There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like depression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.

ChatGPT on App Store displayed on a phone screen on 07 June 2025. ChatGPT on App Store displayed on a phone screen on 07 June 2025. Has OpenAI really made ChatGPT better for users with mental health problems? Prompts indicating suicidal ideation got alarming replies, which experts say shows'how easy it is to break the model' A n OpenAI statement released this week claimed the company had made its popular service ChatGPT better at supporting users experiencing mental health problems like suicidal ideation or delusions, but experts tell the Guardian they need to do more to truly ensure users are protected. The Guardian tested several prompts indicating suicidal ideation with the ChatGPT GPT-5 updated model, which is now the default, and got alarming responses from the large language model (LLM) chatbot.

Social media has significantly reshaped interpersonal communication, fostering connectivity while also enabling the proliferation of misinformation. The unchecked spread of false narratives has profound effects on mental health, contributing to increased stress, anxiety, and misinformation-driven paranoia. This study presents a hybrid transformer-based approach using a RoBERTa-LSTM classifier to detect misinformation, assess its impact on mental health, and classify disorders linked to misinformation exposure. The proposed models demonstrate accuracy rates of 98.4, 87.8, and 77.3 in detecting misinformation, mental health implications, and disorder classification, respectively. Furthermore, Pearson's Chi-Squared Test for Independence (p-value = 0.003871) validates the direct correlation between misinformation and deteriorating mental well-being. This study underscores the urgent need for better misinformation management strategies to mitigate its psychological repercussions. Future research could explore broader datasets incorporating linguistic, demographic, and cultural variables to deepen the understanding of misinformation-induced mental health distress.

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion", coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

Summary: Utilizing data from Twitter and applying natural language processing artificial intelligence algorithms, researchers created a new, accurate prediction model for depression and anxiety. Researchers at the University of São Paulo (USP) in Brazil are using artificial intelligence (AI) and Twitter, one of the world's largest social media platforms, to try to create anxiety and depression prediction models that could in future provide signs of these disorders before clinical diagnosis. The study is reported in an article published in the journal Language Resources and Evaluation. Construction of a database, called SetembroBR, was the first step in the study. The name is a reference to Yellow September, an annual suicide awareness and prevention campaign, and also to the fact that data collection for the study began one day in September. The second step is still in progress but has provided some preliminary findings, such as the possibility of detecting whether a person is likely to develop depression solely on the basis of their social media friends and followers, without taking their own posts into account.

Nick Ismail One in four people have mental health issues and in men under 50, suicide is the main cause of death. It is a huge, often misunderstood problem that pervades every society and most families. This was the opening message delivered by Valentin Tablan, an artificial intelligence expert and scientist at Ieso Digital Health, at the Healthcare Summit in London last month. His message was clear: the mental health problem is endemic, but we know the solution in cognitive behavioural therapy. The issue is not everyone has access to this service. There are not enough therapists to deliver the necessary therapy.

An app called TapCounter records each time I touch my phone's screen. My swipes and jabs are averaging about 1,000 a day, though I notice that's falling as I steer shy of social media to meet my deadline. The European company behind it, QuantActions, promises that through capturing and analysing the data it will be able to "detect important indicators related to mental/neurological health". Arko Ghosh is the company's cofounder and a neuroscientist at Leiden University in the Netherlands. "Tappigraphy patterns" – the time series of my touches – can, he says, confidently be used not only to infer slumber habits (tapping in the wee hours means you are not sleeping) but also mental performance level (the small intervals in a series of key-presses represent a proxy for reaction time), and he has published work to support it.

Mental health issues are considered a taboo in India. Given the fact that the country was among the first few to put in place a structured National Mental Health Programme in the early 1980s, this is quite astonishing to believe.As per the data gathered by NIMHANS a few years back, India had about 100 million people dealing with different mental health issues. The WHO reports that mental health problems cause a burden equivalent to 2443 disability adjusted life years (DALYs) per 100,000 people, and suicide rate of 21.1 per 100,000. The organization believes that the country will incur a loss of around $1.03 trillion between 2012-2030. The above stats are based only on the recorded cases, and there is likely to be a much larger population that doesn't discuss the mental health situation despite sustained campaigning and awareness.

Suggesting that budget cuts have rendered mere human police incapable of doing their jobs without cybernetic help, project lead Iain Donnelly claims working with an AI system will allow the force to do more with less. He insists that the National Data Analytics Solution, as it's called, will target only those individuals already known to have criminal tendencies, sniffing out likely offenders to divert them with therapeutic "interventions," including individuals who are stopped and searched but never arrested or charged. Donnelly claims the program is not designed to "pre-emptively arrest" anyone, but to provide "support from local health or social workers," giving the example of an individual with a history of mental health problems being flagged as a likely violent offender, then contacted by social services. Given that a violent mental case would almost certainly react negatively to being contacted out of nowhere by a mysterious social worker – and that a history of mental health problems is not in itself criminal – Donnelly was wise to end his example there. "Interventions" will be offered only to potential offenders, but the NDAS claims to be able to pick their victims as well.

A recent study published by the researchers at MIT and Harvard University used Natural Language Processing (NLP) to monitor the impact of COVID-19 pandemic on people's mental health. Collating Reddit posts of more than 800,000 users from 2018 to 2020, the researchers used various NLP techniques like trend analysis, supervised learning and unsupervised learning to characterise changes in the language used by mental health support groups. The study performed classification among mental health subreddits (forums dedicated to a specific topic on Reddit) as well as non-mental health subreddits and identified important features that help understand how each mental health problem may manifest in language. The trend analysis monitored COVID-19 related tokens (words, characters, or subwords) across subreddits and language features from January to April, to observe patterns. The researchers measured "how much the posts were about COVID-19" compared to the total number of words.