Dataset Information

A computational study of mental health awareness campaigns on social media.

ABSTRACT: 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.

SUBMITTER: Saha K

PROVIDER: S-EPMC6875652 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A computational study of mental health awareness campaigns on social media.

Saha Koustuv K Torous John J Ernala Sindhu Kiranmai SK Rizuto Conor C Stafford Amanda A De Choudhury Munmun M

Translational behavioral medicine 20191101 6

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 ...[more]

PMID: 30834942

Similar Datasets

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.

Project description:BackgroundPrevious studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored.ObjectiveThe aim of this study is to understand clinician perspectives regarding computational mental health insights from patients' social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations.MethodsWe developed a prototype that can analyze consented patients' Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient's social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis.ResultsClinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients' verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients' social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings.ConclusionsOur findings support the touted potential of computational mental health insights from patients' social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology.

Project description:BACKGROUND:Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. OBJECTIVE:The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization's Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users' tweeting activities. METHODS:All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. RESULTS:Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users. CONCLUSIONS:Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

Dataset Information

A computational study of mental health awareness campaigns on social media.

Publications

A computational study of mental health awareness campaigns on social media.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets