Dataset Information

Using social media to monitor mental health discussions - evidence from Twitter.

ABSTRACT: Objectives:Given the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter. Materials and Methods:We collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide. Results:Two types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events. Discussion:Although ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health. Conclusion:Spikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account.

SUBMITTER: McClellan C

PROVIDER: S-EPMC7651954 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Using social media to monitor mental health discussions - evidence from Twitter.

McClellan Chandler C Ali Mir M MM Mutter Ryan R Kroutil Larry L Landwehr Justin J

Journal of the American Medical Informatics Association : JAMIA 20170501 3

<h4>Objectives</h4>Given the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter.<h4>Materials and methods</h4>We collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviat ...[more]

PMID: 27707822

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Project description:BackgroundThe COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world's mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population's mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being.ObjectiveThis study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic?MethodsWe analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool.ResultsLDA topics generated in the early months of the data set corresponded to major COVID-19-specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March.ConclusionsOur findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Project description:BackgroundSocial media holds exciting promise for advancing mental health research recruitment, however, the extent and efficacy to which these platforms are currently in use are underexplored.ObjectiveA systematic review was conducted to characterize the current use and efficacy of social media in recruiting participants for mental health research.MethodA literature review was performed using MEDLINE, EMBASE, and PsychINFO. Only non-duplicative manuscripts written in the English language and published between 1/1/2004-3/31/2019 were selected for further screening. Data extracted included study type and design, participant inclusion criteria, social media platform, advertising strategy, final recruited sample size, recruitment location, year, monetary incentives, comparison to other recruitment methods if performed, and final cost per participant.ResultsA total of 176 unique studies that used social media for mental health research recruitment were reviewed. The majority of studies were cross-sectional (62.5%) in design and recruited adults. Facebook was overwhelmingly the recruitment platform of choice (92.6%), with the use of paid advertisements being the predominant strategy (60.8%). Of the reviewed studies, substance abuse (43.8%) and mood disorders (15.3%) were the primary subjects of investigation. In 68.3% of studies, social media recruitment performed as well as or better than traditional recruitment methods in the number and cost of final enrolled participants. The majority of studies used Facebook for recruitment at a median cost per final recruited study participant of $19.47. In 55.6% of the studies, social media recruitment was the more cost-effective recruitment method when compared to traditional methods (e.g., referrals, mailing).ConclusionSocial media appears to be an effective and economical recruitment tool for mental health research. The platform raises methodological and privacy concerns not covered in current research regulations that warrant additional consideration.

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Using social media to monitor mental health discussions - evidence from Twitter.

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Using social media to monitor mental health discussions - evidence from Twitter.

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