Emotional Content Research Articles

A barking emotion recognition method based on Mamba and Synchrosqueezing Short-Time Fourier Transform

Dog barks are a crucial means for dogs to express their emotions, conveying needs and emotional states such as anger, sadness, or excitement. With the increasing number of pet dogs, understanding their emotional states is essential for better care and interaction. However, accurately interpreting the emotional content of dog barks is often challenging for humans. Therefore, developing a technology for recognizing the emotions in dog barks is of great importance. In this paper, we establish a dataset for dog bark emotion recognition and propose a method using the Synchrosqueezing Short-Time Fourier Transform (SST_STFT) to extract the time–frequency features of dog barks. Considering the characteristics of dog barks, we design an emotion recognition model based on Mamba. This model leverages the state-space model (Select SMM) for global modeling of long sequence features, enabling rapid and effective processing of time–frequency features and accurate perception of their variations. Experiments on the self-built DogEmotionSound dataset, as well as the public IEMOCAP and UrbanSound8K datasets, demonstrate that our model outperforms existing state-of-the-art sound recognition models across various evaluation metrics. The recognition accuracies on the three datasets are 91.97%, 95.36%, and 67.25%, respectively. The code is open-source at https://github.com/yangcjya/A-barking-emotion-recognition-method-based-on-Mamba.git.

Emotional text messages affect the early processing of emoticons depending on their emotional congruence: evidence from the N170 and EPN event related potentials.

Emoticons have been considered pragmatic cues that enhance emotional expressivity during computer-mediated communication. Yet, it is unclear how emoticons are processed in ambiguous text-based communication due to incongruences between the emoticon's emotional valence and its context. In this study, we investigated the electrophysiological correlates of contextual influence on the early emotional processing of emoticons, during an emotional congruence judgment task. Participants were instructed to judge the congruence between a text message expressing an emotional situation (positive or negative), and a subsequent emoticon expressing positive or negative emotions. We analyzed early event-related potentials elicited by emoticons related to face processing (N170) and emotional salience in visual perception processing (Early Posterior Negativity, EPN). Our results show that accuracy and Reaction Times depend on the interaction between the emotional valence of the context and the emoticon. Negative emoticons elicited a larger N170, suggesting that the emotional information of the emoticon is integrated at the early stages of the perceptual process. During emoticon processing, a valence effect was observed with enhanced EPN amplitudes in occipital areas for emoticons representing negative valences. Moreover, we observed a congruence effect in parieto-temporal sites within the same time-window, with larger amplitudes for the congruent condition. We conclude that, similar to face processing, emoticons are processed differently according to their emotional content and the context in which they are embedded. A congruent context might enhance the emotional salience of the emoticon (and therefore, its emotional expression) during the early stages of their processing.

الصحراء والغابة في قصيدة سيف الرحبي؛ ذاكرة الجفاف والرعب

The desert represents a profound textual space in the writings of the Omani poet, Saif Al Rahbi. His texts emanate broadly from an ancient Arabian memory laden with rich emotional and cultural contents, and, more specifically, from the land to which he belongs, the Arabian Peninsula, of which the desert is a significant component. The dominant presence of the desert in his poems is expressed through a glossary of terms that convey rigidity and harshness, on the one hand, and nostalgia, crumbling and passion, on the other hand. The imposing presence in Arabic literature of this Omani poet, with origins deeply rooted in the desert and its quick sands, is owed to his unique poetic style. Various linguistic formulations and visual contradictions, combined with the poet’s internal rhythm of emotions, create composites of personal visions that coalesce to furnish the text with endless structural, decorative, visual and mental possibilities, paving the way for a connection between the text and its reader. As for the forest, its cruel presence in the text is felt mainly through its various wild creatures. This study is an attempt to trace the image of the desert and the forest in Saif Al Rahbi’s poetry by identifying its distinct linguistic properties and artistic formulations.

Social cues tip the scales in emotional processing of complex pictures.

Perception of a picture is influenced by the social information and emotional value it carries for the viewer. There are still many unanswered questions about how social and emotional processing are related, but it is clear they involve overlapping brain areas and are cognitively interconnected. Yet studies of emotion processing using standardized picture datasets typically leave the social content in the pictures free to vary. In a few studies where the social content has been measured, it correlated with emotional arousal and valence. Here we tested the association between social and emotional content orthogonally, by selecting a similar number of pictures in four categories varying in presence of nonverbal social cues (e.g., gestures, facial expression, body language) and emotional content (neutral, negative). Across two studies, participants (N = 698 in total) provided three ratings for each picture: social relevance (defined as the self-reported use of social cues to understand the picture), valence, and arousal. Despite our best effort to balance the presence of social cues between negative and neutral pictures, ratings of social relevance were strongly associated with ratings of arousal and, to a lesser extent, with valence. These findings likely reflect the intertwined nature of social and emotional processing, which has implications for the neurobiology underlying them, how these systems develop, and how picture databases are used in research.

How do affective and cognitive involvement affect tourism intentions: a perspective on blogger impact

ABSTRACT Social Network Services (SNS) platforms are crucial sources of travel information. Xiaohongshu, a highly esteemed platform in China and globally, provides travel guides and lifestyle information. Empirical studies on how influencers, as well as the affective and rational factors of travel user-generated content (UGC) affect travel intentions are limited. Based on involvement theory and dual-system theory, this study uses topic modelling, sentiment analysis, and semantic network visualization to analyze travel posts and comments on Xiaohongshu. It reveals the affective and functional factors of posts by bloggers of varying impact and their effects on audience affective and cognitive involvement, processing systems, and travel intentions. The results are as follows: (1) Posts by medium-impact bloggers are more successful in generating and enhancing travel intentions. (2) Affective factors trigger affective involvement and processing, driving heuristic travel intentions; functional factors drive cognitive processing through cognitive involvement, leading to cognitive travel intentions. (3) The proper emotional intensity of affective and functional factors effectively transmits information, builds audience confidence, and maximises the enhancement of travel intentions. This study enriches the research viewpoint and theoretical foundation of enhancing travel intentions, providing practical recommendations for tourism practitioners in creating appropriate and effective emotional promotional content.

A theory-informed deep learning approach to extracting and characterizing substance use-related stigma in social media

BackgroundStigma surrounding substance use can result in severe consequences for physical and mental health. Identifying situations in which stigma occurs and characterizing its impact could be a critical step toward improving outcomes for individuals experiencing stigma. As part of a larger research project with the goal of informing the development of interventions for substance use disorder, this study leverages natural language processing methods and a theory-informed approach to identify and characterize manifestations of substance use stigma in social media data.MethodsWe harvested social media data, creating an annotated corpus of 2,214 Reddit posts from subreddits relating to substance use. We trained a set of binary classifiers; each classifier detected one of three stigma types: Internalized Stigma, Anticipated Stigma, and Enacted Stigma, from the Stigma Framework. We evaluated hybrid models that combine contextual embeddings with features derived from extant lexicons and handcrafted lexicons based on stigma theory, and assessed the performance of these models. Then, using the trained and evaluated classifiers, we performed a mixed-methods analysis to quantify the presence and type of stigma in a corpus of 161,448 unprocessed posts derived from subreddits relating to substance use.ResultsFor all stigma types, we identified hybrid models (RoBERTa combined with handcrafted stigma features) that significantly outperformed RoBERTa-only baselines. In the model’s predictions on our unseen data, we observed that Internalized Stigma was the most prevalent stigma type for alcohol and cannabis, but in the case of opioids, Anticipated Stigma was the most frequent. Feature analysis indicated that language conveying Internalized Stigma was predominantly characterized by emotional content, with a focus on shame, self-blame, and despair. In contrast, Enacted Stigma and Anticipated involved a complex interplay of emotional, social, and behavioral features.ConclusionOur main contributions are demonstrating a theory-based approach to extracting and comparing different types of stigma in a social media dataset, and employing patterns in word usage to explore and characterize its manifestations. The insights from this study highlight the need to consider the impacts of stigma differently by mechanism (internalized, anticipated, and enacted), and enhance our current understandings of how each stigma mechanism manifests within language in particular cognitive, emotional, social, and behavioral aspects.

Increased sensorimotor activity during categorisation of emotionally ambiguous faces.

Actions are rarely devoid of emotional content. Thus, a more complete picture of the neural mechanisms underlying the mental simulation of observed actions requires more research using emotion information. The present study used high-density electroencephalography to investigate mental simulation associated with facial emotion categorisation. Alpha-mu rhythm modulation was measured at each frequency, from 8Hz to 13 Hz, to infer the degree of sensorimotor simulation. Results suggest the sensitivity of the sensorimotor activity to emotional information, because (1) categorising static images of neutral faces as happy or sad was associated with stronger suppression in the central region than categorising clearly happy faces, (2) there was preliminary evidence indicating that the strongest suppression in the central region was in response to neutral faces, followed by sad and then happy faces and (3) in the control task, which required categorising images with the head oriented right, left, or forward as right or left, differences between conditions showed a pattern more indicative of task difficulty rather than sensorimotor engagement. Dissociable processing of emotional information in facial expressions and directionality information in head orientations was further captured in beta band activity (14-20 Hz). Stronger mu suppression to neutral faces indicates that sensorimotor simulation extends beyond crude motor mimicry. We propose that mu rhythm responses to facial expressions may serve as a biomarker for empathy circuit activation. Future research should investigate whether atypical or inconsistent mu rhythm responses to facial expressions indicate difficulties in understanding or sharing emotions.

Multilayer perceptron-based literature reading preferences predict anxiety and depression in university students.

This study aims to precisely model the nonlinear relationship between university students' literature reading preferences (LRP) and their levels of anxiety and depression using a multilayer perceptron (MLP) to identify reading-related risk factors affecting anxiety and depression among university students. In this cross-sectional study, an internet-based questionnaire was conducted among 2,092 undergraduate students (aged 18-22, 62.7% female, from seven provinces in China). Participants completed a customized questionnaire on their LRP, followed by standardized assessments of anxiety and depression using the Generalized Anxiety Disorder 7-item Scale and the Beck Depression Inventory, respectively. An MLP with residual connections was employed to establish the nonlinear relationship between LRP and anxiety and depression. The MLP model achieved an average accuracy of 86.8% for predicting non-anxious individuals and 81.4% for anxious individuals. In the case of depression, the model's accuracy was 90.1% for non-depressed individuals and 84.1% for those with depression. SHAP value analysis identified "Tense/Suspenseful-Emotional Tone," "War and Peace-Thematic Content," and "Infrequent Reading-Reading Habits" as the top contributors to anxiety prediction accuracy. Similarly, "Sad-Emotional Tone Preference," "Emotional Depictions-Thematic Content," and "Thought-Provoking-Emotional Tone" were the primary contributors to depression prediction accuracy. The MLP accurately models the nonlinear relationship between LRP and mental health in university students, indicating the significance of specific reading preferences as risk factors. The study underscores the importance of literature emotional tone and themes in mental health. LRP should be integrated into psychological assessments to help prevent anxiety and depression among university students.

Unpleasant words can affect the detection of morphosyntactic errors: An ERP study on individual differences.

In recent years, several ERP studies have investigated whether the early computation of agreement is permeable to the emotional content of words. Some studies have reported interactive effects of grammaticality and emotionality in the left anterior negativity (LAN) component, while others have failed to replicate these results. Furthermore, novel findings suggest that grammatical processing can elicit different neural patterns across individuals. In this study, we aim to investigate whether the interaction between grammaticality and emotionality is restricted to participants with a specific neural profile. Sixty-one female native speakers of Spanish performed an agreement judgment task in noun phrases composed of a determiner, a noun, and an unpleasant or neutral adjective that could agree or disagree in gender with the preceding noun. Our results support the existence of two different brain profiles: negative and positive dominance (individuals showing either larger LAN or larger P600 amplitudes in ungrammatical stimuli than in grammatical ones, respectively). Interestingly, the neural pattern of these two groups diverged at different points along the time course. Thus, the negative dominance group showed grammaticality effects as early as 200 ms, along with parallel and autonomous processing of grammaticality and emotionality at the LAN/N400 time window. Instead, for the positive dominance group an early interaction was found at around 200 ms, evidencing a grammaticality effect that emerged only for unpleasant words. Our findings confirm the role of individual differences in the interplay between grammar and emotion at the neural level and call for the inclusion of this perspective in studies on syntactic processing.

Examining the effectiveness of an education-based road safety intervention and the design and delivery mechanisms that promote road safety in young people

BackgroundRoad traffic collisions (RTCs) are the leading cause of deaths for young people worldwide. Whilst educational interventions that adopt fear-based messaging are commonly used to improve road safety in young people, limited focus has been directed to examining how they are delivered. Accordingly, this mixed-method study aims to i) measure the effectiveness of a UK intervention called Safe Drive Stay Alive (SDSA) for improving road safety knowledge, attitudes and intentional behaviors, and ii) what design and delivery mechanisms are important for achieving this. MethodsIn study one, participants completed online questionnaires that measured road safety knowledge, attitudes, and behavioural intentions pre- (n = 1304) and post-intervention (n = 407), and two months later (n = 72). In study two, focus groups were conducted with 10 young people post-intervention to understand what mechanisms were important for promoting road safety. ResultsStatistical analysis of questionnaires showed that road safety knowledge, attitudes, and intentional behaviours significantly improved post-intervention. However, descriptive comparisons of the smaller number of two-month follow-up questionnaires indicated that ratings returned to pre-intervention levels. Thematic analysis of focus groups highlighted that having emotive, realistic, and relatable content was important for encouraging young people to attend to the message. However, more focus was needed on how to address peer pressure. ConclusionSDSA improves road safety knowledge, attitudes, and behavioural intentions. However, there are indications that benefits may be short lived, requiring ‘top up’ interventions. Whilst young people also perceive SDSA to be useful, engaging, and delivering an important message, support is needed for addressing peer pressure.

Use of narratives to enhance childhood vaccine acceptance: Results of an online experiment among Canadian parents

ABSTRACT Identifying effective interventions to promote children’s vaccination acceptance is crucial for the health and wellbeing of communities. Many interventions can be implemented to increase parental awareness of the benefits of vaccination and positively influence their confidence in vaccines and vaccination services. One potential approach is using narratives as an intervention. This study aims to evaluate the effects of a narrative-based intervention on parents’ attitudes and vaccination intentions. In a pre-post experiment, 2,000 parents of young children recruited from an online pan-Canadian panel were randomly exposed to one of the three videos presenting narratives to promote childhood vaccination or a control condition video about the importance and benefits of physical activity in children. Pre-post measures reveal a relatively modest but positive impact of the narratives on parents’ attitudes and intention to vaccinate their child(ren). The results also suggest that narratives with more emotional content may be more effective in positively influencing vaccine attitudes than the more factual narrative. Using narratives to promote vaccination can positively influence parents’ views and intentions toward childhood vaccines, but research is still required to identify the best components of such interventions.

Service Users' Experiences of a Nationwide Digital Type 2 Diabetes Self-Management Intervention (Healthy Living): Qualitative Interview Study.

Diabetes Self-Management Education and Support programs for people living with type 2 diabetes mellitus (T2DM) can increase glycemic control and reduce the risk of developing T2DM-related complications. However, the recorded uptake of these programs is low. Digital self-management interventions have the potential to overcome barriers associated with attendance at face-to-face sessions. Healthy Living is an evidence-based digital self-management intervention for people living with T2DM, based on the Healthy Living for People with Type 2 Diabetes (HeLP-Diabetes) intervention, which demonstrated effectiveness in a randomized controlled trial. NHS England has commissioned Healthy Living for national rollout into routine care. Healthy Living consists of web-based structured education and Tools components to help service users self-manage their condition, including setting goals. However, key changes were implemented during the national rollout that contrasted with the trial, including a lack of facilitated access from a health care professional and the omission of a moderated online support forum. This qualitative study aims to explore service users' experiences of using Healthy Living early in the national rollout. A total of 19 participants were interviewed via telephone or a videoconferencing platform. Topics included users' experiences and views of website components, their understanding of the intervention content, and the overall acceptability of Healthy Living. Transcripts were analyzed thematically using a framework approach. Participants valued having trustworthy information that was easily accessible. The emotional management content resonated with the participants, prompting some to book an appointment with their general practitioners to discuss low mood. After completing the structured education, participants might have been encouraged to continue using the website if there was more interactivity (1) between the website and other resources and devices they were using for self-management, (2) with health professionals and services, and (3) with other people living with T2DM. There was consensus that the website was particularly useful for people who had been newly diagnosed with T2DM. Digital Diabetes Self-Management Education and Support programs offering emotional aspects of self-management are addressing an unmet need. Primary care practices could consider offering Healthy Living to people as soon as they are diagnosed with T2DM. Participants suggested ways in which Healthy Living could increase interaction with the website to promote continued long-term use.

The impact of high arousal and displeasure on online review helpfulness: Exploring the moderating role of product involvement

Online reviews help consumers make purchasing decisions on e-commerce platforms by addressing uncertainties in online shopping. In the context of information overload, the value of review helpfulness has become increasingly evident. This study aims to explore the effects of discrete emotions on helpfulness by categorizing Ekman’s six basic emotions based on arousal and valence, and extracting their probabilities from Amazon reviews using BERT (Bidirectional Encoder Representations from Transformers). The results indicate high arousal or negative emotions adversely influence review usefulness. Furthermore, anger, disgust, and fear, characterized by high arousal and displeasure, significantly affect the perceived helpfulness of the reviews. Additionally, we identify product involvement as a significant moderator, suggesting that consumers’ investment in products influences their interpretation of reviews’ emotional content. This study contributes to understanding the relationship between discrete emotions, product involvement, and review helpfulness in e-commerce, offering insights for online retailers and platform developers to enhance consumer decision-making.

Social media’s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation

Most existing methods for detecting fraudulent news or other forms of disinformation primarily rely on user profiling or content analysis, determining whether a given article aligns with a user’s stylistic or content-related preferences. This research departs from conventional approaches by concentrating not only on the characteristics of shared content but also on user interactions and the resultant topologies of content propagation trees. In our study, we have introduced a deep learning model based on Graph Convolutional Neural Networks (GCNN) and enhanced with multi-head attention mechanisms. This model leverages a wide range of psycholinguistic attributes extracted from users’ posts, including sentiment, emotional content, linguistic features, personality traits, readability, and communication style. We further enrich these attributes with BERT embeddings to improve textual representation. Our research framework involves creating two distinct graph networks: the user interaction graph and the semantic propagation graph. These graphical representations are essential for visualizing dynamic interactions and patterns of information dissemination among users. For each user, we generate a set of carefully crafted features that capture their unique characteristics and behaviors. These user-specific features are then integrated into the User Interaction Graph, enabling a more nuanced understanding and representation of user interactions within our proposed model. The proposed framework has demonstrated superior performance on benchmark datasets, achieving accuracies and precisions of 91.07% and 91.24% on the FakeNewsNet dataset, and 94.20% and 94.92% on the FibVid dataset, respectively. The results obtained from the experimental evaluation have also revealed the effect of various parameters significant to profile disinformers, signifying a substantial improvement in the field of fake news detection and profiling.

Unveiling tone manipulation in MD&A: Evidence from ChatGPT experiments

This research uses ChatGPT-3.5 and ChatGPT-4 to investigate tone manipulation in the management discussion and analysis of annual reports of Chinese-listed companies. We find that the quantification of emotional content in text using financial BERT model and dictionary approach is inconsistent due to two kinds of manipulation: "Expression Manipulation" and "Word Manipulation". Based on ChatGPT we verify the manipulative behavior of complex expressions and sentiment word substitutions. Our research suggests that using ChatGPT with appropriate cue words can help alleviate tone manipulation in financial texts, with GPT-4 having a better effect.

Neural correlates of approach–avoidance behavior in healthy subjects: Effects of low-frequency repetitive transcranial magnetic stimulation (rTMS) over the right dorsolateral prefrontal cortex

The dorsolateral prefrontal cortex (dlPFC) is implicated in top-down regulation of emotion, but the detailed network mechanisms require further elucidation. To investigate network-level functions of the dlPFC in emotion regulation, this study measured changes in task-based activation, resting-state and task-based functional connectivity (FC) patterns following suppression of dlPFC excitability by 1-Hz repetitive transcranial magnetic stimulation (rTMS). In a sham-controlled within-subject design, 1-Hz active or sham rTMS was applied to the right dlPFC of 19 healthy volunteers during two separate counterbalanced sessions. Following active and sham rTMS, functional magnetic resonance imaging (fMRI) was conducted in the resting state (rs-fMRI) and during approach–avoidance task responses to pictures with positive and negative emotional content (task-based fMRI). Activation and generalized psychophysiological interaction analyses were performed on task-based fMRI, and seed-based FC analysis was applied to rs-fMRI data. Task-based fMRI revealed greater and more lateralized activation in the right hemisphere during negative picture responses compared to positive picture responses. After active rTMS, greater activation was observed in the left middle prefrontal cortex compared to sham rTMS. Further, rTMS reduced response times and error rates in approach to positive pictures compared to negative pictures. Significant FC changes due to rTMS were observed predominantly in the frontoparietal network (FPN) and visual network (VN) during the task, and in the default mode network (DMN) and VN at rest. Suppression of right dlPFC activity by 1-Hz rTMS alters large-scale neural networks and modulates emotion, supporting potential applications for the treatment of mood disorders.

Words and Attitudes of the Heart: The Emotional Content of Christian Nationalist Communications

Christian nationalism has emerged as an important component of the relationship between religious identities and political attitudes. While several studies have analyzed the constellation of Christian nationalist elites and the effects of Christian nationalist orientations on public opinion, to date no study has explored how Christian nationalist elites message to the public or what effects these messages have. Moreover, the current literature lacks comparisons of Christian nationalism to other similar orientations. This study uses content analysis to compare the content and use of emotion language of Facebook messages of Christian nationalist, Christian Christian nationalism (CN) opposition, and patriotic groups. I find that these groups focus posts on issues that are stereotypical to the group identity, and that the use of emotion language differs by topic and group type. Additionally, groups’ use of emotion language shifts the emotional responses of readers, especially in Christian nationalist groups. This study adds to our understanding of the role of emotion in social media communications and the effects of social media communications on readers.

A study in scarlet: cultural memory of the tropes related to the color red, female countenance, and onstage makeup in the Sinophone world

Abstract In a high-context culture like Chinese, the linguistic code encompasses only part of the message and is incomplete without context. One of the implicit codes embedded in high-context communication is color tropes. Highly recognizable in the Sinophone world, color tropes often manifest themselves in the forms of metaphor, metonymy, allusions, and similes and are often related to the conveyance of emotional content. This paper provides a selection and discussion of such color tropes, demonstrates that color in a language inspires associations and connotations that are often very subtle and determined by deep cultural memory, and, giving several Chinese examples, shows how the primary semasiology of a color term often influences its subsequent semantic extension.

Speech emotion recognition based on multi-feature speed rate and LSTM

Correctly recognizing speech emotions is of significant importance in various fields, such as healthcare and human–computer interaction (HCI). However, the complexity of speech signal features poses challenges for speech emotion recognition. This study introduces a novel multi-feature method for speech emotion recognition that combines short-and rhythmic features. Utilizing short-time energy, zero-crossing rate, and average amplitude difference, the proposed approach effectively addressed overfitting concerns by reducing feature dimensionality. Employing an (LSTM) network, the experiment achieved notable accuracy across diverse datasets. Specifically, the proposed method achieved an impressive accuracy of up to 98.47% on the CASIA dataset, 100% on the Emo-DB dataset, and 98.87% on the EMOVO dataset, demonstrating its capability to accurately discern speaker emotions across different languages and emotion classes. These findings underscore the significance of incorporating speech rate for emotional content recognition, which holds promise for application in HCI and auxiliary medical diagnostics.

The Impact of Audio Voice Characteristics on Audience Positive Emotions in Podcasting Analysis

In this study, the researchers directed their attention towards the phenomenon of sound. Specifically, they sought to examine the emotional responses of the participants by analyzing electrodermal responses (EDRs). The study involved the comparison of two distinct sound conditions. Initially, it was observed that there was no discernible variation in the Electrodermal Response (EDR) pattern across different conditions. However, upon careful examination of the peak EDR outputs observed in various simulations, it became evident that the emotional content had a discernible impact on the spatial conditions. Furthermore, it was particularly noteworthy that the interaction between sound and the aforementioned spatial conditions played a significant role. It was observed that no distinct individuals stimulators could be identified, although there were notable trends and variations in Emotional Dysregulation Rating (EDR) under specific circumstances. In addition, it is worth noting that the participants in the study recognized the most prevalent instances of positive emotions across all experimental conditions. Furthermore, the utilization of a combination of arousal sound design and 3D sound mix elicited the greatest number of emotional responses. Conversely, the omission of data regarding participants' educational level, cultural background, and gender prevents a comprehensive exploration of these factors, thereby limiting the potential for generating more nuanced insights.