Search Results (99)

In ecosystems of algorithmic visibility, Instagram bios operate as high salience microdiscourses of self-presentation and signaling. We examine whether observable bio attributes are associated with visibility and interaction among Brazilian sports journalists. We analyzed 151 public Instagram profiles (≥100,000 followers) and extracted bios and profile metadata via automated collection. Bio attributes (length, emojis, @mentions, hashtags, location, informational cues, and external links) were related to followers, average likes and comments, and engagement rate (primary outcome) using Spearman rank correlations under conservative interpretation. Emojis and mentions were near universal; links were common; hashtags and locations were rare. Associations were small and exploratory: personal information correlated negatively with followers; hashtags correlated positively with likes and comments but relied on five cases; and references to other platforms correlated negatively with engagement. Overall, bios appear to function mainly as signaling infrastructures, with any performance effects likely indirect and mediated by content practices and platform exposure within this ecosystem. Full article

Previous research on Internet altruistic behavior (IAB) has primarily focused on helpers. However, as a form of prosocial behavior embedded in online interpersonal interaction, IAB is likely shaped by characteristics of both helpers and seekers. Accordingly, the present research integrated attachment theory and social information processing models to examine how helper-related factors (secure attachment and positive empathy) and seeker-related factors (emoji symbols) influence IAB, as well as the mechanisms underlying these effects. Three studies were conducted. Study 1 used a one-factor between-subjects design to examine the effect of secure attachment on IAB. Study 2 employed an experimental causal-chain approach with three sub-studies to test the causal links among secure attachment, positive empathy, and IAB. Study 3 used a 2 (positive empathy: high vs. low) × 2 (emoji: present vs. absent) between-subjects design to examine whether seekers’ emoji use moderated the relationship between helpers’ positive empathy and IAB. The results showed that individuals with higher (vs. lower) secure attachment engaged in more IAB, and that positive empathy mediated the relationship between secure attachment and IAB. In addition, seekers’ emoji use significantly moderated the relationship between helpers’ positive empathy and IAB. Specifically, this moderation was antagonistic: the presence of emoji was associated with a weaker, rather than stronger, positive relationship between positive empathy and IAB. Nevertheless, overall levels of IAB were higher in messages containing emoji. These findings provide a more comprehensive understanding of the antecedents of IAB and offer practical implications for promoting altruistic behavior in online contexts. Full article

Online learning environments often lack nonverbal cues that support relational communication between instructors and students, making it difficult to establish emotional connection and trust. This study examines how emojis, as affective visual cues, influence instructor credibility, emotional attachment, trust, and responsible behavior in asynchronous online education. A scenario-based experiment was conducted with 297 undergraduate students who were randomly assigned to view an instructor email either with or without emojis. Participants subsequently evaluated the instructor’s credibility (expertise, trustworthiness, and likeability), as well as their emotional attachment, trust, and anticipated responsible behavior. The results indicate that emojis significantly enhance perceived likeability, which in turn fosters emotional attachment and trust. These relational mechanisms subsequently promote students’ responsible behavior. In contrast, expertise and trustworthiness do not independently produce significant effects on emotional attachment or trust. These findings highlight the central role of relational warmth in shaping student responses in digitally mediated learning environments. Overall, the study demonstrates that subtle affective cues can strengthen instructor–student relationships and encourage responsible engagement in asynchronous online contexts. Full article

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

The objective of this paper is to propose a sociological and interdisciplinary framework for analyzing the digitization of emotions in adolescence. This contribution aims to promote theoretical reflection and inform educational and political interventions in the digital age, framing adolescents’ digital experiences as emotionally embodied and socially integrated processes. These aspects are of paramount importance due to the rapid proliferation of digital technologies and artificial intelligence, which have precipitated a profound transformation in the emotional, relational, and educational experiences of adolescents. The role of digital and AI-based environments in mediating communication is expanding beyond the scope of simple facilitation. These environments are increasingly implicated in the production, modulation, and regulation of emotions, thereby influencing developmental trajectories and identity formation processes. This phenomenon is theorized as a socio-technical process, wherein emotions are embodied, narrated, and governed within digital environments. The article introduces the concept of digital emotional embodiment, drawing on the sociology of emotions, theories of embodiment, and critical perspectives on artificial intelligence. Specifically, the concept refers to the manner in which adolescents experience and express emotions through avatars, images, emojis, algorithmic feedback, and AI-mediated interactions. Therefore, it is imperative to underscore the evolution of empathy, which is progressively configured as a virtualized and datafied process, diverging from the tradition established by Hume and characterized by sympathy. In contemporary processes, shaped by the logic of platforms, recommendation systems, and emotionally reactive technologies, conventional emotional concepts have undergone deconstruction, and digital constructs are undergoing a gradual restructuring. In this context, AI systems do not merely reflect adolescents’ emotions but rather actively contribute to the construction of emotional narratives, influencing emotional regulation, social connection, and future orientation. Digital environments have been shown to encourage emotional expressiveness, experimentation, and inclusivity. Conversely, they have the capacity to encourage emotional standardization, dependency, and forms of affective vulnerability, particularly during a sensitive developmental stage such as adolescence. Full article

This work proposes a hybrid Uzbek sentiment analysis model (sometimes referred to as tonality analysis in the local literature) that integrates contextual text representations with named-entity information from an NER module and emoji-based emotional cues that are common in short online messages. To provide a comprehensive baseline comparison, we evaluate seven approaches—SVM, LSTM, mBERT, XLM-RoBERTa-base, mDeBERTa-v3, LaBSE, and the proposed hybrid model—covering both classical machine learning and modern multilingual transformer architectures for low-resource sentiment tasks. The overall pipeline begins with Uzbek-specific text normalization to reduce noise from informal spellings, transliteration variants, and inconsistent apostrophe usage. In parallel, the system performs explicit emoji extraction to capture affective signals that are often expressed non-verbally in social media texts. Next, we construct three complementary feature streams: a context encoder for sentence-level semantics, NER-driven entity features that encode entity mentions and types, and an emotion module that models emoji priors and their interaction with contextual meaning. These streams are fused into a unified representation and fed to a final classifier to predict sentiment polarity. Experiments on an Uzbek test set demonstrate that the hybrid model reaches an F1-score of 0.92, consistently outperforming text-only baselines. The results indicate that entity-aware and emoji-informed features improve robustness under sarcasm/irony, mixed sentiment with multiple targets, and orthographic noise, making the approach suitable for social media analytics, public opinion monitoring, customer feedback triage, and recommendation-oriented text mining. Full article

Conversational Agents have been showing promise for depression in adults in the short-term. Although, there has been little research done for conversational agents (CAs) with depression in adolescents. This study aimed to determine adolescents’ user experience with Athenabot, a behavioral activation CA for depression. The study included 66 participants who interacted with Athenabot. Participants were aged 13 to 18 (mean = 14.12) and predominantly identified as female (56.1%). Participants’ confidence in the CA’s utility to improve mood significantly increased from baseline to post-intervention (p < 0.001). Adolescents provided an acceptable Net Promoter Score of 6.73. Positive themes from feedback included the CA being helpful and favorably viewed, while negative themes included its perceived audience-dependency and impersonal nature. Recommendations for improvement included reducing repetitive questions and enhancing personalization. Adolescents significantly preferred multiple-choice questions over typed response questions (p < 0.05). However, there were no significant differences in preference for emojis, memes, or GIFs. Adolescents reported an increased confidence that the CA could improve their mood. While the CAs received acceptable support, feedback highlighted a need for improved engagement and personalization. Adolescents favored multiple-choice button questions over typed responses and preferred GIFs over memes and emojis, with no significant demographic differences. Full article

Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates traditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on Term Frequency–Inverse Definition Frequency (TF–IDF)-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and AraBERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experimental configurations: (1) a baseline traditional machine learning architecture that employs TF-IDF, N-grams, and emoji features with an Support Vector Machine (SVM) classifier; (2) an Large-language Model (LLM) feature extraction approach that leverages deep contextual embeddings from the pre-trained AraBERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classification task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline. Full article

This data descriptor presents two fully synthetic corpora for sentiment analysis and named entity recognition (NER) in Uzbek. The first corpus contains 12,000 hybrid synthetic sentences generated from templates with lexical randomization, automatic insertion of named entities (PER/ORG/LOC), lexicon-based polarity scoring, and a controlled emoji distribution. The second corpus includes 3000 “manual-style” sentences designed to resemble short, naturally structured messages. Although the manual-style subset was initially intended to be emoji-free, the released version includes a 39.6% emoji presence (sentences containing at least one emoji) to maintain comparability in emotional markers across corpora. Both corpora are released in CSV, XLSX, and JSONL formats and share a unified schema (id, text, sentiment, entities, entity_type, polarity_score, polarity_source, token_count, emojis, emoji_position, emoji_sentiment, conflict_flag, sentiment_from_polarity_score, split). The dataset is publicly available via Mendeley Data (DOI: 10.17632/y2d5pcyrzz.3). Full article

The integration of emojis into Early Childhood Education (ECE) presents a promising avenue for enhancing student engagement, emotional expression, and comprehension. While prior studies suggest the benefit of visual aids in learning, systematic frameworks for pedagogically aligned emoji recommendation remain underdeveloped. This paper presents EduEmoji-ECE, a pedagogically annotated dataset of early-childhood learning text segments. Specifically, the proposed model incorporates Bidirectional Encoder Representations from Transformers (BERTs) for contextual embedding extraction, Gated Recurrent Units (GRUs) for sequential pattern recognition, Deep Neural Networks (DNNs) for classification and emoji recommendation, and DECOC for improving emoji class prediction robustness. This hybrid BERT-GRU-DNN-DECOC architecture effectively captures textual semantics, emotional tone, and pedagogical intent, ensuring the alignment of emoji class recommendation with learning objectives. The experimental results show that the system is effective, with an accuracy of 95.3%, a precision of 93%, a recall of 91.8%, and an F1-score of 92.3%, outperforming baseline models in terms of contextual understanding and overall accuracy. This work helps fill a gap in AI-based education by combining learning with visual support for young children. The results suggest an association between emoji-enhanced materials and improved engagement/comprehension indicators in our exploratory classroom setting; however, causal attribution to the AI placement mechanism is not supported by the current study design. Full article

Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for nursing faculty, senior clinical nurses, and nurse leaders, developed using a design-thinking approach supported by generative AI. Methods: The workshop comprised four thematic sessions: (1) Learning styles across generations, (2) Interactive teaching methods, (3) Application of interactive teaching strategies, and (4) Lesson planning and transfer. Generative AI was used during planning to create icebreakers, discussion prompts, clinical teaching scenarios, and application templates. Design decisions emphasized low-tech, low-prep strategies suitable for spontaneous clinical teaching, thereby reducing barriers to adoption. Activities included emoji-card introductions, quick generational polls, colored-paper reflections, portable whiteboard brainstorming, role plays, fishbowl discussions, gallery walks, and movement-based group exercises. Participants (N = 37) were predominantly female (95%) and represented multiple generations of X, Y, and Z. Mid- and end-of-workshop reflection prompts were embedded within Sessions 2 and 4, with participants recording their responses on colored papers, which were then compiled into a single Word document for thematic analysis. Results: Thematic analysis of 59 mid- and end-workshop reflections revealed six interconnected themes, grouped into three categories: (1) engagement and experiential learning, (2) practical applicability and generational awareness, and (3) facilitation, environment, and motivation. Participants emphasized the workshop’s lively pace and hands-on design. Experiencing strategies firsthand built confidence for application, while generational awareness encouraged reflection on adapting methods for younger learners. The facilitator’s passion, personable approach, and structured use of peer learning created a psychologically safe and motivating climate, leaving participants recharged and inspired to integrate interactive methods. Discussion: The workshop illustrates how AI-assisted, design-thinking-driven professional development can model effective strategies for next-generation learners. When paired with skilled facilitation, AI-supported planning enhances engagement, fosters reflective practice, and promotes immediate transfer of interactive strategies into diverse teaching settings. Full article

Vegetable consumption in childhood remains below recommendations worldwide, particularly in disadvantaged socioeconomic groups. Building on prior work showing no socioeconomic status (SES) differences in children’s liking of familiar vegetables, this study examined whether their sensory and emotional descriptions vary by SES and how these relate to liking beyond hedonic ratings. A total of 363 Chilean fourth graders (9–10 years) from five SES groups evaluated eight vegetables at school. For each sample, children rated overall liking (7-point facial hedonic scale) and completed two CATA (Check-All-That-Apply) tasks: a child-derived sensory list (13 terms) and a validated emoji-based emotion list (33 items). Data were analyzed using Cochran’s Q tests, correspondence analyses, and mean-impact analyses. The use and diversity of sensory and emotional descriptors differed significantly between socioeconomic groups (p < 0.05): children from higher SES levels employed a broader and more differentiated vocabulary, while those from lower SES backgrounds used fewer significant terms. Across the sample, juicy, fresh, and mild flavors increased liking, whereas strong aroma decreased it (p < 0.05); positive emojis increased liking, whereas negative and neutral ones had no effect. These findings reveal that perceptual and affective representations are socially patterned, underscoring the need to foster sensory–affective literacy in lower-SES contexts. Full article

Depression represents a critical global mental health challenge, with social media offering unprecedented opportunities for early detection through computational analysis. We propose a novel BERT–CNN–BiLSTM architecture with attention mechanisms that systematically integrate emoji usage patterns—fundamental components of digital emotional expression overlooked by existing approaches. Evaluated on the SuicidEmoji dataset, our model achieves 97.12% accuracy, 94.56% precision, 93.44% F1-score, 85.67% MCC, and 91.23% AUC-ROC. Analysis reveals distinct emoji patterns: depressed users favour negative emojis (😔 13.9%, 😢 12.8%, 💔 6.7%) while controls prefer positive expressions (😂 16.5%, 😊 11.0%, 😎 10.2%). The attention mechanism identifies key linguistic markers, including emotional indicators, personal pronouns, and emoji features, providing interpretable insights into depression-related language. Our findings suggest that the integration of emojis substantially improves optimal social media-based mental health detection systems. Full article

The decontextualization (the portrayal of wildlife removed from their natural ecological context through social media), can distort the public perception of these animals and harm conservation efforts. This paper presents an exploratory case study based on two highly visible Facebook videos. To explore this, we analyzed Facebook comments (n = 720) and emoji-based reactions (n = 23,024) regarding Barbary macaques (Macaca sylvanus) in two contexts: entertainment (macaque dressed in sports attire during political protests) and natural habitat (macaque being fed soda by tourists in its forest environment). This is the first study to examine how social media context influences public perception of Barbary macaque conservation status and welfare through analysis of viewer engagement on viral videos. The results indicated that videos depicting macaques in their natural habitat elicited significantly more positive conservation sentiments (68.4% of comments) compared to entertainment contexts (6.04% of comments). Conversely, the entertainment video generated predominantly negative conservation sentiments (54.95% of comments), with viewers expressing amusement rather than concern for species protection. Videos showing macaques in natural settings, particularly when depicting problematic feeding behaviors, prompted more critical engagement and awareness of conservation issues. This pattern suggests that anthropomorphized contexts may obscure recognition of species threats and normalize inappropriate human–wildlife interactions. Given the small dataset, these findings should be interpreted cautiously and as illustrative rather than generalizable. These findings lend preliminary support to the animal decontextualization hypothesis and underscore the importance of context in shaping public perceptions of wildlife and conservation priorities. Full article

Background: Social media has become a space for sharing personal experiences and shaping public opinion. This study explored how people respond to substance misuse recovery journeys shared on TikTok. Methods: The researchers collected 3583 comments from 350 TikTok videos under the hashtags #wedorecover, #recovery, and #sobertok using a scraper tool. A discourse analysis categorized comments into Narrative Strategies, Rhetorical Strategies, Linguistic Features, and Power Relationships, each with subcategories revealing public perceptions of substance use and recovery. A correlation analysis was also conducted to examine the role of emojis across narrative and linguistic features. Results: Most comments (94%) expressed support or positivity toward recovery videos. The heart emoji was the most common (93.35% of all emojis), symbolizing connection, encouragement, and solidarity. Four themes emerged reflecting public attitudes: encouragement and positive messaging, acknowledgment of struggle, the culture of sharing, and the influence of broader social narratives. Conclusions: These results provide insight into public responses to recovery content on TikTok, suggesting that peer support may be facilitated through the platform’s algorithmic design. While TikTok shows promise as a supportive digital space, further research is needed to understand its broader implications for substance use recovery support. Full article