Dataset Information

Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey.

ABSTRACT: BACKGROUND:Depression is a common disorder that still remains underdiagnosed and undertreated in the UK National Health Service. Charities and voluntary organizations offer mental health services, but they are still struggling to promote these services to the individuals who need them. By analyzing social media (SM) content using machine learning techniques, it may be possible to identify which SM users are currently experiencing low mood, thus enabling the targeted advertising of mental health services to the individuals who would benefit from them. OBJECTIVE:This study aimed to understand SM users' opinions of analysis of SM content for depression and targeted advertising on SM for mental health services. METHODS:A Web-based, mixed methods, cross-sectional survey was administered to SM users aged 16 years or older within the United Kingdom. It asked participants about their demographics, their usage of SM, and their history of depression and presented structured and open-ended questions on views of SM content being analyzed for depression and views on receiving targeted advertising for mental health services. RESULTS:A total of 183 participants completed the survey, and 114 (62.3%) of them had previously experienced depression. Participants indicated that they posted less during low moods, and they believed that their SM content would not reflect their depression. They could see the possible benefits of identifying depression from SM content but did not believe that the risks to privacy outweighed these benefits. A majority of the participants would not provide consent for such analysis to be conducted on their data and considered it to be intrusive and exposing. CONCLUSIONS:In a climate of distrust of SM platforms' usage of personal data, participants in this survey did not perceive that the benefits of targeting advertisements for mental health services to individuals analyzed as having depression would outweigh the risks to privacy. Future work in this area should proceed with caution and should engage stakeholders at all stages to maximize the transparency and trustworthiness of such research endeavors.

SUBMITTER: Ford E

PROVIDER: S-EPMC6881781 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey.

Ford Elizabeth E Curlewis Keegan K Wongkoblap Akkapon A Curcin Vasa V

JMIR mental health 20191113 11

<h4>Background</h4>Depression is a common disorder that still remains underdiagnosed and undertreated in the UK National Health Service. Charities and voluntary organizations offer mental health services, but they are still struggling to promote these services to the individuals who need them. By analyzing social media (SM) content using machine learning techniques, it may be possible to identify which SM users are currently experiencing low mood, thus enabling the targeted advertising of mental ...[more]

PMID: 31719022

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Project description:BACKGROUND:Society always has limited resources to expend on health care, or anything else. What are the unmet medical needs? How do we allocate limited resources to maximize the health and welfare of the people? These challenging questions might be re-examined systematically within an infodemiological frame on a much larger scale, leveraging the latest advancement in information technology and data science. OBJECTIVE:We expanded our previous work by investigating news media data to reveal the coverage of different diseases and medical conditions, together with their sentiments and topics in news articles over three decades. We were motivated to do so since news media plays a significant role in politics and affects the public policy making. METHODS:We analyzed over 3.5 million archive news articles from Reuters media during the periods of 1996/1997, 2008 and 2016, using summary statistics, sentiment analysis, and topic modeling. Summary statistics illustrated the coverage of various diseases and medical conditions during the last 3 decades. Sentiment analysis and topic modeling helped us automatically detect the sentiments of news articles (ie, positive versus negative) and topics (ie, a series of keywords) associated with each disease over time. RESULTS:The percentages of news articles mentioning diseases and medical conditions were 0.44%, 0.57% and 0.81% in the three time periods, suggesting that news media or the public has gradually increased its interests in medicine since 1996. Certain diseases such as other malignant neoplasm (34%), other infectious diseases (20%), and influenza (11%) represented the most covered diseases. Two hundred and twenty-six diseases and medical conditions (97.8%) were found to have neutral or negative sentiments in the news articles. Using topic modeling, we identified meaningful topics on these diseases and medical conditions. For instance, the smoking theme appeared in the news articles on other malignant neoplasm only during 1996/1997. The topic phrases HIV and Zika virus were linked to other infectious diseases during 1996/1997 and 2016, respectively. CONCLUSIONS:The multi-dimensional analysis of news media data allows the discovery of focus, sentiments and topics of news media in terms of diseases and medical conditions. These infodemiological discoveries could shed light on unmet medical needs and research priorities for future and provide guidance for the decision making in public policy.

Project description:IntroductionThe advertising of e-cigarettes in the UK is regulated through the revised EU Tobacco Products Directive and the Tobacco and Related Products Regulations, with further rules set out in the Advertising Standards Authority (ASA) Committees of Advertising (CAP) Code. Focusing on the ASA CAP Code Rules, we examined e-cigarette advertising regulation compliance in traditional advertising channels and on social media.MethodsWe conducted a content analysis of UK e-cigarette and related product advertising using a randomly selected sample (n = 130) of advertising in traditional channels and on Instagram which appeared between January and December 2019. All ads were independently double-coded to assess compliance with each CAP Code Rule.ResultsIn traditional channels, our sample of advertising had largely good compliance. Only very small numbers of these ads appeared to be clearly in breach of any of the ASA rules (5% were in breach of Rule 22.7; 2% of Rule 22.9; and 1% of Rule 22.10). In contrast, we judged that all of the Instagram sample (n = 30) was in breach of Rule 22.12. For some rules, it was not possible to make definitive judgments about compliance, given uncertainty regarding how a rule should be interpreted and applied.ConclusionsWe found overall good compliance for advertising in traditional channels, but assessed all of our social media advertising samples was in breach of regulations. Current guidance on e-cigarette advertising could be improved to facilitate e-cigarette advertising assessment and regulation. It would be beneficial to bring consumer perspectives into the assessment of regulation compliance.ImplicationsThe regulation of e-cigarette advertising is a global concern. The UK Government has a statutory obligation to review the Tobacco and Related Products Regulations by May 2021. This study assessed compliance with current UK e-cigarette advertising regulations on placement and content. We identified areas where greater clarity is needed and outlined implications for future regulation.

Project description:BackgroundThe coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic.ObjectiveThe objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform.MethodsUsing a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19-related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions.ResultsData were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8%), immunity (707/10,096, 7%), testing (605/10,096, 6%), and virus infection and transmission (503/10,096, 5%).ConclusionsOur findings indicate that Twitter and social media platforms can help identify important and useful knowledge shared by medical professionals during a pandemic.

Project description:BackgroundPrior research suggests that social media-based public health campaigns are often targeted by countercampaigns.ObjectiveUsing reactance theory as the theoretical framework, this research characterizes the nature of public response to tobacco prevention messages disseminated via a social media-based campaign. We also examine whether agreement with the prevention messages is associated with comment tone and nature of the contribution to the overall discussion.MethodsUser comments to tobacco prevention messages, posted between April 19, 2017 and July 12, 2017, were extracted from Twitter, Facebook, and Instagram. Two coders categorized comments in terms of tone, agreement with message, nature of contribution, mentions of government agency and regulation, promotional or spam comments, and format of comment. Chi-square analyses tested associations between agreement with the message and tone of the public response and the nature of contributions to the discussions.ResultsOf the 1242 comments received (Twitter: n=1004; Facebook: n=176; Instagram: n=62), many comments used a negative tone (42.75%) and disagreed with the health messages (39.77%), while the majority made healthy contributions to the discussions (84.38%). Only 0.56% of messages mentioned government agencies, and only 0.48% of the comments were antiregulation. Comments employing a positive tone (84.13%) or making healthy contributions (69.11%) were more likely to agree with the campaign messages (P=0.01). Comments employing a negative tone (71.25%) or making toxic contributions (36.26%) generally disagreed with the messages (P=0.01).ConclusionsThe majority of user comments in response to a tobacco prevention campaign made healthy contributions. Our findings encourage the use of social media to promote dialogue about controversial health topics such as smoking. However, toxicity was characteristic of comments that disagreed with the health messages. Managing negative and toxic comments on social media is a crucial issue for social media-based tobacco prevention campaigns to consider.

Project description:BACKGROUND:High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public's views on medical AI. Currently, the opinions of the general public on this matter remain unclear. OBJECTIVE:The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. METHODS:Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. RESULTS:Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. CONCLUSIONS:Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients' emotional needs instead of focusing merely on technical issues.

Project description:BackgroundPalliative and end-of-life care (PEoLC) played a critical role in relieving distress and providing grief support in response to the heavy toll caused by the COVID-19 pandemic. However, little is known about public opinions concerning PEoLC during the pandemic. Given that social media have the potential to collect real-time public opinions, an analysis of this evidence is vital to guide future policy-making.ObjectiveThis study aimed to use social media data to investigate real-time public opinions regarding PEoLC during the COVID-19 crisis and explore the impact of vaccination programs on public opinions about PEoLC.MethodsThis Twitter-based study explored tweets across 3 English-speaking countries: the United States, the United Kingdom, and Canada. From October 2020 to March 2021, a total of 7951 PEoLC-related tweets with geographic tags were retrieved and identified from a large-scale COVID-19 Twitter data set through the Twitter application programming interface. Topic modeling realized through a pointwise mutual information-based co-occurrence network and Louvain modularity was used to examine latent topics across the 3 countries and across 2 time periods (pre- and postvaccination program periods).ResultsCommonalities and regional differences among PEoLC topics in the United States, the United Kingdom, and Canada were identified specifically: cancer care and care facilities were of common interest to the public across the 3 countries during the pandemic; the public expressed positive attitudes toward the COVID-19 vaccine and highlighted the protection it affords to PEoLC professionals; and although Twitter users shared their personal experiences about PEoLC in the web-based community during the pandemic, this was more prominent in the United States and Canada. The implementation of the vaccination programs raised the profile of the vaccine discussion; however, this did not influence public opinions about PEoLC.ConclusionsPublic opinions on Twitter reflected a need for enhanced PEoLC services during the COVID-19 pandemic. The insignificant impact of the vaccination program on public discussion on social media indicated that public concerns regarding PEoLC continued to persist even after the vaccination efforts. Insights gleaned from public opinions regarding PEoLC could provide some clues for policy makers on how to ensure high-quality PEoLC during public health emergencies. In this post-COVID-19 era, PEoLC professionals may wish to continue to examine social media and learn from web-based public discussion how to ease the long-lasting trauma caused by this crisis and prepare for public health emergencies in the future. Besides, our results showed social media's potential in acting as an effective tool to reflect public opinions in the context of PEoLC.

Dataset Information

Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey.

Publications

Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey.

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