Emotional Content Research Articles

"All the Stars Will Be Wells with a Rusty Pulley": Neural Processing of the Social and Pragmatic Content in a Narrative.

Making sense of natural language and narratives requires building and manipulating a situation model by adding incoming information to the model and using the context stored in the model to comprehend subsequent details and events. Situation model maintenance is supported by the default mode network (DMN), but comprehension of the individual moments in the narrative relies on access to the conceptual store within the semantic system. The present study examined how these systems are engaged by different narrative content to investigate whether highly informative, or semantic, content is a particularly strong driver of semantic system activation compared with contextually driven content that requires using the situation model, which might instead engage DMN regions. The study further investigated which subregions of the graded semantic hub in the left anterior temporal lobe (ATL) were engaged by the type of narrative content. To do this, we quantified the semantic, pragmatic, social, ambiguous, and emotional content for each sentence in a complete narrative, the English translation of The Little Prince. Increased activation in the transmodal hub in the ventral ATL was only observed for high semantic (i.e., informative) relative to low semantic sentences. Activation in the dorsolateral and ventrolateral ATL subregions was observed for both high relative to low semantic and social content sentences, but the ventrolateral ATL effects were more extensive in the social condition. There was high correspondence between the social and pragmatic content results, particularly in the ventrolateral ATL. We argue that the ventrolateral ATL may be particularly engaged by internal, or endogenous, processing demands, aided by functional connections between the anterior middle temporal gyrus and the DMN. Pragmatic and social content may have driven endogenous processing given the pervasive and plot-progressing nature of this content in the narrative. We put forward a revised account of how the semantic system is engaged in naturalistic contexts, a critical step toward better understanding real-world semantic and social processing.

Skin conductance response and habituation to emotional facial expressions and words.

Skin conductance response (SCR) serves as a dependable marker of sympathetic activation used to measure emotional arousal. This study investigates the impact of presentation modality (face or word) on the degree of emotional discrimination elicited by SCR. Facial expressions or words associated with six basic emotions-anger, happiness, disgust, fear, sadness, and surprise-were studied among 102 participants. The amplitude of SCR was accurately predicted by subjective arousal ratings of these stimuli, but not by valence ratings. The habituation process to emotional and neutral stimuli across six successive presentations was characterized by an exponential decay function, capturing the rate at which SCR response diminishes in relation to the preceding trial of the same stimulus. Through the subtraction of the response to neutral stimuli from the emotion-evoked SCR, it was demonstrated that the initial presentation of each emotion elicits a substantial response, particularly attributable to the emotional content. Notably, the initial emotional response to faces expressing happiness, disgust, and sadness surpassed that of words conveying the same emotions. The results indicate that different emotional responses can be quantified using a simple electrical instrument.

Enhancing public engagement through NICU storytelling on Facebook and Instagram: a case study from Gaslini Children’s Hospital

BackgroundSocial media platforms, particularly Instagram, have become pivotal in disseminating medical information to the public. However, the challenge of presenting neonatal intensive care unit (NICU) stories in a manner that is both engaging and informative remains significant, especially in the context of a reputable pediatric hospital.ObjectiveThis study aimed to explore the effectiveness of Instagram as a storytelling medium for NICU experiences at the Gaslini Children’s Hospital, focusing on the impact of various storytelling elements on audience engagement and follower growth.MethodsWe analyzed Facebook and Instagram posts of the Gaslini Children’s Hospital, examining factors such as emotional appeal, news value, and content quality. Additionally, we considered the adherence of these posts to journalistic and medical guidelines.ResultsThe research identified key themes in successful posts, including emotional resonance, portrayal of medical excellence, and humanization of patient stories. Posts that effectively combined these elements saw a significant increase in audience engagement, including likes, comments, and shares.ConclusionStrategic storytelling on Instagram can significantly enhance public engagement with hospital content, fostering a deeper connection between the institution and its audience. This study underscores the significance of narrative elements and adherence to ethical guidelines in crafting compelling medical stories for social media.

Hierarchical Attention and Contextual Embedding for Robust Sarcasm Detection

Objective: The study aims to develop a technique for sarcasm detection in social media text to enrich the sentiment analysis. Methods: The methodology used in the present research is as follows: emotion-embedded vectors for capturing the emotional content of the text, dynamic contextual modulation for the adaption of the model to the given context, and Hierarchical attention mechanism for segmentation of the text at the different level of abstraction. Findings: Evaluation with the test set proved that the proposed model achieved accuracy rates 89% higher than the benchmark models. Including these several complicated methods helped achieve greater accuracy and F1 measures, contributing to sarcasm detection efficiency. Novelty: This approach ensures that vital sarcastic alerts are detected, providing a better text analysis and increasing sentiment analysis's accuracy and robustness. Keywords: Sarcasm detection, Sentiment analysis, Natural language processing, Deep learning, Opinion mining

Peer threat evaluations shape one’s own threat perceptions and feelings of distress

ABSTRACT We are continuously exposed to what others think and feel about content online. How do others’ evaluations shared in this medium influence our own beliefs and emotional responses? In two pre-registered studies, we investigated the social transmission of threat and safety evaluations in a paradigm that mimicked online social media platforms. In Study 1 (N = 103), participants viewed images and indicated how distressed they made them feel. Participants then categorised these images as threatening or safe for others to see, while seeing how “previous participants” ostensibly categorised them (these values were actually manipulated across images). We found that participants incorporated both peers’ categorisations of the images and their own distress ratings when categorizing images as threatening or safe. Study 2 (N = 115) replicated these findings and further demonstrated that peers’ categorisations shifted how distressed these images made them feel. Taken together, our results indicate that people integrate their own and others’ experiences when exposed to emotional content and that social information can influence both our perceptions of things as threatening or safe, as well as our own emotional responses to them. Our findings provide replicable experimental evidence that social information is a powerful conduit for the transmission of affective evaluations and experiences.

Together we can do it! A roadmap to effectively tackle propaganda-related tasks

PurposeIn this paper, we address the need to study automatic propaganda detection to establish a course of action when faced with such a complex task. Although many isolated tasks have been proposed, a roadmap on how to best approach a new task from the perspective of text formality or the leverage of existing resources has not been explored yet.Design/methodology/approachWe present a comprehensive study using several datasets on textual propaganda and different techniques to tackle it. We explore diverse collections with varied characteristics and analyze methodologies, from classic machine learning algorithms, to multi-task learning to utilize the available data in such models.FindingsOur results show that transformer-based approaches are the best option with high-quality collections, and emotionally enriched inputs improve the results for Twitter content. Additionally, MTL achieves the best results in two of the five scenarios we analyzed. Notably, in one of the scenarios, the model achieves an F1 score of 0.78, significantly surpassing the transformer baseline model’s F1 score of 0.68.Research limitations/implicationsAfter finding a positive impact when leveraging propaganda’s emotional content, we propose further research into exploiting other complex dimensions, such as moral issues or logical reasoning.Originality/valueBased on our findings, we provide a roadmap for tackling propaganda-related tasks, depending on the types of training data available and the task to solve. This includes the application of MTL, which has yet to be fully exploited in propaganda detection.

Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores

This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into how different F1-score variants could improve the reliability of multilabel emotion classifier evaluation, particularly in the context of class imbalance present in the case of phishing emails.

Parents' experiences of psychotherapeutic support on the neonatal unit: A mixed methods systematic review to inform intervention development for a multicultural population.

Parents of infants admitted to neonatal intensive care require support to minimize the impact on their mental health and to encourage engagement with their infants to support infant neurodevelopment. Many interventions aim to address this need, but there is a lack of research considering the accessibility of these for a multicultural population. To systematically identify sources of psychotherapeutic support available for parents with infants admitted to neonatal care (NNU, neonatal intensive care unit [NICU] and special care units), assess their accessibility and acceptability and identify challenges and facilitators. Six electronic databases with no restrictions on language or date were used to identify relevant studies following Preferred Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Publications were included in the review if they reviewed parent experience of an intervention actively in place to support parent experience during the neonatal unit stay. Any studies where the intervention's primary aim was infant focused, such as developmental care, were excluded. All publications were quality-assessed using quality appraisal tools appropriate for their design type. Data were extracted line by line using Sekhon et al.'s theoretical acceptability framework and questionnaire. A total of 3309 studies were found, of which 36 studies met the inclusion criteria. Included studies were published worldwide between 2000 and 2023 and explored 15 different interventions. Challenges for parental engagement were due to preconceived ideas about intervention requirements and parents' ability to participate in them. Timely information and providers' experience in delivering the intervention were reported to support engagement and as being valuable for enhancing participant knowledge. The emotional content of interventions was found to be challenging by parents across most studies. This was prominent in interventions designed to be carried out in a group format and where keepsakes were created. However, the value of these interventions was in reducing parents' feelings of isolation through increased social support and providing a starting point for conversations with wider family and friends about the family's neonatal experience. Participant demographics were poorly reported, with only two studies taking into consideration the ethicality of the intervention. Poor reporting of participant demographics, and a focus on mothers as participants, means findings are not transferrable to the wider population of parents in neonatal units. Future studies should consider how to ensure that research and interventions are accessible to multicultural populations to improve the understanding of the acceptability of interventions. Better knowledge of neonates and the NNU setting amongst intervention providers could increase the accessibility of psychotherapeutic support for parents. Training for providers on how to manage sensitive conversations may also be beneficial to support parents during interventions. The impact of neonatal admission on parental mental health is increasingly recognized and reported. Interventions have been developed to reduce the negative impact on the mental health of parents. There continue to be significant health inequalities as a result of many services not taking into account the acceptability and accessibility of interventions in this setting for their multicultural populations. This review highlights the need for better reporting of participant demographics in research and the inclusion of those seldom heard to ensure interventions are culturally, religiously and linguistically appropriate for multicultural populations.

Graph Neural Network-Based Speech Emotion Recognition: A Fusion of Skip Graph Convolutional Networks and Graph Attention Networks

In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip graph convolutional networks (SkipGCNs) and graph attention networks (GATs) to address these challenges. SkipGCN incorporates skip connections, enhancing the flow of information across the network and mitigating issues such as vanishing gradients, while also facilitating deeper representation learning. Meanwhile, the GAT in the model assigns dynamic attention weights to neighboring nodes, allowing SkipGCNGAT to focus on both the most relevant local and global interactions within the speech data. This enables the model to capture subtle and complex dependencies between speech segments, thus facilitating a more accurate interpretation of emotional content. It overcomes the limitations of previous single-layer graph models, which were unable to effectively represent these intricate relationships across time and in different speech contexts. Additionally, by introducing a pre-pooling SkipGCN combination technique, we further enhance the ability of the model to integrate multi-layer information before pooling, improving its capacity to capture both spatial and temporal features in speech. Furthermore, we rigorously evaluated SkipGCNGAT on the IEMOCAP and MSP-IMPROV datasets, two benchmark datasets in SER. The results demonstrated that SkipGCNGAT consistently achieved state-of-the-art performance. These findings highlight the effectiveness of the proposed model in accurately recognizing emotions in speech, offering valuable insights and a solid foundation for future research on capturing complex relationships within speech signals for emotion recognition.

Body, image, and digital technology in adolescence and contemporary youth culture.

The physical, psychological and social changes that occur during adolescence constitute a physiological crisis that is necessary for development and growth. The establishment of a suitable "self-image" is important for facilitating harmonious psychophysical development during this time. In the current era, digital technology (DT) serves as an extraordinary means of communication for young people, who make significant use of images as a mode of expression. Accordingly, there is growing interest in the relationship between physical development, self-image and use of DT. A review of the published literature on the topic was carried out in April 2024. Fourteen studies (n = 14) were inclused from search of electronic databases such as PubMed, CINAHL, PsycInfo, MedLine, and Cochrane Library. The aim of this study is to explore the influence of (DT) on cultural models of adolescent body image, and how this "social" culture can affect wellbeing and development. It was considered that the rise of DT and social media (SM) emphasized in young people the culture of appearance, adherence to ideal models (thinness ideal) and social comparison at an unprecedented level. It was estimated that the digital mechanism works on the adolescent's vulnerability and stimulates the desire for experimentation and amplifies cultural beliefs that expose the young to deviant or pathological behaviors on the body. The use of digital images emphasizes the perception of self by making it more real and alive but empty of content. Our framework highlights that the adolescent can defend himself if he leaves the homologation that the SM condition, regains his own experiences, fill with emotional content and real life the images and the representation of the body.

EXPRESS: Exploring the differences in processing between Chinese emotion and emotion-laden words: A cross-task comparison study.

Affective words can be classified into two types: emotion words (EM words, e.g., "happy") and emotion-laden words (EL words, e.g., "wedding"). Several studies have shown differences in processing between EM and EL words, although results are inconclusive. These two types of words may have representational differences because affective content is an inherent part of the semantic features of EM words (i.e., denotative meaning) but not of EL words, whose affective content is part of their connotative meaning (i.e., these words do not name emotions, but are associated to emotions). In this study, we tested a set of Chinese EM and EL words. Both conditions included positive and negative words. The study involved two tasks, an implicit task, in which emotional content was not relevant (lexical decision task, LDT), and an explicit task, in which the emotional content was relevant (affective categorization task, ACT). Our results showed that participants responded faster to EM words than to EL words. This advantage was mostly observed in the ACT and with negative words. These results reveal differences in processing between EM and EL words which can be related to the greater relevance of affective content in the meaning of EM words compared to EL words.

Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications.

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

The impact of varying levels of compassion in ethics education case studies on students’ moral reasoning

ABSTRACT Previous research has revealed that including emotional content in ethics cases may improve students’ learning. However, there remain concerns as to whether the emotional content will also impact students’ moral reasoning. There is evidence that incorporating compassion-related emotional content into ethics cases through the implicit induction method increased the intensity of compassion without influencing the moral reasoning of participants. However, the impact of a higher intensity of compassion on moral reasoning remained unclear. Thus, in this experimental study, after using two (explicit expression and descriptions of severe consequences) methods of generating a higher intensity of compassion in ethics cases, we investigated whether this higher emotional intensity impacted the moral reasoning schemas activated in participants (N = 299). Our findings indicated that there was no evident impact. This research suggests there are now multiple strategies to integrate compassion into ethics cases at multiple levels of intensity without distorting moral reasoning.

‘The most stressful thing…was never the content, really’: The emotional impact PICS practitioners experience during consultation and formulation

The Psychologically Informed Consultation Service (PICS), based in Merseyside and Cheshire, aims to help Probation Practitioners develop a psychological understanding of people on probation who are assessed as high risk and have been reported to have personality disorder traits. A role of high importance and benefit, but one that also sometimes involves exposure to highly emotive content. It is vital to consider PICS Practitioners’ emotional wellbeing. This research aims to explore the emotional impact PICS Practitioners experience during the consultation and formulation processes. Semi-structured interviews were conducted with seven PICS Practitioners and Interpretative Phenomenological Analysis was used to analyse the data. Five Experiential Themes were identified: Changes within PICS impacted Practitioners’ experiences of the role; advantages and disadvantages to being a removed service; high workload having negative impact on consultation and formulation process; emotional impact of the role; and an overall sense of gratitude for the role and appreciating its purpose.

Research on the effectiveness and strategies of new media in promoting voluntary blood donation from a public health perspective in the post-pandemic era.

This study explores the effectiveness of new media in enhancing public enthusiasm for voluntary blood donation in the post-pandemic era and proposes effective publicity strategies to promote the sustainability and healthy development of blood donation activities. A questionnaire survey was widely used to collect public opinions and attitudes toward voluntary blood donation. The sample data underwent rigorous reliability and validity analysis to ensure authenticity and reliability. Statistical methods such as correlation analysis and regression analysis were employed to deeply investigate the underlying relationships between factors like new media publicity, emotional value, social recognition, convenience, information reliability, and willingness to donate blood voluntarily. Based on these analyses, a research model was constructed, and relevant hypotheses were verified through empirical methods. The study found that new media publicity may be associated with increasing the willingness of the public to voluntarily donate blood. Survey participants indicated that they were more likely to consider donating blood after exposure to new media publicity. Among these factors, the emotional value and content conveyed by the publicity appeared to be particularly important. Additionally, our research revealed that social recognition, the convenience of blood donation, and the reliability of promotional information seemed to have a direct impact on the public's willingness to donate blood. At the same time, these factors may indirectly promote actual blood donation behavior by enhancing the public's emotional resonance and acceptance of blood donation. This study suggests that new media may play multiple positive roles in promoting voluntary blood donation. Based on these findings, we propose a series of strategic recommendations, including further optimizing publicity content, striving to enhance the public's emotional resonance, improving the reliability of promotional information, and enhancing service convenience. These suggestions aim to potentially raise public awareness and willingness to participate in voluntary blood donation, providing a scientific basis and strong support for the promotion of voluntary blood donation in the post-epidemic era.

Assessing motivational biases in brain and behavior: Event-related potential and response time concomitants of the approach-avoidance task.

The approach-avoidance task (AAT) is designed to measure implicit motivated action biases instantiated by emotional stimuli and alterations in such biases that drive psychiatric disorder. While some research has measured AAT event-related potential (ERP) correlates to establish bias sensitivity even at a neural level, a lack of work with unpleasant, pleasant, and neutral stimuli together and a common focus on psychiatric disorder-matched (rather than generally emotional) content limits conclusions that can be drawn. Thus, current work extends the AAT literature by testing ERP modulations across normatively unpleasant, pleasant, and neutral conditions; and supporting the task's use as an individual difference assessment, it also provides data on AAT reliability and initially explores anxiety-related effects when stimuli are not disorder-matched. In 38 participants including 19 anxiety treatment-seeking individuals, 32 sensor electroencephalography revealed robust N100, N200, and late positive potential (LPP) ERP components and bias-consistent modulations for unpleasant images (reduced N200s on unpleasant push relative to pull trials; enhanced LPP for unpleasant compared to neutral trials). Meanwhile, modulations were less consistent with emotion-driven bias for other conditions-that is, LPPs were enhanced but N200 was not modulated for pleasant images, and for neutral images, N200 was unexpectedly enhanced on push compared to pull trials. Following these analyses, reliability tests revealed excellent raw ERP reliabilities but lower reliabilities for modulation scores, and comparing treatment- to non-treatment-seeking groups showed no preliminary indication of ERP modulation changes when stimuli are not personally relevant. How these findings together inform understanding of AAT as a measure of bias is discussed.

Age-inclusive hospitality and tourism: navigating the metaverse travel with avatar

Purpose This study aims to investigate the impact of virtual tours on the engagement and travel intentions of older adults, emphasizing the role of emotional and informative content. It aims to enhance travel confidence and reduce stress among older travelers, fostering inclusive tourism through advanced avatar technology. Design/methodology/approach Using two between-subjects experiments, this research compares the effects of emotion-driven and knowledge-centric virtual tours on older adults. It explores the mediating role of travel confidence and stress reduction, along with the moderating influence of positive psychological cues on engagement and travel intentions. Findings The results highlight the potential of technology in promoting inclusive tourism. Emotionally engaging virtual tours significantly increase travel intentions among older adults by boosting confidence and alleviating stress, with positive psychological cues enhancing these effects. Practical implications This study offers valuable insights for tourism industry stakeholders by suggesting the development of avatar-based virtual tours tailored to the emotional and cognitive needs of older travelers. This approach could create more accessible and satisfying tourism experiences for older travelers. Originality/value This study extends the socioemotional selectivity theory to the realm of metaverse travel, providing a novel perspective on the emotional and cognitive engagement of older adults in the metaverse. This underscores the importance of inclusive technology in addressing the needs of older travelers.

Emotional dialogue generation model of electronic commerce intelligent customer service based on topic expansion

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience. In this paper, we applied artificial intelligence techniques, notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network. The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.

Differences in emotional content, dream awareness, and dream control between lucid dreams and out-of-body experiences: Report analyses.

Differences in emotional content, dream awareness, and dream control between lucid dreams and out-of-body experiences: Report analyses.

Мedia Text: Communication Tactics for Building Trust

Using the activities example of journalist-podcaster, Kommersant media holding expert Daria Tsivina, the article examines communicative tactics for building trust as an media culture integral part. One of the main reasons for the article topic relevance is the transformation of the formation processes and trust manifestation in a journalist associated with the development of information and communication technologies and convergence processes. Media trust is a key concept in the overall assessment of the journalistic text quality. We show the relationship between the main trust elements, which is understood as the truthfulness of words, actions, emotions and motives. The article presents the empirical study results, the purpose of which is to identify journalistic communication tactics that are of particular importance in the media trust formation. Nonverbal communication – voice intonation – is the most important, along with verbal, means of communication and infl uence. The features of nonverbal communication compared to verbal communication are described.Leading qualities determined based on the study results are indicated, which include speech skills (speech technique), communication skills (speech logic, emotional content); persuasiveness, ability to present information; erudition. To achieve the study purpose, an online survey was conducted. The research process used the content analysis method, as well as lexical-semantic and morphological analysis. It is concluded that the podcast is an audio version of a written text. This is one of the main differences between the podcast format and other formats of modern media discourse, which allows us to explore the author’s speech techniques. It can be concluded that the Tsivina’s texts lexical features show that the restaurant critic views his own podcast as an effective means of communication with the audience. In general, the podcast “Restaurant Criticism with Daria Tsivina” is characterized by a clear, concise structure and the use of easy-to-understand morphological units. Prosody as a super segmental phonological phenomenon in context. This study suggests that the podcaster must take into account prosody as a super segmental phonological phenomenon that is critical to the trust formation. Keywords: podcast, media trust, prosody, intonation, convergence