Mapping variation in orangutan gestural communication using multilayer social networks

Social media use among young people has been associated with body dissatisfaction due to exposure to hegemonic beauty standards. This study examined the effect of hegemonic beauty content on body dissatisfaction among Indonesian adults. A true experiment with a pretest-posttest control group design involved 40 participants (20 females and 20 males) aged 18–25 years. Repeated-measures ANOVA showed no significant difference in body dissatisfaction between the experimental and control groups (p = .335). The control group had a higher mean score (44.770) than the experimental group (31.681), although the difference was not statistically significant. These findings indicate that brief exposure to body diversity content did not significantly affect body dissatisfaction. This result may reflect limited exposure duration and participants’ prior experiences with beauty standards. Future research should examine longer exposure periods using naturalistic longitudinal designs that account for individual differences and cultural context in social media use and body image.

This mixed-methods study had two aims: to determine the purpose of school administrators’ use of social media and to develop and validate a scale measuring those purposes. In the qualitative phase, 15 school principals were interviewed individually, and the social media accounts of five principals were analyzed to identify purposes of use. In the second phase, an instrument was developed and validated across two independent samples of school administrators attending separate in-service training programs organized by the Turkish Ministry of National Education (MONE). Exploratory factor analysis (EFA; n = 156), using principal axis factoring with direct oblimin rotation, revealed a three-factor structure: stakeholder communication, personal use, and professional development, accounting for 65.52% of the total variance. Confirmatory factor analysis (CFA; n = 148) supported the factor structure with satisfactory model fit (χ²/df = 1.60, CFI = .955, RMSEA = .064). Convergent and discriminant validity were established through average variance extracted, composite reliability, McDonald’s omega, and the Fornell–Larcker criterion.

In recent years, retro-style outfits have continued to gain popularity on social media platforms. Clothing styles marked by earlier eras, such as Y2K millennium style, vintage outfits, and retro sportswear, have become widely popular among young people. This phenomenon raises a question worth exploring: why have young people developed such strong interest in “old clothes”? What role does social media play in the transformation of nostalgic emotions into consumption behavior? Taking 2018 to 2026 as the time window, this paper systematically reviews the literature on youth nostalgic consumption in social media environments and discusses it from three dimensions: conceptual definition, activation mechanism, and identity. The review preliminarily reveals the role of social media in the dissemination and diffusion of nostalgic content and also confirms that nostalgic consumption is an important path through which young people respond to identity anxiety and seek group belonging. However, existing studies still have three limitations. First, they lack a systematic explanation of how platform mechanisms promote the transformation of nostalgic emotions into consumption behavior. Second, they pay insufficient attention to how young people continuously construct identity through symbolic practices, community interaction, and consumption choices on social media. Third, the special mechanism of “non-experiential nostalgia” in the digital age has not been fully discussed. Future research may focus on constructing theoretical models that take social media as an intermediary variable and connect the logical path from emotion to behavior and then to identity.

Introduction This study treats adolescent athletes' self-efficacy as a dynamic process rather than a fixed quantity, asking how its fluctuations arise in online social settings and how that volatility shapes training engagement. Methods We recruited a stratified sample of 320 youth athletes across sport discipline, competitive tier, and sex, and combined ecological momentary assessment with weekly follow-up over 8 consecutive weeks, using instruments released under open licenses and open-source software throughout. Two dispersion indices, the intraindividual standard deviation and the root mean square of successive differences, summarized fluctuation at the person level, while absolute occasion-to-occasion change indexed momentary instability at the event level. We fitted multilevel linear models, a two-level dynamic structural equation model, latent growth curve models, and multilevel structural equation models, with each path labeled by its level of analysis. Results Momentary self-efficacy showed genuine within-person temporal dependence rather than independent scatter, and its trajectory was phase-linked to the competitive calendar and unevenly distributed across individuals. Between persons, fluctuation amplitude tracked the intensity of negatively valenced comparison rather than the sheer volume of feedback, and comparison orientation amplified that association on the negative arm alone. Within persons, negative comparison enlarged momentary instability both immediately and at a one-occasion lag, whereas positive signals steadied confidence only briefly. Amplitude was negatively associated with all three engagement facets beyond mean efficacy, most strongly for absorption, although a formal equality test separated absorption from dedication but not vigor. The direct reach of negative comparison into engagement proved facet-specific: vigor and absorption were affected, dedication was not, so the facet-by-facet hypothesis holds only in part. Sensitivity analyses adding competitive-calendar phase, splitting the series by season phase, partialling the person mean and the person-specific autoregression out of the amplitude index, entering competitive tier as a covariate, and checking participant-coded valence against automated sentiment scoring left the pattern intact. Discussion The findings extend social cognitive theory's vicarious and persuasive sources into a temporally dense digital regime and point toward protective coaching and self-regulation strategies.

Suicidal ideation is the consideration of self-inflicted death and has emerged as a serious and rising concern for worldwide public health systems, contributing to over 800,000 deaths annually worldwide. Individuals express intentions, distress, and thoughts of self-harm and death, thereby offering a crucial and unprecedented avenue for timely psychological intervention and surveillance. The core problem addressed by this research is the pronounced global insufficiency in detecting and mitigating suicidal ideation across linguistically diverse environments, specifically focusing on low-resource languages. Previous computational studies have primarily focused on English and other widely used languages, leaving a critical research gap for contexts such as Amharic. The main aim of this study was to investigate and to develop a robust and optimal deep learning model for identifying suicidal ideation in Amharic social media content. A total of 5,428 postings from social media such as Facebook and Telegram were carefully collected, manually annotated, and processed to provide the first publicly available dataset on Amharic suicidal ideation. After preprocessing, Word2Vec, GloVe, and FastText were among the word embedding techniques used for feature engineering. Five deep learning models and four machine learning models were then compared in this study. Based on the experimental results, ensemble learning models achieved slightly higher performance than the individual deep learning models in suicidal ideation detection. Specifically, hard voting ensemble learning models achieved the highest accuracy (83.3%) and precision (86.55%). CNN-BiLSTM achieved an accuracy of 81.45%, while the BiLSTM model achieved an accuracy of 81.4%, indicating that bidirectional sequence learning effectively captures contextual information in Amharic texts. Beyond its strong empirical performance, this study contributes a robust detection framework and introduces a public Amharic dataset, thereby establishing a foundational methodological resource for mental health research. Future studies should focus on developing scalable datasets and context-aware deep learning models that effectively handle multimodal data, multilingual transformer models, fusion-based features, and addressing bias in low-resource computational linguistics.

افکار خودکشی به معنای اندیشیدن به مرگ خودخواسته است و به عنوان یک نگرانی جدی و رو به افزایش برای نظام‌های سلامت عمومی در سراسر جهان مطرح شده است، به طوری که سالانه بیش از ۸۰۰٬۰۰۰ مرگ را در سطح جهانی رقم می‌زند. افراد، نیت‌ها، پریشانی و افکار آسیب به خود و مرگ را ابراز می‌کنند و بدین ترتیب، مسیر مهم و بی‌سابقه‌ای برای مداخله و پایش روان‌شناختی به موقع فراهم می‌شود. مسئله اصلی که این پژوهش به آن می‌پردازد، کمبود چشمگیر جهانی در شناسایی و کاهش افکار خودکشی در محیط‌های زبانی متنوع، به ویژه در زبان‌های کم‌منبع است. مطالعات محاسباتی پیشین عمدتاً بر زبان انگلیسی و سایر زبان‌های پرکاربرد تمرکز داشته‌اند و این امر موجب ایجاد شکاف پژوهشی مهمی در زمینه‌هایی مانند زبان آمهاریک شده است. هدف اصلی این مطالعه، بررسی و توسعه یک مدل یادگیری عمیق قوی و بهینه برای شناسایی افکار خودکشی در محتوای شبکه‌های اجتماعی به زبان آمهاریک بود. در مجموع، ۵٬۴۲۸ پست از شبکه‌های اجتماعی مانند فیس‌بوک و تلگرام با دقت جمع‌آوری، به صورت دستی برچسب‌گذاری و پردازش شدند تا نخستین مجموعه‌داده عمومی درباره افکار خودکشی به زبان آمهاریک فراهم شود. پس از پیش‌پردازش، تکنیک‌های تعبیه واژه مانند Word2Vec، GloVe و FastText برای مهندسی ویژگی‌ها به کار گرفته شدند. در این پژوهش، پنج مدل یادگیری عمیق و چهار مدل یادگیری ماشین با یکدیگر مقایسه شدند. بر اساس نتایج تجربی، مدل‌های یادگیری تجمعی (ensemble learning) عملکردی اندکی بهتر از مدل‌های یادگیری عمیق منفرد در شناسایی افکار خودکشی داشتند. به طور خاص، مدل‌های یادگیری تجمعی با رأی‌گیری سخت (hard voting) بالاترین دقت (۸۳/۳٪) و دقت مثبت (precision) (۸۶/۵۵٪) را به دست آوردند. مدل CNN-BiLSTM به دقت ۸۱/۴۵٪ و مدل BiLSTM به دقت ۸۱/۴٪ دست یافتند که نشان می‌دهد یادگیری دنباله‌ای دوسویه به طور مؤثری اطلاعات زمینه‌ای را در متون آمهاریک استخراج می‌کند. افزون بر عملکرد تجربی قوی، این پژوهش چارچوب شناسایی قابل اعتمادی ارائه داده و مجموعه‌داده عمومی آمهاریک را معرفی می‌کند و بدین ترتیب منبع روش‌شناختی بنیادی برای پژوهش‌های سلامت روان فراهم می‌آورد. مطالعات آتی باید بر توسعه مجموعه‌داده‌های مقیاس‌پذیر و مدل‌های یادگیری عمیق آگاه از زمینه که به طور مؤثر داده‌های چندوجهی، مدل‌های ترنسفورمر چندزبانه، ویژگی‌های مبتنی بر همجوشی و رفع سوگیری در زبان‌شناسی محاسباتی کم‌منبع را مدیریت می‌کنند، تمرکز کنند.