Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning

Social networks use in the context of Schizophrenia: a review of the literature

Schizophrenia is a persistent mental health condition that, while presenting challenges, underscores the dynamic nature of cognitive functions and encourages a unique perspective on how individuals engage with their surroundings. Social networks, as a means of communication of great importance at the present time, are for this type of people a way of interacting with their environment with a high level of security. The aim is to find out how schizophrenia is dealt with in different social networks and to differentiate between different types of articles dealing with the use of Facebook, X (former Twitter), YouTube, TikTok, Instagram, and Weibo. A total of 45 articles to i) Social networks used, ii) Country of analyzed users, iii) age of the users analyzed, iv) focus of the analyzed manuscript (mental health literacy, stigmatization, detection of patterns associated with schizophrenia, and Harmful substance use). It was observed that 45.45% of the studies analyzed were conducted in the USA population, followed by UK and China (13.64%). The most analyzed social networks were those based on audiovisual communication (60%). Furthermore, the two main foci addressed in these articles were: stigmatization of schizophrenia with 16 articles (35.55%), following by the prediction of schizophrenia-detecting patterns with 15 articles (33.33%) and the use of social networks to stigmatize people with schizophrenia (38%) and only 14 articles (31.11%) were focused on mental health literacy. Likewise, it was found that there is great potential in the use of the analysis of the content generated, as possible predictors of the presence of this disease, which would allow rapid detection and intervention for psychosis and schizophrenia.

What methods are used to examine representation of mental ill-health on social media? A systematic review

There has been an increasing number of papers which explore the representation of mental health on social media using various social media platforms and methodologies. It is timely to review methodologies employed in this growing body of research in order to understand their strengths and weaknesses. This systematic literature review provides a comprehensive overview and evaluation of the methods used to investigate the representation of mental ill-health on social media, shedding light on the current state of this field. Seven databases were searched with keywords related to social media, mental health, and aspects of representation (e.g., trivialisation or stigma). Of the 36 studies which met inclusion criteria, the most frequently selected social media platforms for data collection were Twitter (n = 22, 61.1%), Sina Weibo (n = 5, 13.9%) and YouTube (n = 4, 11.1%). The vast majority of studies analysed social media data using manual content analysis (n = 24, 66.7%), with limited studies employing more contemporary data analysis techniques, such as machine learning (n = 5, 13.9%). Few studies analysed visual data (n = 7, 19.4%). To enable a more complete understanding of mental ill-health representation on social media, further research is needed focussing on popular and influential image and video-based platforms, moving beyond text-based data like Twitter. Future research in this field should also employ a combination of both manual and computer-assisted approaches for analysis.

Associations between executive functions assessed in different contexts in a genetically informative sample

Executive functions (EFs) are cognitive functions that help direct goal-related behavior. EFs are usually measured via behavioral tasks assessed in highly controlled laboratory settings under the supervision of a research assistant. Online versions of EF tasks are an increasingly popular alternative to in-lab testing. However, researchers do not have the same control over the testing environment during online EF assessments. To assess the extent to which EFs assessed in-lab and online are related, we used data from the Colorado Online Twin Study (CoTwins; 887 individual twins aged 13.98-19.05) and constructed an Lab Common EF factor and an Online Common EF factor from four EF tasks assessed in-lab and online. The Lab Common and Online Common EF factors were genetically identical (rA = 1.00) but phenotypically separable (r = .77, 95% confidence interval [0.59, 0.94]) indicating that these EF factors have the same genetic underpinnings but may be differentially influenced by environmental factors. We examined phenotypic, genetic, and environmental correlations between the EF factors and a general cognitive ability factor (g) assessed in the lab and found similar relationships between Lab Common EF and g and Online Common EF and g. Overall, these results suggest that Common EF factors assessed in different contexts are highly related to each other and similarly related to other cognitive outcomes. These findings indicate that online task-based EF assessments could be a viable strategy for increasing sample sizes in large-scale studies, particularly genetically informed studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Recreational Drug Use and Distress From Hallucinations in the General Dutch Population

BACKGROUND AND HYPOTHESIS

Distress associated with auditory (AH) and visual (VH) hallucinations in the general population was found to be predictive of later need for mental healthcare. It is, therefore, important to understand factors relating to the distress individuals experience from their hallucinations. Hallucinations can easily occur under substance-induced states, but recreational drug use is also known as a self-medication strategy. The current study, therefore, investigated whether recreational drug use by individuals from the general population is associated with the degree of distress experienced from AH and/or VH.

STUDY DESIGN

Drug use and distress severity associated with AH (N = 3.041) and/or VH (N = 2.218) were assessed by means of an online survey in the general Dutch population (>14 years of age).

STUDY RESULTS

Multiple linear regression revealed that while past month consumption of alcohol was associated with less AH- and VH-related distress, past month cannabis use was associated with more AH- and VH-related distress. Furthermore, past month use of nitrous oxide was associated with more severe VH-related distress.

CONCLUSION

Recreational use of alcohol, cannabis, and nitrous oxide may play important differential roles in the degree of distress associated with AH and VH in individuals from the general population. The consumption of these substances could form a potential risk factor for the development of distressing hallucinations or function as a signal marker for their occurrence. Due to the cross-sectional design of the current study, the causal relation between recreational drug use and distressing hallucinations remains to be elucidated.

Language abnormalities in schizophrenia: binding core symptoms through contemporary empirical evidence

Both the ability to speak and to infer complex linguistic messages from sounds have been claimed as uniquely human phenomena. In schizophrenia, formal thought disorder (FTD) and auditory verbal hallucinations (AVHs) are manifestations respectively relating to concrete disruptions of those abilities. From an evolutionary perspective, Crow (1997) proposed that "schizophrenia is the price that Homo sapiens pays for the faculty of language". Epidemiological and experimental evidence points to an overlap between FTD and AVHs, yet a thorough investigation examining their shared neural mechanism in schizophrenia is lacking. In this review, we synthesize observations from three key domains. First, neuroanatomical evidence indicates substantial shared abnormalities in language-processing regions between FTD and AVHs, even in the early phases of schizophrenia. Second, neurochemical studies point to a glutamate-related dysfunction in these language-processing brain regions, contributing to verbal production deficits. Third, genetic findings further show how genes that overlap between schizophrenia and language disorders influence neurodevelopment and neurotransmission. We argue that these observations converge into the possibility that a glutamatergic dysfunction in language-processing brain regions might be a shared neural basis of both FTD and AVHs. Investigations of language pathology in schizophrenia could facilitate the development of diagnostic tools and treatments, so we call for multilevel confirmatory analyses focused on modulations of the language network as a therapeutic goal in schizophrenia.

Investigating mental health service user views of stigma on Twitter during COVID-19: a mixed-methods study

Background: Mental health stigma on social media is well studied, but not from the perspective of mental health service users. Coronavirus disease-19 (COVID-19) increased mental health discussions and may have impacted stigma.

Objectives: (1) to understand how service users perceive and define mental health stigma on social media; (2) how COVID-19 shaped mental health conversations and social media use.

Methods: We collected 2,700 tweets related to seven mental health conditions: schizophrenia, depression, anxiety, autism, eating disorders, OCD, and addiction. Twenty-seven service users rated them as stigmatising or neutral, followed by focus group discussions. Focus group transcripts were thematically analysed.

Results: Participants rated 1,101 tweets (40.8%) as stigmatising. Tweets related to schizophrenia were most frequently classed as stigmatising (411/534, 77%). Tweets related to depression or anxiety were least stigmatising (139/634, 21.9%). A stigmatising tweet depended on perceived intention and context but some words (e.g. “psycho”) felt stigmatising irrespective of context.

Discussion: The anonymity of social media seemingly increased stigma, but COVID-19 lockdowns improved mental health literacy. This is the first study to qualitatively investigate service users' views of stigma towards various mental health conditions on Twitter and we show stigma is common, particularly towards schizophrenia. Service user involvement is vital when designing solutions to stigma.

Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study

Background

Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns.

Objective

This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions.

Methods

Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time.

Results

A random forest model best identified misconceptions with an accuracy of 82% from blind validation and found that 37% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions.

Conclusions

Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time.