SNA) using the NodeXL program was used to examine the relational structure of that discourse across a dataset of 1186 Twitter accounts and 1362 relational ties. The analysis identified 27 prominent actors operating within a distinct community cluster whose discourse spanned politics, health, technology, media, and education, with thematically diverse but uneven engagement with leadership topics. Combining semantic cluster analyses, inductive thematic mapping, and a supplementary exploratory factor analysis (EFA), the study finds that the four TFL principles (individualized consideration, intellectual stimulation, inspirational motivation, and idealized influence) are unevenly represented in this discourse. The EFA condensed the co-occurrence structure into three platform-shaped factors, with the strongest support for individualized consideration and no coherent factor for idealized influence, indicating partial rather than comprehensive alignment with the four-dimensional TFL model. The findings position digital leadership as a relational and iterative social process, sustained through repeated interactions, endorsements, and positional recognition within platform-based publics that extend across academic, industry, and socio-political boundaries. The study highlights social media as a networked yet uneven environment for leadership development and the broader social negotiation of skill-transfer challenges across digital professional contexts. Full article

Social media studies often rely on one platform while drawing conclusions about online publics more generally. This study tests that inferential move through an event-centered comparison of El Niño discourse across X/Twitter, YouTube, Facebook, Reddit, TikTok, and LinkedIn. The observation window ran from 9 May through 17 May 2026, several days before and after the May 14 El Niño Watch issued by the National Oceanic and Atmospheric Administration (NOAA), which reported an 82 percent probability of El Niño emerging during May to July 2026 and a 96 percent probability of continuation through the 2026 to 2027 Northern Hemisphere winter. The corpus contains 8145 items classified as highly or moderately related to El Niño after platform-specific collection and common annotation. X/Twitter supplies 7075 items, YouTube 864, Facebook 66, Reddit 59, TikTok 50, and LinkedIn 31. Texts were annotated with a shared structured schema covering relevance, sentiment, emotion, topic, stance, likely misinformation, personal experience, humor, calls to action, language, engagement, and length. The results show that platform choice changes the empirical object. X/Twitter appears multilingual, fast-moving, and weather-heavy. YouTube is more negative, humorous, and personally experiential. Facebook is long-form and media/news oriented, with the highest model-flagged likely misinformation rate. Reddit is concentrated around weather concern. TikTok is short, playful, and personal. LinkedIn is small, professional, and mostly informational. These differences caution against generalizing from one platform to social media as a whole unless a study explicitly defines its scope, accounts for platform and genre differences, and recognizes that visible discourse may include organizational, algorithmically amplified, automated, or otherwise inauthentic activity alongside genuine human expression. Full article