https://onlinelibrary.wiley.com/doi/pdf/10.1002/wps.20883

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality.

Researchers have collected Twitter data to study a wide range of topics. This growing body of literature, however, has not yet been reviewed systematically to synthesize Twitter-related papers. The existing literature review papers have been limited by constraints of traditional methods to manually select and analyze samples of topically related papers. The goals of this retrospective study are to identify dominant topics of Twitter-based research, summarize the temporal trend of topics, and interpret the evolution of topics withing the last ten years. This study systematically mines a large number of Twitter-based studies to characterize the relevant literature by an efficient and effective approach. This study collected relevant papers from three databases and applied text mining and trend analysis to detect semantic patterns and explore the yearly development of research themes across a decade. We found 38 topics in more than 18,000 manuscripts published between 2006 and 2019. By quantifying temporal trends, this study found that while 23.7% of topics did not show a significant trend (P = 0.05), 21% of topics had increasing trends and 55.3% of topics had decreasing trends that these hot and cold topics represent three categories: application, methodology, and technology. The contributions of this paper can be utilized in the growing field of Twitter-based research and are beneficial to researchers, educators, and publishers.

/pdf/twitter-and-research-a-systematic-literature-review-through-2199wcn2zf.pdf

Twitter and Research: A Systematic Literature Review Through Text Mining

Redefining fatty liver disease: an international patient perspective.

As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and stakeholders, ranging from researchers, to those affected by the issue, to be aware of the advancing new mediums in which the public can share content ranging from useful resources and self-help tips to personal struggles with respect to both illness and its stigmatization. A better understanding of this new public discourse on mental health, often framed as social media campaigns, can help perpetuate the allocation of sparse mental health resources, the need for educational awareness, and the usefulness of community, with an opportunity to reach those seeking help at the right moment. The objective of this study was to understand the nature of and engagement around mental health content shared on mental health campaigns, specifically #MyTipsForMentalHealth on Twitter around World Mental Health Awareness Day in 2017. We collected 14,217 Twitter posts from 10,805 unique users between September and October 2017 that contained the hashtag #MyTipsForMentalHealth. With the involvement of domain experts, we hand-labeled 700 posts and categorized them as (a) Fact, (b) Stigmatizing, (c) Inspirational, (d) Medical/Clinical Tip, (e) Resource Related, (f) Lifestyle or Social Tip or Personal View, and (g) Off Topic. After creating a "seed" machine learning classifier, we used both unsupervised and semi supervised methods to classify posts into the various expert identified topical categories. We also performed a content analysis to understand how information on different topics spread through social networks. Our support vector machine classification algorithm achieved a mean cross-validation accuracy of 0.81 and accuracy of 0.64 on unseen data. We found that inspirational Twitter posts were the most spread with a mean of 4.17 retweets, and stigmatizing content was second with a mean of 3.66 retweets. Classification of social media-related mental health interactions offers valuable insights on public sentiment as well as a window into the evolving world of online self-help and the varied resources within. Our results suggest an important role for social media-based peer support to not only guide information seekers to useful content and local resources but also illuminate the socially-insular aspects of stigmatization. However, our results also reflect the challenges of quantifying the heterogeneity of mental health content on social media and the need for novel machine learning methods customized to the challenges of the field.

A computational study of mental health awareness campaigns on social media

There is some dissatisfaction with the term "nonalcoholic fatty liver disease (NAFLD)," which overemphasizes alcohol and underemphasizes the importance of metabolic risk factors in this disease. Recently, a consensus recommended "metabolic (dysfunction)-associated fatty liver disease (MAFLD)" as a more appropriate term to describe fatty liver diseases (FLD) associated with metabolic dysfunction. During the definition change from NAFLD to MAFLD, subjects with FLD and metabolic abnormalities, together with other etiologies of liver diseases such as alcohol, virus, or medication who have been excluded from the NAFLD criteria, were added to the MAFLD criteria, while subjects with FLD but without metabolic abnormality, who have been included in the NAFLD criteria, were excluded from the MAFLD criteria. This means that there is an emphasis on the metabolic dysfunction in MAFLD which may underestimate the prognostic value of hepatic steatosis itself, whereas the MAFLD criteria might better identify subjects who are at a higher risk of hepatic or cardiovascular outcomes. However, non-metabolic risk NAFLD subjects who are excluded from the MAFLD criteria are missed from the diagnosis, and their potential risk can be the cause of future diseases. Although huge controversies remain, this review focused on summarizing recent studies that compared the clinical and prognostic characteristics between subjects with NAFLD and MAFLD.

/pdf/from-nonalcoholic-fatty-liver-disease-to-metabolic-4dpea3t2eb.pdf

From nonalcoholic fatty liver disease to metabolic-associated fatty liver disease: Big wave or ripple?

There are numerous campaigns targeting mental health stigma. However, evaluating how effective these are in changing perceptions is complex. Social media may be used to assess stigma levels and highlight new trends. This study uses a social media platform, Twitter, to investigate stigmatising and trivialising attitudes across a range of mental and physical health conditions. Tweets (i.e. messages) associated with five mental and five physical health conditions were collected in ten 72-h windows over a 50-day period using automated software. A random selection of tweets per condition was considered for the analyses. Tweets were categorised according to their topic and presence of stigmatising and trivialising attitudes. Qualitative thematic analysis was performed on all stigmatising and trivialising tweets. A total of 1,059,258 tweets were collected, and from this sample 1300 tweets per condition were randomly selected for analysis. Overall, mental health conditions were found to be more stigmatised (12.9%) and trivialised (14.3%) compared to physical conditions (8.1 and 6.8%, respectively). Amongst mental health conditions the most stigmatised condition was schizophrenia (41%) while the most trivialised was obsessive compulsive disorder (33%). Our findings show that mental health stigma is common on social media. Trivialisation is also common, suggesting that while society may be more open to discussing mental health problems, care should be taken to ensure this is done appropriately. This study further demonstrates the potential for social media to be used to measure the general public’s attitudes towards mental health conditions.

/pdf/measuring-attitudes-towards-mental-health-using-social-media-3elwvtas3a.pdf

Measuring attitudes towards mental health using social media: investigating stigma and trivialisation

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Measuring attitudes towards mental health using social media: investigating stigma and trivialisation