Content analysis of neurodegenerative and mental diseases social groups

The quality of information provided by the most popular dementia videos on TikTok

Summary of background

Dementia is among the leading causes of death and disability worldwide, having a major impact not only on the affected person but also on all of society. The Internet is a popular and growing source of health-related information for patients, family members, carriers, and physicians. TikTok, one of the most popular social media platforms, is an important source for knowledge access and adoption. However, the quality of health information on TikTok has not been sufficiently studied.

Objective

To evaluate the quality of the information provided in the most popular videos on dementia shared on TikTok.

Study design

A cross-sectional study.

Methods

The top 100 most popular videos on TikTok obtained by searching the hashtag "dementia" were included in the study and grouped based on their source and content. The popularity of the videos was estimated based on the numbers of likes, comments, and shares. The quality of health-related information was evaluated using the DISCERN score and the Global Quality Score (GQS).

Results

Videos had a median duration of 33.29 s; the median number of likes was 635,100, with a total of 93,698,200 likes, 903,859 comments, and 5,310,912 shares. The source (uploader) of 65% of the videos was family members, while only 4% were uploaded by doctors. The content was lifestyle-related in 62% of the videos, while 12% of the videos were for fun. Videos had a median DISCERN score of 22.5 (IQR 20-27) and a median GQS of 2 (IQR 1-3). The videos uploaded by doctors had the highest quality scores and the lowest popularity.

Conclusion

The most popular dementia videos on TikTok are mostly shared by family members and are of poor quality. Given the major public health issues associated with dementia, experts must provide appropriate and active assistance to patients in interpreting the information identified.

Dementia Research on Facebook and Twitter: Current Practice and Challenges

BACKGROUND

Social media is a powerful tool for engaging diverse audiences in dementia research. However, there is little data summarizing current content exchange in this context.

OBJECTIVE

To inform ethical dementia research engagement on social media, we characterized current practices by analyzing public social media posts.

METHODS

We retrieved Facebook (2-year period, N = 7,896) and Twitter (1-year period, N = 9,323) posts containing dementia research-related keywords using manual and machine learning-based search strategies. We performed qualitative and quantitative content and sentiment analyses on random samples (10%) of the posts.

RESULTS

Top Facebook users were advocacy (45%) and health organizations (25%). On Twitter, academics/researchers were the largest user group. Prevention was the most frequently coded theme (Facebook 30%; Twitter 26%), followed by treatment (Facebook 15%; Twitter 18%). Diagnostics had the highest Facebook engagement. Sharing knowledge was the primary form of content exchange (Facebook 63%; Twitter 80%). Most shared journal articles were peer-reviewed and open access. Emotional tone was overall more positive on Facebook. Justice was a prominent ethics topic regarding inequalities related to identity and intersecting modes of marginalization in dementia research.

CONCLUSION

The findings indicate the importance of social media as an engagement tool of current topics in health research and reveal areas of potential for increased engagement. These data can inform consensus-based best practices for ethical social media application in dementia research.

A Long Way to Go: Patient Perspectives on Digital Health for Parkinson's Disease

Digital health promises to improve healthcare, health, and wellness through the use of digital technologies. The purpose of this commentary is to review and discuss the field of digital health for Parkinson’s disease (PD) focusing on the needs, expectations, and wishes of people with PD (PwP). Our analysis shows that PwP want to use digital technologies to actively manage the full complexity of living with PD on an individual level, including the unpredictability and variability of the condition. Current digital health projects focusing on PD, however, does not live up to the expectations of PwP. We conclude that for digital health to reach its full potential, the right of PwP to access their own data needs to be recognised, PwP should routinely receive personalised feedback based on their data, and active involvement of PwP as an equal partner in digital health development needs to be the norm.

Mining Twitter to Inform the Design of Online Interventions for Hispanic Alzheimer's Disease and Related Dementias Caregivers

BACKGROUND/OBJECTIVE

Hispanics are about 1.5 times as likely as non-Hispanic Whites to experience Alzheimer's disease and related dementias (AD/ADRD). Eight percent of AD/ADRD caregivers are Hispanics. The purpose of this article is to provide a methodological case study of using data mining methods and the Twitter platform to inform online self-management and social support intervention design and evaluation for Hispanic AD/ADRD caregivers. It will enable other researchers to replicate the methods for their phenomena of interest.

METHOD

We extracted an analytic corpus of 317,658 English and Spanish tweets, applied content mining (topic models) and network structure analysis (macro-, meso-, and micro-levels) methods, and created visualizations of results.

RESULTS

The topic models showed differences in content between English and Spanish tweet corpora and between years analyzed. Our methods detected significant structural changes between years including increases in network size and subgroups, decrease in proportion of isolates, and increase in proportion of triads of the balanced communication type.

DISCUSSION/CONCLUSION

Each analysis revealed key lessons that informed the design and/or evaluation of online self-management and social support interventions for Hispanic AD/ADRD caregivers. These lessons are relevant to others wishing to use Twitter to characterize a particular phenomenon or as an intervention platform.

What Can We Learn About Mental Health Needs From Tweets Mentioning Dementia on World Alzheimer's Day?

BACKGROUND

Twitter can address the mental health challenges of dementia care. The aims of this study is to explore the contents and user interactions of tweets mentioning dementia to gain insights for dementia care.

METHODS

We collected 35,260 tweets mentioning Alzheimer’s or dementia on World Alzheimer’s Day, September 21^st in 2015. Topic modeling and social network analysis were applied to uncover content and structure of user communication.

RESULTS

Global users generated keywords related to mental health and care including #psychology and #mental health. There were similarities and differences between the UK and the US in tweet content. The macro-level analysis uncovered substantial public interest on dementia. The meso-level network analysis revealed that top leaders of communities were spiritual organizations and traditional media.

CONCLUSIONS

The application of topic modeling and multi-level network analysis while incorporating visualization techniques can promote a global level understanding regarding public attention, interests, and insights regarding dementia care and mental health.

Use of Twitter to monitor attitudes toward depression and schizophrenia: an exploratory study

Introduction. The paper reports on an exploratory study of the usefulness of Twitter for unobtrusive assessment of stigmatizing attitudes in the community.

Materials and Methods. Tweets with the hashtags #depression or #schizophrenia posted on Twitter during a 7-day period were collected. Tweets were categorised based on their content and user information and also on the extent to which they indicated a stigmatising attitude towards depression or schizophrenia (stigmatising, personal experience of stigma, supportive, neutral, or anti-stigma). Tweets that indicated stigmatising attitudes or personal experiences of stigma were further grouped into the following subthemes: social distance, dangerousness, snap out of it, personal weakness, inaccurate beliefs, mocking or trivializing, and self-stigma.

Results and Discussion. Tweets on depression mostly related to resources for consumers (34%), or advertised services or products for individuals with depression (20%). The majority of schizophrenia tweets aimed to increase awareness of schizophrenia (29%) or reported on research findings (22%). Tweets on depression were largely supportive (65%) or neutral (27%). A number of tweets were specifically anti-stigma (7%). Less than 1% of tweets reflected stigmatising attitudes (0.7%) or personal experience of stigma (0.1%). More than one third of the tweets which reflected stigmatising attitudes were mocking or trivialising towards individuals with depression (37%). The attitude that individuals with depression should “snap out of it” was evident in 30% of the stigmatising tweets. The majority of tweets relating to schizophrenia were categorised as supportive (42%) or neutral (43%). Almost 10% of tweets were explicitly anti-stigma. The percentage of tweets showing stigmatising attitudes was 5%, while less than 1% of tweets described personal experiences of stigmatising attitudes towards individuals with schizophrenia. Of the tweets that indicated stigmatising attitudes, most reflected inaccurate beliefs about schizophrenia being multiple personality disorder (52%) or mocked or trivialised individuals with schizophrenia (33%).

Conclusions. The study supports the use of analysis of Twitter content to unobtrusively measure attitudes towards mental illness, both supportive and stigmatising. The results of the study may be useful in assisting mental health promotion and advocacy organisations to provide information about resources and support, raise awareness and counter common stigmatising attitudes.