A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition

Mental illness has grown to become a prevalent and global health concern that affects individuals across various demographics. Timely detection and accurate diagnosis of mental disorders are crucial for effective treatment and support as late diagnosis could result in suicidal, harmful behaviors and ultimately death. To this end, the present study introduces a novel pipeline for the analysis of facial expressions, leveraging both the AffectNet and 2013 Facial Emotion Recognition (FER) datasets. Consequently, this research goes beyond traditional diagnostic methods by contributing a system capable of generating a comprehensive mental disorder dataset and concurrently predicting mental disorders based on facial emotional cues. Particularly, we introduce a hybrid architecture for mental disorder detection leveraging the state-of-the-art object detection algorithm, YOLOv8 to detect and classify visual cues associated with specific mental disorders. To achieve accurate predictions, an integrated learning architecture based on the fusion of Convolution Neural Networks (CNNs) and Visual Transformer (ViT) models is developed to form an ensemble classifier that predicts the presence of mental illness (e.g., depression, anxiety, and other mental disorder). The overall accuracy is improved to about 81% using the proposed ensemble technique. To ensure transparency and interpretability, we integrate techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and saliency maps to highlight the regions in the input image that significantly contribute to the model's predictions thus providing healthcare professionals with a clear understanding of the features influencing the system's decisions thereby enhancing trust and more informed diagnostic process.

Prospective associations of text-message-based sentiment with symptoms of depression, generalized anxiety, and social anxiety

OBJECTIVE

Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in vivo text messaging language and differential symptoms of depression, generalized anxiety, and social anxiety.

METHODS

Over 16 weeks, we collected outgoing text messages from 335 adults. Using Linguistic Inquiry and Word Count (LIWC), NRC Emotion Lexicon, and previously established depression and stress dictionaries, we evaluated the degree to which language features predict symptoms of depression, generalized anxiety, or social anxiety the following week using hierarchical linear models. To isolate the specificity of language effects, we also controlled for the effects of the two other symptom types.

RESULTS

We found significant relationships of language features, including personal pronouns, negative emotion, cognitive and biological processes, and informal language, with common mental health conditions, including depression, generalized anxiety, and social anxiety (ps < .05). There was substantial overlap between language features and the three mental health outcomes. However, after controlling for other symptoms in the models, depressive symptoms were uniquely negatively associated with language about anticipation, trust, social processes, and affiliation (βs: -.10 to -.09, ps < .05), whereas generalized anxiety symptoms were positively linked with these same language features (βs: .12-.13, ps < .001). Social anxiety symptoms were uniquely associated with anger, sexual language, and swearing (βs: .12-.13, ps < .05).

CONCLUSION

Language that confers both common (e.g., personal pronouns and negative emotion) and specific (e.g., affiliation, anticipation, trust, and anger) risk for affective disorders is perceptible in prior week text messages, holding promise for understanding cognitive-behavioral mechanisms and tailoring digital interventions.

Linguistic predictors from Facebook postings of substance use disorder treatment retention versus discontinuation

Background: Early indicators of who will remain in - or leave - treatment for substance use disorder (SUD) can drive targeted interventions to support long-term recovery.Objectives: To conduct a comprehensive study of linguistic markers of SUD treatment outcomes, the current study integrated features produced by machine learning models known to have social-psychology relevance.Methods: We extracted and analyzed linguistic features from participants' Facebook posts (N = 206, 39.32% female; 55,415 postings) over the two years before they entered a SUD treatment program. Exploratory features produced by both Linguistic Inquiry and Word Count (LIWC) and Latent Dirichlet Allocation (LDA) topic modeling and the features from theoretical domains of religiosity, affect, and temporal orientation via established AI-based linguistic models were utilized.Results: Patients who stayed in the SUD treatment for over 90 days used more words associated with religion, positive emotions, family, affiliations, and the present, and used more first-person singular pronouns (Cohen's d values: [-0.39, -0.57]). Patients who discontinued their treatment before 90 days discussed more diverse topics, focused on the past, and used more articles (Cohen's d values: [0.44, 0.57]). All ps < .05 with Benjamini-Hochberg False Discovery Rate correction.Conclusions: We confirmed the literature on protective and risk social-psychological factors linking to SUD treatment in language analysis, showing that Facebook language before treatment entry could be used to identify the markers of SUD treatment outcomes. This reflects the importance of taking these linguistic features and markers into consideration when designing and recommending SUD treatment plans.

Tracking group identity through natural language within groups

To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a-1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students' likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates-Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members' joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.

The Potential of Psychedelics for End of Life and Palliative Care

End of life and palliative care has improved in recent decades but the psychopharmacological options available to clinicians and patients in these contexts remain limited. In particular, psychological factors such as depression, existential distress, and well-being remain challenging to address with current medications. Here, we review recent research on the use of psychedelics in clinical settings with a particular focus on patients with life-threatening diagnoses. We propose that psychedelics may provide clinicians with an additional psychopharmacological treatment in the context of end of life and palliative care.

Perspectives of ESCAPE-Pain Programme for Older People With Knee Osteoarthritis in the Community Setting

Background: Functional limitationscommonly affect patients with knee osteoarthritis (OA) which reduces quality of life. The Enabling Self-management and Coping with Arthritic Pain using Exercise (ESCAPE-pain) is an evidence-based programme identified to be suitable for adaptation for the Malaysian health care system. It is important to understand the acceptance from a sociocultural context of the ESCAPE-pain programme from the perspectives of patients with knee OA and healthcare professionals. This qualitative study aims to explore the perspectives of stakeholders to inform the adaptation of the ESCAPE-pain programme into the Malaysian health care system. Method: Semi-structured interviews using interview guides were conducted with 18 patients with knee OA and 14 healthcare professionals including nurses, physiotherapists, occupational therapists, medical doctors, and orthopedic surgeons. The data were transcribed and analyzed using framework analysis. Results: The findings show that patients and healthcare professionals positively accept the programme into their daily living activities and recommend some modifications related to the Malaysian context. This study also highlights strategies to adopt when providing ESCAPE-pain to patients with knee OA. Conclusion: The findings reveal how sociocultural considerations could facilitate uptake and engagement with the ESCAPE-pain programme for home exercise among patients with knee osteoarthritis. These findings may benefit t patients with knee OA in the Malaysian healthcare system, although future research is recommended.

The Relationship between Psycholinguistic Features of Religious Words and Core Dimensions of Religiosity: A Survey Study with Japanese Participants

Previous studies have reported that religious words and religiosity affect mental processes and behaviors. However, it is unclear what psycholinguistic features of religious words (e.g., familiarity, imageability, and emotional aspects) are associated with each dimension of personal religiosity (intellect, ideology, public practice, private practice, and experience). The purpose of this study was to examine whether and how the above-mentioned psycholinguistic features of religious words correlate with each of the core dimensions of religiosity. Japanese participants evaluated four psycholinguistic features of twelve religious words using a 5-point Semantic Differential scale for familiarity and imageability and a 9-point Self-Assessment Manikin (SAM) scale for emotional valence and emotional arousal. The participants also rated their own religiosity using the Japanese version of the Centrality of Religiosity Scale (JCRS). The results of the study revealed that (1) the scales measuring the psycholinguistic features of religious words were statistically reliable; (2) the JCRS was reliable; (3) the familiarity, emotional valence, and emotional arousal of religious words and each mean dimensional score of the JCRS score correlated positively with each other; and (4) highly religious people had higher familiarity and higher emotional arousal to religious words than non-religious people, whereas highly religious people had higher emotional valence to religious words in comparison with non-religious and religious people. In addition, religious people had higher familiarity to religious words than non-religious people. Taken together, these findings suggest that psycholinguistic features of religious words contribute to the detection of religiosity.

The language of character strengths: Predicting morally valued traits on social media

OBJECTIVE

Social media is increasingly being used to study psychological constructs. This study is the first to use Twitter language to investigate the 24 Values in Action Inventory of Character Strengths, which have been shown to predict important life domains such as well-being.

METHOD

We use both a top-down closed-vocabulary (Linguistic Inquiry and Word Count) and a data-driven open-vocabulary (Differential Language Analysis) approach to analyze 3,937,768 tweets from 4,423 participants (64.3% female), who answered a 240-item survey on character strengths.

RESULTS

We present the language profiles of (a) a global positivity factor accounting for 36% of the variances in the strengths, and (b) each of the 24 individual strengths, for which we find largely face-valid language associations. Machine learning models trained on language data to predict character strengths reach out-of-sample prediction accuracies comparable to previous work on personality (r_median = 0.28, ranging from 0.13 to 0.51).

CONCLUSIONS

The findings suggest that Twitter can be used to characterize and predict character strengths. This technique could be used to measure the character strengths of large populations unobtrusively and cost-effectively.

Christians and Buddhists Are Comparably Happy on Twitter: A Large-Scale Linguistic Analysis of Religious Differences in Social, Cognitive, and Emotional Tendencies

Are different religions associated with different social, cognitive, and emotional tendencies? Although major world religions are known to encourage social interactions and help regulate emotions, it is less clear to what extent adherents of various religions differ in these dimensions in daily life. We thus carried out a large-scale sociolinguistic analysis of social media messages of Christians and Buddhists living in the United States. After controlling for age and gender effects on linguistic patterns, we found that Christians used more social words and fewer cognitive words than Buddhists. Moreover, adherents of both religions, similarly used more positive than negative emotion words on Twitter, although overall, Christians were slightly more positive in verbal emotional expression than Buddhists. These sociolinguistic patterns of actual rather than ideal behaviors were also paralleled by language used in the popular sacred texts of Christianity and Buddhism, with the exception that Christian texts contained more negative and fewer positive emotion words than Buddhist texts. Taken together, our results suggest that the direct or indirect influence of religious texts on the receivers of their messages may partially, but not fully, account for the verbal behavior of religious adherents.

The Future of Technology in Positive Psychology: Methodological Advances in the Science of Well-Being

Advances in biotechnology and information technology are poised to transform well-being research. This article reviews the technologies that we predict will have the most impact on both measurement and intervention in the field of positive psychology over the next decade. These technologies include: psychopharmacology, non-invasive brain stimulation, virtual reality environments, and big-data methods for large-scale multivariate analysis. Some particularly relevant potential costs and benefits to individual and collective well-being are considered for each technology as well as ethical considerations. As these technologies may substantially enhance the capacity of psychologists to intervene on and measure well-being, now is the time to discuss the potential promise and pitfalls of these technologies.