This Viewpoint discusses the ways social media can be used as a critical tool in managing the COVID-19 outbreak, such as by directing users to trusted sources and counteracting misinformation, and ...how it can transform aspects of preparedness and response for the future.
Social media can be used to disseminate information about preventive health care. Challenges include avoiding misinformation, updating information, data protection, and influencing uninsured ...individuals.
Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose.
To systematically review ...the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research.
We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015.
We searched for peer-reviewed original research studies that primarily used Twitter for health research.
Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source.
Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%).
We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user's Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.
Social media are changing the way people communicate both in their day-to-day lives and during disasters that threaten public health. Engaging with and using such media may help the ...emergency-management community to respond to disasters.
Despite blocked Internet service, new social media such as “speak-to-tweet” (which allows brief Twitter messages to be sent through a voice connection) were being used to improve communication about health and safety within the first few days of the 2011 Egyptian uprising, which had itself been organized by means of social media. After Haiti's 2010 earthquake, Ushahidi, an open-source Web platform that uses “crowd-sourced” information to support crisis management, linked health care providers requiring supplies to those who had them, and victims trapped under the rubble used Facebook to reach out for help.
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During the 2009 influenza pandemic, within minutes . . .
Facebook language predicts depression in medical records Eichstaedt, Johannes C.; Smith, Robert J.; Merchant, Raina M. ...
Proceedings of the National Academy of Sciences - PNAS,
10/2018, Letnik:
115, Številka:
44
Journal Article
Recenzirano
Odprti dostop
Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of ...consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy area under the curve (AUC) = 0.69, approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.
The article reports on the need for authentic information for patients and the misuse of social media by publishing wrong medical information. It also discusses the measures to prevent such misuse, ...the cost undertaken by the publishing houses for publishing original information, and the impact of social media influence on the society.
We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language ...significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients' consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients' Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Public Health Messaging in an Era of Social Media Merchant, Raina M; South, Eugenia C; Lurie, Nicole
JAMA : the journal of the American Medical Association,
01/2021, Letnik:
325, Številka:
3
Journal Article
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