Search Results (2,301)

Social media serves as a vital channel for emotional expression, yet mandatory IP location disclosure raises concerns about how reducing anonymity affects users’ shared emotions, particularly in privacy-sensitive contexts such as mental health discussions. In 2022, all Chinese social media platforms implemented this disclosure feature. This study examines the emotional and behavioral consequences of Sina Weibo’s mandatory IP location disclosure policy, which took effect on 28 April 2022. We collected 193,761 Weibo posts published under the topic of depression from 1 March to 30 June 2022, and applied sentiment analysis combined with regression discontinuity in time (RDiT) to estimate causal effects around the policy threshold. Results indicate that the policy significantly intensified negative emotional expression: the estimated discontinuity is −1.3506 (p < 0.01), meaning posts became more negative immediately after implementation. In contrast, the effect on positive sentiment was comparatively weak and mostly statistically insignificant. Behavioral changes were also observed: both average daily posting volume and average text length are declined. These findings demonstrate that mandatory disclosure can suppress self-disclosure and amplify negative emotional tone in privacy-sensitive settings, offering practical guidance for users, platform designers, and policymakers on implementing transparency features responsibly. Full article

This qualitative single-case study examines how spirituality promotes mental health within a stigmatized occupation by analyzing an in-depth interview with “Perla,” a 62-year-old Mexican woman with decades of experience in sex work. Guided by Virginia Satir’s Transformational Systemic Therapy (STST), specifically the Self-Mandala and Iceberg Metaphor, we conceptualize spirituality as a universal human dimension of meaning, moral orientation, and relational connection that may be expressed within or beyond formal religion. Narrative thematic analysis identifies processes through which Perla cultivates congruence (alignment of inner experience and outward conduct), safeguards dignity, and sustains hope amid systemic constraints. Her Catholic practices (prayer, ritual boundaries regarding Eucharist) coexist with a broader spiritual agency that supports self-worth, emotional regulation, boundary-setting, and coherent identity, factors associated with mental well-being. Interdisciplinary implications bridge marriage and family therapy, psychology, pastoral care, and cultural studies. Clinically, we translate Satir’s constructs (yearnings, perceptions, expectations, coping stances) into practical assessment and intervention steps that can be applied in secular settings without religious presuppositions. Analytic rigor was supported through reflective memoing, a structured three-level coding process, constant comparison, and verification by a second coder. The case challenges pathologizing frames of sex workers by demonstrating how spirituality can function as a protective, growth-oriented resource that fosters agency and moral coherence. Full article

Facial Expression Recognition (FER) is a critical component of affective computing, with deep learning models dominating performance metrics. In contrast, geometric approaches based on the Facial Action Coding System (FACS) offer explainability through using triangles aligned to facial landmarks. The notable points of these triangles capture the deformation of muscles. However, restricting the feature extraction to notable points may be suboptimal. This paper introduces a novel method for optimizing the extraction of features by searching for optimal inner points in 22 facial triangles applying three metaheuristics: Differential Evolution (DE), Particle Swarm Optimization (PSO), and Convex Partition (CP). This results in a set of 59 geometric-based descriptors that capture muscle deformation more accurately. The proposed method was evaluated using five machine learning classifiers on two benchmark databases: the Karolinska Directed Emotional Faces (KDEF) and the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate significant performance improvements. The combination of DE with a Multi-Layer Perceptron (MLP) achieved an accuracy of $0.91$ on the KDEF database, while Support Vector Machine (SVM) optimized via CP attained an accuracy of $0.81$ on the JAFFE database. Statistical analysis confirms that optimized descriptors yield higher accuracy than previous geometric methods. Full article

The Coronavirus Disease 2019 (COVID-19) pandemic and its prolonged effects have been widely discussed on social media, and these discussions have been analyzed in various studies. A long-term analysis of Twitter (now “X”) posts regarding COVID-19 vaccination is essential for informing policy and improving public health communication strategies. In addition, to prevent the spread of infectious diseases, it is crucial to rapidly promote vaccination while mitigating the impact of negative sentiment toward vaccination on social media platforms. Therefore, identifying the key factors behind negative discussions is important for guiding policy decisions and shaping responses. In this study, we collected Japanese tweets (posts) containing the words “Corona” and “vaccine” that were posted from February 2021 to December 2022. The results indicate that negative sentiment was primarily driven by concerns about adverse reactions and general fear and anxiety, which were particularly prominent before vaccination for the general public began, as well as mentions of pain during and after vaccination. While concerns about adverse reactions persisted throughout the analysis period, their prominence decreased over time as positive reactions became more frequent. Our findings provide insights into the characteristics and key factors behind negative discussions on COVID-19 vaccination in the Japanese context and may help improve public health communication strategies. Full article

Emotion regulation refers to the processes through which people modulate their emotional experiences and expressions, and difficulties in these processes underpin many forms of psychopathology. According to the process model, emotion regulation encompasses five classes of strategies, commonly grouped into antecedent-focused strategies (e.g., cognitive reappraisal) and response-focused strategies (e.g., expressive suppression). These strategies involve both explicit and implicit processes, which can be objectively assessed using physiological indices. The present study examined the effects of startle habituation and vagally mediated heart rate variability (vmHRV) on the use of cognitive appraisal and suppression. Forty-nine college-aged participants were recruited, and their resting heart rate variability (HRV) and response habituation to an auditory startle-eliciting stimulus were measured. Emotion regulation strategies were assessed by a self-report questionnaire. Multiple regressions were used to analyze the effects of startle habituation, vmHRV, and their interaction on emotion regulation strategies. Results indicated that, although suppression was not associated with any physiological indices in the regression models, cognitive reappraisal was predicted by both vmHRV and startle habituation. Notably, vmHRV and startle habituation interacted such that the positive association between vmHRV and cognitive reappraisal emerged only among individuals who exhibited slow startle habituation. These findings have practical implications for the prevention and treatment of psychopathology, as well as for promoting more adaptive emotion regulation in daily life. Full article

Gaze behavior is a critical component of social interaction, reflecting emotional recognition and social regulation. While previous research has emphasized either situational influences (e.g., deception) or stable individual differences (e.g., attachment avoidance) on gaze patterns, studies exploring how these factors interact to shape gaze behavior in interpersonal contexts remain scarce. In this vein, the aim of the present study was to experimentally determine whether the gaze direction of individuals differs, with respect to their avoidant orientation, under changing situational conditions, including truthful and deceptive communication towards a counterpart. Using a within-person experimental design and the eye-tracking methodology, 31 participants took part in both rehearsed and spontaneous truth-telling and lie-telling tasks. Consistent with expectations, higher attachment avoidance was associated with significantly fewer fixations on emotionally expressive facial regions (e.g., mouth, jaw), and non-significant but visually consistent increases in fixations on the upper face (e.g., eyes) and background. These findings indicate that stable dispositional tendencies, rather than situational demands such as deception, predominantly shape gaze allocation during interpersonal interactions. They further provide a foundation for future investigations into the dynamic interplay between personality and situational context in interactive communicative settings. Full article

Grounded in stress-reactivity accounts and the Prosocial Classroom model, this study examines how preschool teachers’ responses to children’s negative emotions are associated with teacher job stress and age in Greek early childhood education settings. These frameworks suggest that elevated job stress may erode teachers’ regulatory resources and responsiveness, increasing non-supportive reactions and reducing supportive emotion coaching during emotionally charged classroom interactions. A sample of 101 full-time preschool educators (M age = 42.3 years; 97% female) completed two instruments: the Coping with Children’s Negative Emotions Scale (CCNES) and the Child Care Workers’ Job Stress Inventory (CCW-JSI). Age-controlled partial correlations indicated that higher job stress was associated with more frequent use of non-supportive reactions, including punitive and minimizing responses, and less frequent use of supportive strategies, such as emotion-focused, problem-focused, and expressive encouragement responses. Older teachers tended to report higher supportive response scores, particularly for problem-focused reactions and expressive encouragement. These findings highlight the importance of teacher well-being for the emotional climate of preschool classrooms and suggest that job stress may undermine educators’ capacity to consistently engage in supportive emotion socialization. The study contributes to the education literature by linking teacher stress and emotion socialization practices in a policy context where early childhood education is expanding but remains under-resourced. Implications for teacher education, professional development, and system-level initiatives to support educators’ social-emotional competence are discussed. Full article

Little attention has been paid to whether an instructor’s hand-pointing gestures or use of a mouse-guided arrow can mitigate the attentional loss caused by an instructor’s happy facial expressions or can enhance the social benefits of these expressions in instructional videos. The goal of the present study is to determine whether social cues in an instructional video affect learning processes and outcomes. The participants were 57 female students from a university. We employed a 2 × 2 mixed experimental design. The instructor’s facial expression was a within-subject variable, while the type of pointing cue was a between-subject variable. Students who had the smiling instructor rather than the bored instructor gave higher ratings of the perceived positive emotion of the instructor, felt more positive emotion, and had more motivation to learn. Eye-tracking technology showed that students who learned with the smiling instructor spent more time looking at the content on the slides than those who learned with a bored instructor. Students who learned with the smiling instructor scored higher on a learning outcome post-test than those who learned with the bored instructor. Among female Chinese students, this pattern is consistent with the five steps posited by the positivity principle, which concludes that people learn better from instructors who exhibit positive social cues. Pointing with a human hand was not superior to pointing with an arrow, suggesting that in this case hand-pointing was not a strong social cue and did not moderate the effects of facial expression. Given the exclusively female sample, future research should examine whether these effects generalize across genders. Full article

This paper presents a hybrid architecture for automatic sentiment analysis of Kazakh-language political discourse. The Kazakh language is characterized by an agglutinative structure, a complex word-formation system, and the limited availability of digital resources, which significantly complicates the application of standard neural network approaches. To account for these characteristics, a multi-level system was developed that combines morphological and syntactic analysis rules, ontological relationships between political concepts, and multilingual representations of the XLM-R model, used in zero-shot mode. A corpus of 12,000 sentences was annotated for sentiment polarity and used for training and evaluation, while Universal Dependencies annotation was applied for morpho-syntactic analysis. Rule-based components compensate for errors related to affixation variability, modality, and directive constructions. An ontology comprising over 300 domain concepts ensures the correct interpretation of set expressions, terms, and political actors. Experimental results show that the proposed hybrid architecture outperforms both neural network baseline models and purely rule-based solutions, achieving Macro-F1 = 0.81. Ablation revealed that the contribution of modules is unevenly distributed: the ontology provides +0.04 to Macro-F1, the UD syntax +0.08, and the rule-based module +0.11. The developed system forms an interpretable and robust assessment of tonality, emotions, and discursive strategies in political discourse, and also creates a basis for further expansion of the corpus, additional training of models, and the application of hybrid methods to other tasks of analyzing low-resource languages. Full article

Rapid climate change and its anticipated impacts trigger significant worry and distress among vulnerable groups, including young adults. Little is known about how Dutch young adults experience and cope with climate change within their specific social and environmental context. This study examines Dutch young people’s emotional responses to climate change, their perceived emotional and psychological needs arising from these experiences, and the barriers they encounter in engaging in pro-environmental behaviour, with the aim of informing public health strategies to better support and empower this vulnerable group. Data were drawn from a large online survey among a representative sample of 1006 Dutch young adults (16–35 years; 51% women). The questionnaire included fixed-answer sections assessing emotional responses to climate change, as well as two open-ended questions exploring participants’ perceptions of their emotional and psychological needs related to climate change and the barriers they perceive to pro-environmental behaviour. Descriptive statistics were used for the fixed-response items, and thematic analysis was applied to the open-ended responses. Many Dutch young adults reported worry and sadness about climate change and its impacts, with approximately one third experiencing feelings of powerlessness. A large percentage of respondents attributed responsibility to large companies, and nearly half indicated that they still had hope for the future. One third (31%) felt that nothing could make them feel better about climate change, and another third (36%) reported to experience no climate-related emotions. Key emotional needs included more action at personal, community, and governmental levels, and more motivating positive news. Almost half (46%) of young adults said they already lived sustainably, while perceived barriers to pro-environmental behaviour were mainly financial (21%), knowledge-related (8%), and time-related (7%). This exploratory study highlights key practical and emotional barriers to pro-environmental behaviour reported by Dutch young adults 16–35, who expressed diverse emotional needs while coping with climate change. The findings underscore the need for a multi-level public health response to climate-related emotions, that simultaneously addresses emotional needs, structural barriers, and opportunities for meaningful engagement. Lowering barriers to pro-environmental behaviour and fostering supportive environments that enable sustainable action among young adults may enhance wellbeing and strengthen their sense of agency. Public health supports this by reducing barriers to pro-environmental behaviour in young adults, through targeted support, clear information, and enabling social and structural conditions that promote wellbeing and sustained engagement. Full article

Text-to-Speech (TTS) methods typically employ a sequential approach with an Acoustic Model (AM) and a vocoder, using a Mel spectrogram as an intermediate representation. However, in home environments, TTS systems often struggle with issues such as inadequate robustness against environmental noise and limited adaptability to diverse speaker characteristics. The quality of the Mel spectrogram directly affects the performance of TTS systems, yet existing methods overlook the potential of enhancing Mel spectrogram quality through more comprehensive speech features. To address the complex acoustic characteristics of home environments, this paper introduces AirSpeech, a post-processing model for Mel-spectrogram synthesis. We adopt a Generative Adversarial Network (GAN) to improve the accuracy of Mel spectrogram prediction and enhance the expressiveness of synthesized speech. By incorporating additional conditioning extracted from synthesized audio using specified speech feature parameters, our method significantly enhances the expressiveness and emotional adaptability of synthesized speech in home environments. Furthermore, we propose a global normalization strategy to stabilize the GAN training process. Through extensive evaluations, we demonstrate that the proposed method significantly improves the signal quality and naturalness of synthesized speech, providing a more user-friendly speech interaction solution for smart home applications. Full article

Emotion–cause pair extraction is a crucial task in natural language processing that identifies emotional expressions and their corresponding causes within text. Despite substantial progress, most current approaches depend on sequence modeling or standard attention mechanisms, which frequently overlook intricate inter-sentential relationships and fail to utilize causal commonsense knowledge to enhance semantic links between clauses. To address these limitations, this paper introduces KESIF, a novel emotion–cause pair extraction model that integrates knowledge enhancement with enriched semantic information for improved performance. The proposed model incorporates a graph attention network to capture semantic dependency relationships between sentences, integrates causal commonsense knowledge from the ATOMIC knowledge base to enrich semantic representations, and utilizes a bidirectional MRC mechanism for achieving effective bidirectional matching between emotions and causes. The model’s performance is assessed using core metrics, such as precision, recall, and F1 score. Experimental results on both Chinese and English datasets demonstrate that our method outperforms SOTA baselines. Full article

While perfectionism is recognized as a complex personality trait with both adaptive and maladaptive facets in adults, the specific developmental and contextual factors that influence its emergence in children are poorly understood. This study addresses this critical gap by examining associations between children’s perceptions of family climate and emotion regulation strategies. A sample of 191 children (94 boys, M_age = 11.27 years, SD = 0.97) completed standardized measures of perfectionism, family environment, and emotion regulation. Results indicated that both family climate and emotion regulation significantly predict perfectionism in children (R² = 0.36). Specifically, children’s perceptions of high parental control, a strong achievement family orientation, and reliance on expressive suppression (hiding emotions) emerged as moderate, significant predictors. These findings clarify the developmental factors underlying perfectionism, providing actionable targets—particularly around adaptive parenting and emotion coping—for child and family support programs and preventative interventions focused on promoting long-term well-being. Full article

This paper presents a narrative review of the contextualization and contribution offered by vision–language models (VLMs) for human-centric understanding in images. Starting from the correlation between humans and their context (background) and by incorporating VLM-generated embeddings into recognition architectures, recent solutions have advanced the recognition of human actions, the detection and classification of violent behavior, and inference of human emotions from body posture and facial expression. While powerful and general, VLMs may also introduce biases that can be reflected in the overall performance. Unlike prior reviews that focus on a single task or generic image captioning, this review jointly examines multiple human-centric problems in VLM-based approaches. The study begins by describing the key elements of VLMs (including architectural foundations, pre-training techniques, and cross-modal fusion strategies) and explains why they are suitable for contextualization. In addition to highlighting the improvements brought by VLMs, it critically discusses their limitations (including human-related biases) and presents a mathematical perspective and strategies for mitigating them. This review aims to consolidate the technical landscape of VLM-based contextualization for human state recognition and detection. It aims to serve as a foundational reference for researchers seeking to control the power of language-guided VLMs in recognizing human states correlated with contextual cues. Full article

This paper explores the potential of interdisciplinary teaching that combines science and fine art to foster students’ responsible engagement with environmental and social challenges, positioned as an important contribution to sustainability. Within a participatory action research project conducted over five cycles in a Slovenian primary school, the Model of Interdisciplinary Teaching in Science and Fine Art (MITSFA) was developed. It integrates problem-based science tasks, experimental work, reflective discussions, and art assignments with a strong communicative and esthetic dimension. The paper analyses activities that encouraged scientific inquiry, critical thinking, and visual interpretation of complex phenomena, ranging from material properties to sustainable spatial planning. Empirical data include students’ artworks, interviews, written reflections, and the teacher’s research diary. Findings suggest that combining scientific exploration with visual expression deepens understanding, fosters emotional engagement, and promotes environmental and social awareness. Students showed greater sensitivity to complexity, ability to recognize layered meanings, and readiness to express their worldview through art. It can be concluded that meaningful learning emerges where scientific and artistic processes are interconnected, highlighting the teacher’s role as a creative facilitator bridging investigation and interpretation. The study demonstrates how integrating science and fine art in primary education directly supports education for sustainable development by cultivating environmental awareness and responsibility. Full article