Emotional Content Research Articles

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.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.

Эмотикон как пример развития графической эмотиологии в виртуальном пространстве

Emoticons are a vivid example of graphic emotionology in virtual communication. The interdisciplinary character of this research made it possible to combine linguistics with exact and natural sciences, including the young science of emotional artificial intelligence. Transmitting psycho-emotional experiences is important for effective virtual communication. Emotional pictography provides a more accurate understanding of the emotive content in a message. It relies on associative links between the image of a specific psycho-emotional state and its perception by a carrier of a specific linguistic culture. Basic emoticons are of universal nature, and internet users with different cultural and linguistic backgrounds tend to interpret them similarly. However, members of different ethno-linguistic communities sometimes decode the same emotions differently, which may lead to misunderstanding, as well as to serve as a marker on the friend – foe axis. Emoticons represent a clearly verified range of emotive states, which can be appealed to in virtual communication. The authors developed a classification of emoticons and proposed graphic emotionology as a new branch of linguistic semiotics. The metaphorical nature of the emoticon as a coded emotion with additional evaluative shades is revealed as a certain unified image or a complex of signs due to the accepted emotive norms of speech behavior. In addition, emoticons provide efficient compression during virtual communication. Intercultural virtual communication enriches the range of emoticons both linguistically and culturally, thus developing graphic emotionology internationally. Emoticons provide insight into the development of graphic emotionology.

The Role Mechanism of Social Preferences and Price Advantage in the Market Economy

In today's market economy environment, social preferences and price advantage, as two core driving forces, directly influence market operation and development trajectories. As consumer demand becomes increasingly diversified, social preferences intuitively reflect deep-seated cultural identity and value orientations, even guiding market trends. Price advantage, as the cornerstone of market competition, also impacts corporate production and development. These two elements intertwine: social preferences infuse emotional and cultural content into pricing strategies, while price advantage becomes an effective means to satisfy social preferences and achieve market penetration. A deep understanding and skillful application of these mechanisms are crucial for companies to navigate the complex and ever-changing market environment and achieve sustainable development.

Perceived prolonged stress leads to difficulties in recognizing sadness from voice cues in men but not women.

It has long been known that stress has detrimental effects on cognition (e.g., Alderson & Novack, 2002; Lupien & Lepage, 2001), most notably documented for memory functions (e.g., Schwabe & Wolf, 2013). Interestingly, less is known about the effects of stress on other cognitive functions including language processing. Here, we have examined the effects of self-reported prolonged stress on recognition of emotional language content with a particular emphasis on gender differences. We tested how well 399 participants with different perceived stress levels recognized emotional voice cues. Findings confirm previous results from the emotional prosody literature by demonstrating that women generally outperform men in the vocal emotion recognition task. Crucially, results also revealed that medium levels of perceived stress impair the ability to detect sadness from voice cues in men but not women. These findings were not modulated by task demands (e.g., speeded response) or better acoustic discrimination abilities in women. Results are in line with the idea that perceived stress has a different impact on men versus women and that women have a higher level of experience in voice sadness recognition, potentially due to their predominant role as primary caretakers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The impact of stimulus format and presentation order on social working memory updating.

Emotional faces and words have been extensively employed to examine cognitive emotional processing including social working memory, which plays a pivotal role in social interactions. However, it remains unclear which exact role these two stimulus formats play in updating specific emotional content, such as positive or negative information. Therefore, the current study examined the differences in working memory updating (WMU) of negative, neutral, and positive faces (Experiment 1) and words (Experiment 2), using a classic two-back paradigm with an event-related potential technique. In both experiments, emotional stimuli were presented in the same or different-valence order to further determine whether presentation order can also influence the WMU of specific emotional content. Our results showed that both stimulus format and presentation order play a role: (a) while faces showed an affective bias [larger P2 and late positive potential (LPP) for negative and positive faces than for neutral faces], words showed a negativity bias (larger LPP for negative words than both neutral and positive words); (b) While faces showed better performance with same-valence order, words showed better performance with different-valence order. Taken together, our findings indicate that, even if faces and words can contain the same emotional information, they impact social WMU differently.

Supplemental Material for Differences in Emotional Content, Dream Awareness, and Dream Control Between Lucid Dreams and Out-of-Body Experiences: Report Analyses

Supplemental Material for Differences in Emotional Content, Dream Awareness, and Dream Control Between Lucid Dreams and Out-of-Body Experiences: Report Analyses