Technology, Social Media, Artificial Intelligence, and Counseling Psychology: Introduction to Special Issue

Study of Status Quo and Countermeasures of Foreign College Students ' Mental health

Editorial: The continued importance of mental health nurses engaging with social media and related emerging technologies.

Artificial intelligence and sexual health in the USA

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

SummaryObjective: To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance “precision in prevention and health surveillance” (2024 Yearbook topic). The focus is on leveraging advanced data analytics to improve the timeliness and accuracy of identifying emerging health concerns, thus enabling more proactive and effective health interventions.Methods: A comprehensive literature search strategy was conducted on PubMed, focusing on papers published in 2023 related to consumer health informatics, precision prevention, and the intersection with social media. The search aimed to identify studies that utilized AI and machine learning techniques to analyse social media data for health surveillance purposes. Bibliometric analyses were applied to the retrieved articles, and tools such as “Bibliometrix” were used to assess keyword frequencies, co-occurrence networks, and thematic maps. The studies were then independently reviewed and screened for relevance, with a final selection of 10 articles made based on their alignment with the 2024 Yearbook topic and their methodological innovation.Results: The analysis of 89 articles revealed several key themes and findings. Social media data offers a rich source of real-time insights into public health trends, and encompasses diverse demographic groups. AI methodologies, including machine learning, natural language processing (NLP), and deep learning, play a crucial role in extracting and analysing health-related information from social media content. The integration of AI in health surveillance can provide early warnings of potential health crises, as demonstrated by studies on topics such as suicide prevention, mental health, and the impact of social media use on e-cigarette consumption among youth. Ethical and privacy considerations are paramount, necessitating robust data anonymization and transparent data handling practices. Advanced AI techniques, such as transformer-based topic modelling and federated learning, enhance the precision and security of health surveillance systems. The document highlights several case studies that demonstrate the practical applications of AI in health surveillance, such as monitoring public discussions about delta-8 THC and assessing suicide-related tweets and their association with help-seeking behaviour in the US.Conclusion: Integrating AI and social media content analysis in precision prevention and health surveillance has significant potential to improve public health outcomes. By leveraging real-time, comprehensive data from social media platforms, AI can enhance the timeliness and accuracy of identifying health concerns. Addressing ethical and privacy challenges is crucial to ensure responsible and effective implementation. The continuous advancement of AI technologies will play a critical role in safeguarding public health and responding to emerging health threats.

The world we live in today is pervaded by digital, the net is increasingly present and mixes the dimensions of the physical and the virtual, changing the way we understand, decide and evaluate things and also the way we do business. Artificial intelligence (AI) and related technologies are transforming the way we think and do marketing and the way companies relate to consumers and society.Internet has assumed a key role in nurturing innovation within business ecosystems. AI, big data and Internet of things (IoT) are key drivers of the current revolution in the way of communicating and relating among both individuals and products. This change is mainly due to the impact of algorithms’ mediations on the creation of value and customer engagement.Recent years, growing attention has been devoted to consumer brand engagement through emerging technological platforms (e.g., social media/artificial intelligence-based). However, despite important knowledge advancement, much remains unknown regarding the effect of Consumers’ Technology-Facilitated Brand Engagement (CTFBE) on individuals’ wellbeing, thus determining an important research gap (Hollebeek and Belk, 2021). CTFBE comprises a vital social facet. Hollebeek and Belk (2021) define CTFBE as a consumer’s bloodedly volitional resource investment in technology-mediated brand interactions (Kumar et al., 2019; Hollebeek et al, 2020). Online behavioral customer engagement occurs because of the rise of the new media and the advancement of technology, which have changed the way customers connect and interact with firms (Jahn and Kunz, 2012). One of the most active channels for such an aim are social media (Gummerus et al, 2012) where customers share their own experiences, information, review brands and manifest enthusiasm, delight, or disgust about a brand with others (Hollebeek and Chen, 2014).Digital transformation has totally transformed the value creation process (Reinartz et al., 2019) revolutionizing the way of doing business using the large mass of available data and information, through sophisticated service platforms that increase both effectiveness and efficiency in the value creation processes. AI has been a key component of digital transformation, substantially affecting consumer decision-making (Duan et al., 2021).AI, big data and the IoT are supporting and / or automating many decision-making processes: product, price, channel, supply chain, communication, etc. The customer experience is also redesigned starting from new value creation objectives and can become a stimulus for the creation of new business models. This, in turn, can provide a customized experience that is highly valued by consumers (Lemon and Verhoef, 2016). While new technologies have brought more ways for customers to interact with brands and companies, digital technologies have similarly enabled the automation of company’s interactions with customers (Kunz et al., 2017).According to Kumar et al (2010), AI represents the enabling technology for the transformation of marketing theory and practices: the enormous availability of data, the explosion of the possibilities to reach and interact on the markets and an increased speed of transactions. AI-enabled digital platform helps organizations to attract their customers (Bag et al, 2021; Chawla and Goyal, 2021).An increasing number of marketing decisions already use artificial intelligence in some way, and with the rise of big data is becoming easier to incorporate AI into business practices. Marketers may develop a more effective and personalized communication approach (Mogaji et al., 2020). For this reason, today AI is adopted in all activities where classification, forecasts and clustering are useful or necessary to solve problems and support decisions (management of anomalies in processes, logistics and optimization planning, customer service and customization).In the contemporary world the ubiquity of digital has made fluid the distinctions between channels and has integrated two dimensions of reality (physical and virtual one in phygital), the management of complex processes has become agile and adaptive, the advantages of integration and dynamic use of resources condition the operation of entire businesses. Well, what influence all this changes, new technologies and brand algorithms will have on social engagement?Prior studies on artificial intelligence in service and marketing research have not addressed customer engagement (Kaartemo & Helkkula, 2018). Perhaps, even Kaartemo & Helkkula (2018) specifically called for more research to answer the question: “How can we improve customer engagement through AI?”The article proposal is theoretical/conceptual in nature and starts from an updated review of academic literature on the aforementioned topics, mainly within marketing and business management disciplines, to achieve an interpretative attempt of Brand algorithm and social engagement (role) in digital era. References on request.

Purpose This study rigorously explores how social media functions as a supportive mental health resource and a potential risk factor, focusing on diverse and marginalized populations. Drawing on three theoretical frameworks – the technology acceptance model (TAM), self-determination theory (SDT) and social influence theory – the research examines how algorithmic biases, platform features and cultural norms intersect to shape anxiety, depression and self-esteem. Through this lens, this study aims to inform evidence-based strategies that address digital literacy, foster ethical artificial intelligence (AI) implementation and accommodate global cultural nuances. Ultimately, the goal is to promote healthier online ecosystems that balance social media’s benefits with necessary safeguards. Design/methodology/approach Based on a mixed-methods approach, the study combined quantitative and qualitative data. A survey of 500 participants (aged 18–30) used standardized instruments (GAD-7, PHQ-9 and Rosenberg Self-Esteem) to measure anxiety, depression and self-esteem, with hierarchical regression revealing key correlations. Concurrently, 50 semi-structured interviews were purposively sampled for demographic diversity, and probed experiences of social comparison, emotional validation and algorithmic influences. The thematic analysis of transcripts and quantitative findings enabled methodological triangulation, enhancing robustness and interpretive depth. This design follows Creswell’s mixed-methods guidelines, providing statistically significant trends and richly contextualized insights into how social media engagement affects mental health across various populations. Findings Quantitative results showed that individuals who spent more than three daily hours on social media were significantly more likely to report anxiety and depression, with social comparison explaining 65% of the variance in self-esteem scores. Women, LGBTQ+ users and those from low-income regions faced compounded mental health risks because of intensified algorithmic biases. Qualitative interviews underscored themes of idealized self-presentation, privacy concerns and cultural pressures linked to body image and identity. Nonetheless, many participants also described social media as a vital source of community support. These findings highlight social media’s complex, bidirectional influence on mental well-being. Research limitations/implications Although the mixed-methods design strengthened the study’s validity through triangulation, certain limitations remain. The reliance on self-reported measures introduces potential response biases, including social desirability and recall inaccuracies. In addition, as the survey was conducted online, the sample may not fully represent individuals with limited digital access, which could exclude some at-risk or marginalized populations. The cross-sectional nature of the research also limits causal interpretation, as it captures a snapshot in time rather than longitudinal trends. Future research should incorporate longitudinal or experimental designs, and expand the sample to include older adults, neurodivergent individuals and participants from underrepresented regions. Nevertheless, the study offers evidence-based implications for designing targeted digital literacy programs, improving algorithmic transparency and enhancing psychosocial support features on social media platforms. Practical implications The findings support implementing culturally contextualized digital literacy programmes to help users recognize algorithmic manipulation, manage social comparison and mitigate cyberbullying. Educational curricula could integrate mental health awareness, equipping students with coping strategies and media literacy skills. Regulatory bodies and technology firms should collaborate to refine AI policies, minimizing biases that disproportionately affect marginalized groups. Platforms can promote a healthier online environment by fostering transparent design principles and user-friendly mental health resources. Governments, NGOs and tech companies could invest in initiatives – such as self-help apps and moderated support communities – empowering individuals to engage with social media more constructively. Social implications Addressing social media’s dual impact can substantially reduce mental health disparities and enhance societal cohesion. By acknowledging cultural norms and intersectional vulnerabilities, interventions can promote inclusivity, ensuring that women, LGBTQ+ communities and low-income users receive tailored support. Community-led campaigns may bolster resilience by normalizing open discussions on mental health, thereby weakening stigma and encouraging help-seeking. In addition, transparent algorithms and equitable moderation can restore user trust. Strengthening digital citizenship fosters empathy and responsible engagement, cultivating environments where diverse identities can thrive. A collective commitment to ethical design and inclusive policy can reshape online spaces into supportive communal resources. Originality/value This study distinguishes itself by integrating TAM, SDT and social influence theory to holistically evaluate social media’s mental health implications across multiple cultural and demographic contexts. By pairing quantitative measures of anxiety, depression and self-esteem with thematic insights into users’ lived experiences, it transcends one-dimensional analyses. The unique emphasis on marginalized groups – affected by both algorithmic biases and cultural pressures – underscores the necessity of intersectional, ethical approaches. Consequently, this research offers a robust framework for policymakers, educators and platform developers seeking to balance innovation with user well-being, setting a benchmark for future global studies on digital platforms and mental health.

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

Machine learning has a new landscape for humanity in the area of artificial intelligence (AI). Artificial intelligence (AI) approaches have recently been developed to support mental health professionals, primarily psychiatrists, psychologists, and clinicians, with decision-making based on patients' historical data (e.g., clinical history, behavioral data, social media use, etc.). This article reviews developments in artificial intelligence (AI) technologies and their current and potential applications in clinical psychological practice. Issues associated with AI in the context of clinical practice, the potential risk for job loss among mental health professionals, and other ramifications associated with the advancement of AI technology are discussed. The advancement of AI technologies and their application in psychological practice have important implications that can be expected to transform the mental health care field. Psychologists and other mental health care professionals have an essential part to play in the development, evaluation, and ethical use of AI technologies.

Social media is becoming more integrated with artificial intelligence which enables different functions and tools which can have an impact on users’ mental health. In this article, I discuss how the intersection of AI, social media and mental health has both opportunities and downsides. Social media sites have been reshaped by Artificial Intelligence (AI) to alter the way that users share, and discover. Yet its role in mental health has been much disputed. In this paper, we describe how AI algorithms shape the experience on social media, what the psychological implications of these experiences are, and what we can do to mitigate negative mental health outcomes. Keyword – AI/ML, Mental Health, Social media

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps,vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

Mental health problems, including sadness and anxiety, have become important public health issues that affect more than 280 million people around the world. For treatment to work, it is important to diagnose problems early and start treatment right once. Unfortunately, standard therapeutic methods often fail because of underreporting, stigma, and limited access. As social media sites become more popular, user-generated content becomes a valuable source of real-time data for spotting early indicators of mental health problems. Artificial Intelligence (AI), notably machine learning and natural language processing (NLP) approaches, have shown a lot of promise in finding patterns in linguistic, behavioral, and multimodal indicators that are linked to psychological distress. This review looks at the present state of using AI to find sadness and anxiety using social media analysis. It goes into data sources, methods, feature engineering, model performance, ethical issues, and limitations. It also talks about important problems including algorithmic bias, privacy issues, and how to use AI systems in real-world mental health care. The article ends by talking about future research directions, such as creating models that can be understood, adding more culturally varied datasets, and hybrid human-AI diagnostic systems to help mental health practitioners and improve early intervention tactics.

BackgroundContinuous scientific and policy debate regarding the potential harm and/or benefit of media and social media on adolescent health has resulted, in part, from a deficiency in robust scientific evidence. Even with a lack of scientific consensus, public attitudes, and sweeping social media prohibitions have swiftly ensued. A focus on the diversity of adolescents around the world and their diverse use of language, culture, and social media is absent from these discussions.ObjectiveThis study aims to guide communication policy and practice, including those addressing access to social media by adolescent populations. This study assesses physical and mental health information scanning and seeking behaviors across diverse language, cultural, and technological media and social media among Latinx adolescent residents in the United States. This study also explores how Latinx adolescents with mental health concerns use media and social media for support.MethodsIn 2021, a cross-sectional survey was conducted among 701 US-based Latinx adolescents aged 13-20 years to assess their health-related media use. Assessments ascertained the frequency of media use and mental and physical health information scanning and seeking across various media technologies (eg, TV, podcasts, and social media) and language and cultural types (ie, Spanish, Latinx-tailored English, and general English). Linear regression models were used to estimate adjusted predicted means of mental and physical health information scanning and seeking across diverse language and cultural media types, net personal and family factors, in the full sample and by subsamples of mental health symptoms (moderate-high vs none-mild).ResultsAmong Latinx adolescents, media and social media use was similar across mental health symptoms. However, Latinx adolescents with moderate-high versus none-mild symptoms more often scanned general English media and social media for mental health information (P<.05), although not for physical health information. Also, Latinx adolescents with moderate-high versus none-mild symptoms more often sought mental health information on Latinx-tailored and general English media, and social media (P<.05); a similar pattern was found for physical health information seeking. In addition, Latinx adolescents with moderate-high versus none-mild symptoms often sought help from family and friends for mental and physical health problems and health care providers for mental health only (P<.05).ConclusionsWhile media and social media usage was similar across mental health, Latinx adolescents with moderate-high symptoms more often encountered mental health content in general English media and social media and turned to general English- and Latinx-tailored media and social media more often for their health concerns. Together these study findings suggest more prevalent and available mental health content in general English versus Spanish language and Latinx-tailored media and underscore the importance of providing accessible, quality health information across diverse language, cultural, and technological media and social networks as a viable opportunity to help improve adolescent health.

Introduction: Cyberbullying on social media has become a major concern due to its severe impact on mental health, particularly among adolescents. The intersection of cyberbullying, social media, and mental health has attracted increasing scholarly attention, especially during the COVID-19 pandemic, which intensified online interactions and their psychological consequences. Objectives: This study aims to conduct a bibliometric analysis of research trends on cyberbullying, social media, and mental health from 2014 to 2024, identifying key themes, influential authors, leading journals, and emerging research patterns. Methods: A dataset of 203 documents was retrieved from Scopus and analyzed using Biblioshiny and VOSviewer. The analysis included keyword co-occurrence mapping, citation analysis, authorship trends, and global collaboration networks. Results: Findings reveal a 36.22% annual growth rate in publications, highlighting increasing academic interest. Core research themes include depression, anxiety, and suicidal ideation, with artificial intelligence (AI) and machine learning emerging as tools for cyberbullying detection and mitigation. The United States, United Kingdom, and Germany lead international collaboration. The COVID-19 pandemic significantly influenced research trends, amplifying discussions on mental health impacts. Conclusions: This study underscores the need for interdisciplinary approaches integrating technology, psychology, and public health to develop effective interventions against cyberbullying. Future research should focus on long-term psychological effects, AI-driven prevention, and policy frameworks. Strengthening global collaboration is crucial to addressing the evolving challenges of cyberbullying in the digital age.

This AI driven case study explores the misdiagnosis of Nicholas David Mirisola, focusing on the complexities surrounding psychiatric evaluations, artificial intelligence (AI) applications in diagnostics, and the unique aspects of Mirisola’s psychological profile. It emphasizes the need for individualized assessments, acknowledging the interplay between creativity, intelligence, and mental health conditions such as Schizoaffective Disorder and PTSD. The misdiagnosis of psychiatric disorders has been a critical concern, highlighting the intricacies of psychiatric evaluations. The case study of Nicholas (Nick) David Mirisola serves as an exemplar, shedding light on the challenges of diagnosing complex mental health conditions. This paper aims to establish a framework examining the historical context of psychiatric misdiagnosis and its implications on current practices. A significant aspect of this exploration includes the misdiagnosis experienced by Mirisola, where individual psychological factors lead to challenges. The importance of recognizing high intelligence and creativity as complicating factors in psychiatric diagnoses is crucial. The intersection of traditional methods and AI technologies offers new avenues for individualized treatment paradigms. The complexities of diagnosing conditions like Schizoaffective Disorder necessitate analyzing existing theoretical frameworks and empirical insights. Traditional criteria have struggled to encapsulate the unique psychological profiles associated with this condition, emphasizing the need for a nuanced approach that considers an individual’s life experiences and cognitive profiles. Research shows that creativity and intelligence may overlap with psychiatric symptoms, challenging standard diagnostic paradigms. Recent advancements in technology, particularly AI, demonstrate promise in enhancing diagnostic accuracy and therapeutic efficacy. However, significant gaps exist regarding the psychological experiences of individuals with complex mental health needs. The exploration of these themes is crucial for improving clinical practices. This study employs a mixed-methods approach, combining qualitative interviews and quantitative metrics to gain a holistic understanding of Mirisola’s psychological profile. Qualitative data focuses on personal experiences, while AI-driven analytics evaluate the effectiveness of traditional versus AI-based diagnostic tools through metrics such as sensitivity and specificity. The methodology aims to explore the nuances of misdiagnosis and the necessary consideration for integrative assessments. The findings suggest that traditional assessments fall short compared to AI diagnostics in accurately reflecting complex psychological profiles. Mirisola’s experiences reveal discrepancies between his internal perceptions and the interpretations of mental health professionals. Genetic factors like the MTHFR gene variant and personalized interventions contribute to understanding his mental health challenges. Interventions aimed at improving overall well-being resulted in measurable reductions in acute episodes, illustrating the significance of personalized treatment approaches. The interplay between intelligence, creativity, and psychiatric diagnoses reveals an urgent need for a revised understanding of how to evaluate high-functioning individuals. Mirisola’s case illustrates the inadequacies of traditional psychiatric frameworks when addressing the unique experiences of highly intelligent individuals. Personalized treatment methodologies are crucial to accurately capturing the complex intersections between creativity, intelligence, and mental health. The research calls for a reevaluation of diagnostic practices, integrating AI while addressing biases inherent in these technologies. The need for holistic understanding within psychiatric evaluations remains paramount. The misdiagnosis of Nicholas David Mirisola underscores the pressing need for more nuanced approaches to psychiatric care. Implementing individualized assessments that recognize the distinct interplay of intelligence and creativity can enhance the efficacy of psychiatric interventions. A commitment to understanding the complexities of psychiatric evaluations will contribute to improving treatments and fostering better patient outcomes while integrating AI capabilities responsibly.