State Legislator Social Media Posts About the 988 Suicide and Crisis Lifeline.
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ABSTRACT:
SUBMITTER: Purtle J
PROVIDER: S-EPMC10603493 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
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State Legislator Social Media Posts About the 988 Suicide and Crisis Lifeline.
Purtle Jonathan J Soltero Michael M Crane Margaret E ME McSorley Anna-Michelle Marie AM Knapp Molly M Drapeau Christopher W CW
JAMA network open 20231002 10
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| S-EPMC11579797 | biostudies-literature
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| S-EPMC10618841 | biostudies-literature
Project description:BackgroundRates of suicide attempts and deaths are highest on Mondays and these occur more frequently in the morning or early afternoon, suggesting weekly temporal and diurnal variation in suicidal behaviour. It is unknown whether there are similar time trends on social media, of posts relevant to suicide. We aimed to determine temporal and diurnal variation in posting patterns on the Reddit forum SuicideWatch, an online community for individuals who might be at risk of, or who know someone at risk of suicide.MethodsWe used time series analysis to compare date and time stamps of 90,518 SuicideWatch posts from 1st December 2008 to 31st August 2015 to (i) 6,616,431 posts on the most commonly subscribed general subreddit, AskReddit and (ii) 66,934 of these AskReddit posts, which were posted by the SuicideWatch authors.ResultsMondays showed the highest proportion of posts on SuicideWatch. Clear diurnal variation was observed, with a peak in the early morning (2:00-5:00 h), and a subsequent decrease to a trough in late morning/early afternoon (11:00-14:00 h). Conversely, the highest volume of posts in the control data was between 20:00-23:00 h.ConclusionsPosts on SuicideWatch occurred most frequently on Mondays: the day most associated with suicide risk. The early morning peak in SuicideWatch posts precedes the time of day during which suicide attempts and deaths most commonly occur. Further research of these weekly and diurnal rhythms should help target populations with support and suicide prevention interventions when needed most.
| S-EPMC8136175 | biostudies-literature
Project description:ObjectiveAs part of the National Suicide Prevention Lifeline's crisis response system, the Lifeline Crisis Chat Network (LCC) answers chats from hundreds of thousands of at-risk individuals yearly. The study's objective was to assess the effectiveness of these online crisis interventions.MethodData from 39,911 pre-chat surveys and 13,130 linked pre- and post-chat surveys completed by LCC chatters from October 2017-June 2018 were analyzed. The relationship of several effectiveness measures with chatter demographics, pre-chat distress, suicidal ideation, and chatters' perceptions of engagement with their counselors was examined using a series of logistic regression analyses.ResultsChatters were significantly and substantially less distressed at the end of the chat intervention than at the beginning. By the end of the chat, two-thirds of suicidal chatters reported that the chat had been helpful, while just under half reported being less suicidal.ConclusionsOur study offers empirical evidence for the Lifeline's online crisis chat services' effectiveness, but also highlights areas for improvement. This is of critical import in light of the recent designation of 988 as the nationwide number for the Lifeline beginning in 2022, which will increase the Lifeline's prominence in providing suicide prevention and mental health crisis interventions in the United States.
| S-EPMC9292033 | biostudies-literature
Project description:BackgroundA "concerning post" is a display of a user's emotional crisis on a social media platform. A better understanding of concerning posts is relevant to suicide prevention, but little is known about social media users' attitudes and responses to concerning posts. Military veterans in the United States are disproportionately affected by suicide, often use social media, and may have exposure to individuals with elevated suicide risk via concerning posts.ObjectiveThe objective of the study was (1) to obtain insight into whether and how US military veterans respond to members of their social network on social media (ie, "friends") who are experiencing substantial emotional distress, and (2) to identify potential interventions that could assist in users' response to concerning posts.MethodsWe recruited veterans through Facebook and conducted semistructured interviews with 30 participants between June and December 2017. We used a summary template for rapid analysis of each interview, followed by double-coding using a codebook based on topic domains from the interview guide. Members of the research team met regularly to discuss emerging patterns in the data, generate themes, and select representative quotes for inclusion in the manuscript.ResultsVeterans were reluctant to disclose emotional and health issues on Facebook, but they were open to reaching out to others' concerning posts. There was a complex calculus underlying whether and how veterans responded to a concerning post, which involved considering (1) physical proximity to the person posting, (2) relationship closeness, (3) existing responses to the post, and (4) ability to maintain contact with the person. Veterans desired additional training, backed by community-based veteran organizations, in how to respond to concerning posts from peers.ConclusionsThere is a need to incorporate features that will help veterans effectively respond to concerning posts from peers into suicide prevention training and to expand access for veterans to such training.
| S-EPMC7665941 | biostudies-literature
Project description:Innumeracy (lack of math skills) among nonscientists often leads climate scientists and others to avoid communicating numbers due to concerns that the public will not understand them and may disengage. However, people often report preferring to receive numbers; providing them also can improve decisions. Here, we demonstrated that the presence vs. absence of at least one Arabic integer in climate-related social-media posts increased sharing up to 31.7% but, counter to hypothesis, decreased liking of messages 5.2% in two preregistered observational studies (climate scientists on Twitter, N > 8 million Tweets; climate subreddit, N > 17,000 posts and comments). We speculated that the decreased liking was due, not to reduced engagement, but to more negative feelings towards climate-related content described with numeric precision. A preregistered within-participant experiment (N = 212) then varied whether climate consequences were described using Arabic integers (e.g. "90%") or another format (e.g. verbal terms, "almost all"). The presence of Arabic integers about consequences led to more sharing, wanting to find out more, and greater trust and perceptions of an expert messenger; perceived trust and expertise appeared to mediate effects on sharing and wanting to find out more. Arabic integers about consequences again led to more negative feelings about the Tweets as if numbers clarified the dismaying magnitude of climate threats. Our results indicate that harnessing the power of numbers could increase public trust and concern regarding this defining issue of our time. Communicators, however, should also consider counteracting associated negative feelings-that could halt action-by providing feasible solutions to increase people's self-efficacy.
| S-EPMC11263877 | biostudies-literature
Project description: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.
| S-EPMC6576767 | biostudies-literature
Project description:ImportanceSocial media is an influential source of medical information, but little is known about how posts discuss medical tests that carry potential for overdiagnosis or overuse.ObjectiveTo investigate how social media posts discuss 5 popular medical tests: full-body magnetic resonance imaging, the multicancer early detection test, and tests for antimullerian hormone, gut microbiome, and testosterone.Design, setting, and participantsThis cross-sectional study assessed posts on Instagram and TikTok between April 30, 2015, and January 23, 2024, that discussed full-body magnetic resonance imaging, the multicancer early detection test, and tests for antimullerian hormone, gut microbiome, and testosterone. Using keywords on newly created accounts, posts were searched and screened until 100 posts for each test on each platform were identified (n = 1000). Posts were excluded if they did not discuss 1 of the 5 tests or were not in English or if the account holders had fewer than 1000 followers.Main outcomes and measuresThe main outcome was information about benefits, harms, and overall tone discussed in the post. All outcomes were summarized descriptively. Logistic regression was used to assess whether the use of evidence or the account holder being a physician or having financial interests influenced how tests were discussed.ResultsA total of 982 posts from account holders with a combined 194 200 000 followers were analyzed. Across all tests, benefits were mentioned in 855 posts (87.1%) and harms in 144 (14.7%), with 60 (6.1%) mentioning overdiagnosis or overuse. Overall, 823 posts (83.8%) had a promotional (vs neutral or negative) tone. Evidence was explicitly used in 63 posts (6.4%), personal anecdotes were used in 333 (33.9%), 498 posts (50.7%) encouraged viewers to take action and get the test, and 668 account holders (68.0%) had financial interests. Posts from physicians were more likely to mention harms (odds ratio, 4.49; 95% CI, 2.85-7.06) and less likely to have an overall promotional tone (odds ratio, 0.53; 95% CI, 0.35-0.80).Conclusions and relevanceIn this cross-sectional study of social media posts about 5 popular medical tests, most posts were misleading or failed to mention important harms, including overdiagnosis or overuse. These data demonstrate a need for stronger regulation of misleading medical information on social media.
| S-EPMC11866028 | biostudies-literature
Project description:BackgroundPatient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients' experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases).ResultsWe found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.ConclusionThe main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.
| S-EPMC8371035 | biostudies-literature
Project description:ImportanceUnderstanding the outcomes of artificial light at night (ALAN) on insomnia is crucial for public health, particularly in rapidly urbanizing regions. However, evidence of the association between ALAN exposure and insomnia is limited, despite the large number of people exposed to ALAN.ObjectiveTo explore the association between outdoor ALAN exposure and insomnia among the Chinese population.Design, setting, and participantsThis cross-sectional study used social media data from Weibo (Sina), a social media platform, and satellite-derived nighttime light images. The study period spans from May 2022 to April 2023. The study encompasses 336 cities across China's mainland, providing a comprehensive national perspective. Data include insomnia-related posts from the platform users, representing a large and diverse population sample exposed to varying levels of ALAN.ExposureOutdoor ALAN exposure (in nanowatts per centimeters squared per steradian [nW/cm2/sr]) was measured using satellite-derived nighttime light images at a spatial resolution of 500 m.Main outcomes and measuresThe incidence of insomnia among residents at the city level was measured by the number of insomnia-related posts on social media. Multiple linear regression models were used to estimate the association between ALAN exposure and population insomnia, adjusting for population characteristics and meteorological factors at the city level.ResultsThe study included data from 1 147 583 insomnia-related posts. Daily mean ALAN exposure across the 336 cities ranged from 3.1 to 221.0 nW/cm2/sr. For each 5 nW/cm2/sr increase in ALAN exposure, the incidence of insomnia increased by 0.377% (95% CI, 0.372%-0.382%). The association was greater in less populated cities and under extreme temperature and poor air quality conditions. The observed exposure-response functions between ALAN exposure and insomnia demonstrated an upward trend, with steeper slopes observed at low exposures and leveling off at higher exposures.Conclusions and relevanceThis study provides evidence of the association between increased ALAN exposure and higher incidence of insomnia. These findings expand the current knowledge on adverse health outcomes of ALAN exposure and emphasize the potential health benefits of well-planned artificial nighttime lighting in China and other developing countries in the early stages of city planning.
| S-EPMC11579793 | biostudies-literature