Mental illness on Instagram: a mixed method study to characterize public content, sentiments, and trends of antidepressant use

Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review

BACKGROUND

The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data.

OBJECTIVE

This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care.

METHODS

We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

RESULTS

After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001).

CONCLUSIONS

Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

Understanding Mental Health Organizations' Instagram Through Visuals: A Content Analysis

This study analyzed the content, visual features, and audience engagement data of Instagram posts from two mental health organizations over one year (N = 725). For content features, mental health literacy and communicative strategies were examined. Posts that promoted knowledge of mental disorders and treatments, used information and community strategy were more likely to receive higher audience engagement. Visual features of demographic segments, visual composition, and visual framing topics were analyzed. Images that covered an unspecific population, used illustrated images, and focused on anti-stigma topical frames obtained more engagement. Theoretical contributions and practical applications regarding visual message design and management on social media to promote mental health are also offered.

Implications of Online Self-Diagnosis in Psychiatry

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis.

(De)Stigmatizing Depression on Social Media: The Role of Responsibility Frames

Responsibility frames on social media could shape recipients' responses toward people with depression, which is crucial for the public (de)stigmatization of the mental disorder. Thus, the present study examines the effects of different responsibility frames (individual, social, combination) in Instagram-posts about depression on respondents' related attributions as well as their emotional and behavioral reactions toward people suffering from the illness. Our online-experiment (N = 1,015) revealed that frames emphasizing the responsibility of one's social network (e.g. family, friends and professionals) for depression, i.e. social frames, strengthened participants' attributions to the social network, i.e. social attributions, most effectively. Individual frames, however, primarily intensified individual attributions to those affected by depression. Contrary to previous findings, a combination frame did not prove to increase recipients' social attributions more than a one-sided social frame. For emotional and behavioral responses, we did not find any effects of responsibility frames compared to the control group-possibly due to buffering effects of the narrative structure of the Instagram posts.

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

BACKGROUND

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.

OBJECTIVE

This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?

METHODS

A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).

CONCLUSIONS

Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Web Search Query Volume Correlates With Prescription Volumes of Antidepressants and Antipsychotics in the Netherlands and United Kingdom: An Explorative Study

BACKGROUND

The significant increase in Internet availability has resulted in a rise in search queries on health-related topics. Previous research has demonstrated the potential for analyzing web search query volume for nonpsychotropic prescription drugs, while studies on psychotropic drugs remain scarce. The aims of this study were to expand upon this scarce knowledge by investigating the relationship between web search query volumes and prescription volumes of antidepressants and antipsychotics in the United Kingdom and the Netherlands and to gain insight in topics of concern, such as withdrawal symptoms and discontinuation.

METHODS

Data were obtained for the United Kingdom and the Netherlands from January 2010 until January 2021. Prescription volume data for 5 antidepressants (paroxetine, fluoxetine, sertraline, citalopram, venlafaxine) and 5 antipsychotics (quetiapine, olanzapine, clozapine, aripiprazole, and risperidone) were obtained. Web search query volumes and data on related search queries of these substances were acquired from Google Trends. Descriptive statistics and Pearson correlation analyses were performed.

RESULTS

A strong, positive, and statistically significant correlation between web search query volume and prescription volume was observed for most included substances in both the Netherlands and the United Kingdom. The search queries related to the included antidepressants and antipsychotics indicate important topics of concern for specific substances, such as withdrawal symptoms and discontinuation.

CONCLUSIONS

Web search data from Google Trends could potentially be used as a proxy for prescribing trends of antidepressants and antipsychotics and to gain insight in topics of concern of users of these substances. These findings highlight the importance of providing reliable patient information, particularly regarding adverse effects, withdrawal, and discontinuation.

Misinformation in Italian Online Mental Health Communities During the COVID-19 Pandemic: Protocol for a Content Analysis Study

Background

Social media platforms are widely used by people suffering from mental illnesses to cope with their conditions. One modality of coping with these conditions is navigating online communities where people can receive emotional support and informational advice. Benefits have been documented in terms of impact on health outcomes. However, the pitfalls are still unknown, as not all content is necessarily helpful or correct. Furthermore, the advent of the COVID-19 pandemic and related problems, such as worsening mental health symptoms, the dissemination of conspiracy narratives, and medical distrust, may have impacted these online communities. The situation in Italy is of particular interest, being the first Western country to experience a nationwide lockdown. Particularly during this challenging time, the beneficial role of community moderators with professional mental health expertise needs to be investigated in terms of uncovering misleading information and regulating communities.

Objective

The aim of the proposed study is to investigate the potentially harmful content found in online communities for mental health symptoms in the Italian language. Besides descriptive information about the content that posts and comments address, this study aims to analyze the content from two viewpoints. The first one compares expert-led and peer-led communities, focusing on differences in misinformation. The second one unravels the impact of the COVID-19 pandemic, not by merely investigating differences in topics but also by investigating the needs expressed by community members.

Methods

A codebook for the content analysis of Facebook communities has been developed, and a content analysis will be conducted on bundles of posts. Among 14 Facebook groups that were interested in participating in this study, two groups were selected for analysis: one was being moderated by a health professional (n=12,058 members) and one was led by peers (n=5598 members). Utterances from 3 consecutive calendar years will be studied by comparing the months from before the pandemic, the months during the height of the pandemic, and the months during the postpandemic phase (2019-2021). This method permits the identification of different types of misinformation and the context in which they emerge. Ethical approval was obtained by the Università della Svizzera italiana ethics committee.

Results

The usability of the codebook was demonstrated with a pretest. Subsequently, 144 threads (1534 utterances) were coded by the two coders. Intercoder reliability was calculated on 293 units (19.10% of the total sample; Krippendorff α=.94, range .72-1). Aside from a few analyses comparing bundles, individual utterances will constitute the unit of analysis in most cases.

Conclusions

This content analysis will identify deleterious content found in online mental health support groups, the potential role of moderators in uncovering misleading information, and the impact of COVID-19 on the content.

International Registered Report Identifier (IRRID)

PRR1-10.2196/35347

Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags

Background

Although “infodemiological” methods have been used in research on coronavirus disease (COVID-19), an examination of the extent of infodemic moniker (misinformation) use on the internet remains limited.

Objective

The aim of this paper is to investigate internet search behaviors related to COVID-19 and examine the circulation of infodemic monikers through two platforms—Google and Instagram—during the current global pandemic.

Methods

We have defined infodemic moniker as a term, query, hashtag, or phrase that generates or feeds fake news, misinterpretations, or discriminatory phenomena. Using Google Trends and Instagram hashtags, we explored internet search activities and behaviors related to the COVID-19 pandemic from February 20, 2020, to May 6, 2020. We investigated the names used to identify the virus, health and risk perception, life during the lockdown, and information related to the adoption of COVID-19 infodemic monikers. We computed the average peak volume with a 95% CI for the monikers.

Results

The top six COVID-19–related terms searched in Google were “coronavirus,” “corona,” “COVID,” “virus,” “corona virus,” and “COVID-19.” Countries with a higher number of COVID-19 cases had a higher number of COVID-19 queries on Google. The monikers “coronavirus ozone,” “coronavirus laboratory,” “coronavirus 5G,” “coronavirus conspiracy,” and “coronavirus bill gates” were widely circulated on the internet. Searches on “tips and cures” for COVID-19 spiked in relation to the US president speculating about a “miracle cure” and suggesting an injection of disinfectant to treat the virus. Around two thirds (n=48,700,000, 66.1%) of Instagram users used the hashtags “COVID-19” and “coronavirus” to disperse virus-related information.

Conclusions

Globally, there is a growing interest in COVID-19, and numerous infodemic monikers continue to circulate on the internet. Based on our findings, we hope to encourage mass media regulators and health organizers to be vigilant and diminish the use and circulation of these infodemic monikers to decrease the spread of misinformation.