From theory to practice: insights and hurdles in collecting social media data for social science research

Social media has profoundly changed our modes of self-expression, communication, and participation in public discourse, generating volumes of conversations and content that cover every aspect of our social lives. Social media platforms have thus become increasingly important as data sources to identify social trends and phenomena. In recent years, academics have steadily lost ground on access to social media data as technology companies have set more restrictions on Application Programming Interfaces (APIs) or entirely closed public APIs. This circumstance halts the work of many social scientists who have used such data to study issues of public good. We considered the viability of eight approaches for image-based social media data collection: data philanthropy organizations, data repositories, data donation, third-party data companies, homegrown tools, and various web scraping tools and scripts. This paper discusses the advantages and challenges of these approaches from literature and from the authors' experience. We conclude the paper by discussing mechanisms for improving social media data collection that will enable this future frontier of social science research.

Ethical guidance for conducting health research with online communities: A scoping review of existing guidance

Online research methods have grown in popularity due in part to the globalised and far-reaching nature of the internet but also linked to the Covid-19 pandemic whereby restrictions to travel and face to face contact necessitated a shift in methods of research recruitment and data collection. Ethical guidance exists to support researchers in conducting online research, however this is lacking within health fields. This scoping review aims to synthesise formal ethical guidance for applying online methods within health research as well as provide examples of where guidance has been used. A systematic search of literature was conducted, restricted to English language records between 2013 and 2022. Eligibility focused on whether the records were providing ethical guidance or recommendations, were situated or relevant to health disciplines, and involved the use or discussion of online research methods. Following exclusion of ineligible records and duplicate removal, three organisational ethical guidance and 24 research papers were charted and thematically analysed. Four key themes were identified within the guidance documents, 1) consent, 2) confidentiality and privacy, 3) protecting participants from harm and 4) protecting researchers from harm with the research papers describing additional context and understanding around these issues. The review identified that there are currently no specific guidelines aimed at health researchers, with the most cited guidance coming from broader methodological perspectives and disciplines or auxiliary fields. All guidance discussed each of the four key themes within the wider context of sensitive topics and vulnerable populations, areas and issues which are often prominent within health research thus highlighting the need for unifying guidance specific for health researchers. Further research should aim to understand better how online health studies apply ethical principles, to support in informing gaps across both research and guidance.

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.

Disclosure Standards for Social Media and Generative Artificial Intelligence Research: Toward Transparency and Replicability

Social media dominate today's information ecosystem and provide valuable information for social research. Market researchers, social scientists, policymakers, government entities, public health researchers, and practitioners recognize the potential for social data to inspire innovation, support products and services, characterize public opinion, and guide decisions. The appeal of mining these rich datasets is clear. However, there is potential risk of data misuse, underscoring an equally huge and fundamental flaw in the research: there are no procedural standards and little transparency. Transparency across the processes of collecting and analyzing social media data is often limited due to proprietary algorithms. Spurious findings and biases introduced by artificial intelligence (AI) demonstrate the challenges this lack of transparency poses for research. Social media research remains a virtual "wild west," with no clear standards for reporting regarding data retrieval, preprocessing steps, analytic methods, or interpretation. Use of emerging generative AI technologies to augment social media analytics can undermine validity and replicability of findings, potentially turning this research into a "black box" enterprise. Clear guidance for social media analyses and reporting is needed to assure the quality of the resulting research. In this article, we propose criteria for evaluating the quality of studies using social media data, grounded in established scientific practice. We offer clear documentation guidelines to ensure that social data are used properly and transparently in research and applications. A checklist of disclosure elements to meet minimal reporting standards is proposed. These criteria will make it possible for scholars and practitioners to assess the quality, credibility, and comparability of research findings using digital data.

The Nexus Between Ethical Leadership and Employees' Perception of Workplace Safety During COVID-19 Under Mediation and Moderation Model

Background

Workplace safety is a crucial aspect of employee well-being and organizational success, with ethical leadership playing a key role in shaping employees' perceptions of safety. Today, the underlying mechanisms through which ethical leadership influences workplace safety perception remain underexplored, especially in the Pakistan healthcare industry. Based on the social cognitive theory, this study aims to investigate the relationship between ethical leadership and workplace safety perception and examine the mediating role of media quality, communication climate, and supervisory communication and the moderating role of moral attentiveness.

Methods

An empirical survey method was used to conduct the quantitative study, with respondents representing nursing staff from hospitals in Pakistan. Data was collected using an online questionnaire during COVID-19, and Smart PLS was used to analyze the data.

Results

The study demonstrated that ethical leadership positively and significantly affects workplace safety perception. Media quality, communication climate, and supervisory communication mediate between ethical leadership and workplace safety perception. Moral attentiveness moderates the relationship between ethical leadership and workplace safety perception.

Conclusion

Ethical leadership is an essential tool that improves media quality, communication climate, supervisory communication, and moral attentiveness. The article presents a novel approach to examining the relationship between ethical leadership and workers' safety perceptions under the influence of mediating and moderating variables. By better understanding these dynamics, the study contributes to developing organizational strategies to improve workplace safety and overall employee well-being. In addition, it is a pioneering study exploring ethical leadership's role in influencing workers' perceptions of safety. Overall, the study is a great initiative that fosters the ethical concepts of individuals, thus achieving health protection and safety.

From existential anxiety to posttraumatic growth:: The stranded traveler during the pandemic outbreak

For many, the COVID-19 pandemic is an existential trauma. Existential traumas trigger people's ultimate concerns and shatter individuals' basic assumptions. Based on the perspective of existential positive psychology, this study explores the posttraumatic growth of travelers from Hubei, China, during the early outbreak period. Grounded theory is used to analyze the stranded experience of epicenter travelers during the sudden crisis. This study reveals the intertwined traumatic events that evoke existential anxiety in travelers, as well as five dimensions of posttraumatic growth. Moreover, a theoretical framework is presented, elaborating on the influence path of key factors such as social support, cognition, and coping strategies on posttraumatic growth. Finally, the finding shows a strong resonance between posttraumatic growth and transformative tourism experiences.

Computational analyses identify addiction help-seeking behaviors on the social networking website Reddit: Insights into online social interactions and addiction support communities

INTRODUCTION

Although social connection to others with lived addiction experiences is a strong predictor of long-term recovery from substance use disorders (SUD), the COVID-19 pandemic greatly altered global abilities to physically connect with other people. Evidence suggests online forums for people with SUD may serve as a sufficient proxy for social connection, however efficacy of online spaces as addiction treatment adjuncts remains empirically understudied.

PURPOSE

The purpose of this study is to analyze a collection of Reddit posts germane to addiction and recovery collected between March-August 2022.

METHODS

We collected (n = 9,066) Reddit posts (1) r/addiction; (2) r/DecidingToBeBetter, (3) r/SelfImprovement, (4) r/OpitatesRecovery, (5) r/StopSpeeding, (6) r/RedditorsInRecovery, and (7) r/StopSmoking subreddits. We applied several classes of natural language processing (NLP) methods to analyze and visualize our data including term frequency inverse document frequency (TF-IDF) calculations, k-means clustering, and principal components analysis (PCA). We also applied a Valence Aware Dictional and sEntiment [sic] Reasoner (VADER) sentiment analysis to determine affect in our data.

RESULTS

Our analyses revealed three distinct clusters: (1) Personal addiction struggle, or sharing one's recovery journey (n = 2,520), (2) Giving advice, or offering counseling based on first-hand experiences (n = 3,885), and (3) Seeking advice, or asking for support or advice related to addiction (n = 2,661).

DISCUSSION & CONCLUSION

Addiction, SUD, and recovery dialogue on Reddit is exceedingly robust. Much of the content mirrors tenets for established addiction-recovery programs, which suggests Reddit, and other social networking websites, may serve as efficient tools to promote social connection among people with SUD.

"You Are By No Means Alone": A Netnographic Study of Self-Care Support in an Online Community for Older Adults

Online peer-to-peer communities provide environments in which people with similar health concerns can interact and exchange information that can support self-care of long-term conditions. However, current theories have not adequately accounted for how self-care support is enacted in online communities. We conducted an observational netnography to identify and analyze posts in a publicly accessible online community (discussion forum boards) designed for older people. A Straussian grounded theory approach was used to examine 659 posts in health-related message boards. Self-care support involved the construction of three interrelated identities: (1) the support seeking self, in which members described problems and requested information; (2) the empathizing self, in which they described similar experiences and offered support; and (3) the influencing self, in which they provided information or advice. Online communities appear to be an important source of peer support and information and may be a cost-effective approach to supplement standard care.

Discourses Around Male IPV Related Systemic Biases on Reddit

To date research on intimate partner violence (IPV) has focused on the experience of females. The limited studies on male IPV survivors have shown that they are less likely to disclose their IPV experiences. Systemic biases may marginalize and silence male IPV survivors.The current study sought to explore the discourse around perceived systemic biases that may be present for male IPV survivors.A widely used social networking site (http://www.reddit.com/) was scraped for submissions relating to male IPV. Search was carried out using three keywords resulting in 917 submissions, out of which 82 met inclusion criteria. Submissions were included in final analysis if they consisted of more than half a page of data pertaining to male IPV. Thematic content analysis was utilized to analyze the data.Responses reflect common experiences with participants identifying multiple sources of perceived systemic biases: (1) social norms, (2) legal system, (3) social services, (4) media, and (5) government.The sources of potential support for male IPV survivors exhibit substantial pervasive biases against males as victims of IPV. Findings from current study can inform policies across multiple systems.

Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults

Background:

Artificial Intelligence is making a significant transformation in human lives. Its application in the medical and healthcare field has been also observed making an impact and improving overall outcomes. There has been a quest for similar processes in mental health due to the lack of observable changes in the areas of suicide prevention. In the last five years, there has been an emerging body of empirical research applying the technology of artificial intelligence (AI) and machine learning (ML) in mental health.

Objective:

To review the clinical applicability of the AI/ML-based tools in suicide prevention.

Methods:

The compelling question of predicting suicidality has been the focus of this research. We performed a broad literature search and then identified 36 articles relevant to meet the objectives of this review. We review the available evidence and provide a brief overview of the advances in this field.

Conclusion:

In the last five years, there has been more evidence supporting the implementation of these algorithms in clinical practice. Its current clinical utility is limited to using electronic health records and could be highly effective in conjunction with existing tools for suicide prevention. Other potential sources of relevant data include smart devices and social network sites. There are some serious questions about data privacy and ethics which need more attention while developing these new modalities in suicide research.

Research Ethical Norms, Guidance and the Internet

The internet, either as a tool or as an area of research, adds moral worries to an already complicated research ethical backdrop. Agencies, professional associations and philosophers have formulated research ethical norms designed to help scientists to arrive at responsible solutions to the problems. Yet, many criticize this reliance on norms. Somewhat more precisely, many claim that research ethical norms do not offer guidance. In the literature at least three arguments to that effect can be found. First, the research ethical norms fail to guide since they are inconsistent. Second, they fail to guide since they are too opaque. Third, they fail to guide since they cannot handle the moral complexity of issues scientists doing e-research face. In this paper I argue that these arguments are weak. The arguments are, in their original formulations, rather unclear. I try to improve the situation by spelling out the arguments with reference to a certain set of research ethical norms, to a certain account of action-guidance and with reference to certain important distinctions. It then turns out that the arguments' premises are either untenable or suffers from lack of relevance. The arguments do not force us to conclude that research ethical norms fail to guide.

Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis

Objective: To compare the findings from a qualitative and a natural language processing (NLP) based analysis of online patient experience posts on patient experience of the effectiveness and impact of the drug Modafinil.

Methods: Posts (n = 260) from 5 online social media platforms where posts were publicly available formed the dataset/corpus. Three platforms asked posters to give a numerical rating of Modafinil. Thematic analysis: data was coded and themes generated. Data were categorized into PreModafinil, Acquisition, Dosage, and PostModafinil and compared to identify each poster's own view of whether taking Modafinil was linked to an identifiable outcome. We classified this as positive, mixed, negative, or neutral and compared this with numerical ratings. NLP: Corpus text was speech tagged and keywords and key terms extracted. We identified the following entities: drug names, condition names, symptoms, actions, and side-effects. We searched for simple relationships, collocations, and co-occurrences of entities. To identify causal text, we split the corpus into PreModafinil and PostModafinil and used n-gram analysis. To evaluate sentiment, we calculated the polarity of each post between −1 (negative) and +1 (positive). NLP results were mapped to qualitative results.

Results: Posters had used Modafinil for 33 different primary conditions. Eight themes were identified: the reason for taking (condition or symptom), impact of symptoms, acquisition, dosage, side effects, other interventions tried or compared to, effectiveness of Modafinil, and quality of life outcomes. Posters reported perceived effectiveness as follows: 68% positive, 12% mixed, 18% negative. Our classification was consistent with poster ratings. Of the most frequent 100 keywords/keyterms identified by term extraction 88/100 keywords and 84/100 keyterms mapped directly to the eight themes. Seven keyterms indicated negation and temporal states. Sentiment was as follows 72% positive sentiment 4% neutral 24% negative. Matching of sentiment between the qualitative and NLP methods was accurate in 64.2% of posts. If we allow for one category difference matching was accurate in 85% of posts.

Conclusions: User generated patient experience is a rich resource for evaluating real world effectiveness, understanding patient perspectives, and identifying research gaps. Both methods successfully identified the entities and topics contained in the posts. In contrast to current evidence, posters with a wide range of other conditions found Modafinil effective. Perceived causality and effectiveness were identified by both methods demonstrating the potential to augment existing knowledge.

#MeToo Online Disclosures: A Survivor-Informed Approach to Open Science Practices and Ethical Use of Social Media Data

With social media data widely available, researchers are increasingly incorporating tweets, posts, and blogs in their work. While easily accessible, the use of “public” posts raises important questions about the ethics of mining, storing, analyzing, and reporting publicly available social media data—especially when gathering sensitive information such as sexual violence disclosures. Online movements including #MeToo and #WhyIDidntReport emerged to shed light on gender-based violence. These movements generate large quantities of data with little consistency and oversight across research groups, disciplines, and review boards on data ethics. With the recent push in social science to publish data to open science databases, the concerns of feminist psychologists and ethical concerns of social media research with survivors have become more salient. In this article, we describe and address these ethical issues by reviewing existing social media sexual assault disclosure research and make concrete recommendations for authors seeking to use social media data. We also seek to address these ethical concerns by noting the work of some feminist researchers, but also by pushing researchers to do more for survivors through a survivor-informed approach to this research in an open science context. We propose four survivor-informed recommendations for research with this vulnerable population: (a) get input from survivors, (b) update ethics review boards, (c) maximize benefits to participants, and (d) utilize study-appropriate datasets with informed consent.

Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews

We assess the ability of online employee-generated content in predicting consumption expenditures. In so doing, we aggregate millions of employee expectations for the next six-month business outlook of their employer and build an employee sentiment index. We test whether forward-looking employee sentiment can contribute to baseline models when forecasting aggregate consumption in the United States and compare its performance to well-established, survey-based consumer sentiment indexes. We reveal that online employee opinions have incremental information that can be used to augment the accuracy of consumption forecasting models and inform economic policy decisions.

Scoping Review: Ethical Considerations in Online Ethnographic Research With Military Populations

Researchers using online ethnographic methods to study military communities must employ higher standards of ethical practice. Military populations may face significant risk if reidentified in research. These requirements are especially salient for online data collection. This review questions how and to what extent military online ethnographers are addressing ethics considerations. We charted evidence from seven military studies using an online ethnographic method. Findings reveal that most online military ethnographers did not utilize sufficient ethical safeguards in their studies. Additionally, they did not document or transparently disclose the ethical steps they may have taken. This study argues implementing ethical safeguards is especially important for protecting vulnerable military populations. We present a strategy for evaluating ethics practices in online ethnographic research and provide best practices for military online ethnographers.

Public Perspectives of Using Social Media Data to Improve Adverse Drug Reaction Reporting: A Mixed-Methods Study

Introduction

Information on suspected adverse drug reactions (ADRs) voluntarily submitted by patients can be a valuable source of information for improving drug safety; however, public awareness of reporting mechanisms remains low. Whilst methods to automatically detect ADR mentions from social media posts using text mining techniques have been proposed to improve reporting rates, it is unclear how acceptable these would be to social media users.

Objective

The objective of this study was to explore public opinion about using automated methods to detect and report mentions of ADRs on social media to enhance pharmacovigilance efforts.

Methods

Users of the online health discussion forum HealthUnlocked participated in an online survey (N = 1359) about experiences with ADRs, knowledge of pharmacovigilance methods, and opinions about using automated data mining methods to detect and report ADRs. To further explore responses, five qualitative focus groups were conducted with 20 social media users with long-term health conditions.

Results

Participant responses indicated a low awareness of pharmacovigilance methods and ADR reporting. They showed a strong willingness to share health-related social media data about ADRs with researchers and regulators, but were cautious about automated text mining methods of detecting and reporting ADRs.

Conclusions

Social media users value public-facing pharmacovigilance schemes, even if they do not understand the current framework of pharmacovigilance within the UK. Ongoing engagement with users is essential to understand views, share knowledge and respect users’ privacy expectations to optimise future ADR reporting from online health communities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40264-021-01042-6.

Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review

Background: Text-mining techniques are advancing all the time and vast corpora of social media text can be analyzed for users' views and experiences related to their health. There is great promise for new insights into health issues such as drug side effects and spread of disease, as well as patient experiences of health conditions and health care. However, this emerging field lacks ethical consensus and guidance. We aimed to bring together a comprehensive body of opinion, views, and recommendations in this area so that academic researchers new to the field can understand relevant ethical issues. Methods: After registration of a protocol in PROSPERO, three parallel systematic searches were conducted, to identify academic articles comprising commentaries, opinion, and recommendations on ethical practice in social media text mining for health research and gray literature guidelines and recommendations. These were integrated with social media users' views from qualitative studies. Papers and reports that met the inclusion criteria were analyzed thematically to identify key themes, and an overarching set of themes was deduced. Results: A total of 47 reports and articles were reviewed, and eight themes were identified. Commentators suggested that publicly posted social media data could be used without consent and formal research ethics approval, provided that the anonymity of users is ensured, although we note that privacy settings are difficult for users to navigate on some sites. Even without the need for formal approvals, we note ethical issues: to actively identify and minimize possible harms, to conduct research for public benefit rather than private gain, to ensure transparency and quality of data access and analysis methods, and to abide by the law and terms and conditions of social media sites. Conclusion: Although social media text mining can often legally and reasonably proceed without formal ethics approvals, we recommend improving ethical standards in health-related research by increasing transparency of the purpose of research, data access, and analysis methods; consultation with social media users and target groups to identify and mitigate against potential harms that could arise; and ensuring the anonymity of social media users.

Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours

OBJECTIVE

Social media analytics (SMA) has a track record in business research. The utilisation in nutrition research is unknown, despite social media being populated with real-time eating behaviours. This rapid review aimed to explore the use of SMA in nutrition research with the investigation of dietary behaviours.

DESIGN

The review was conducted according to rapid review guidelines by WHO and the National Collaborating Centre for Methods and Tools. Five databases of peer-reviewed, English language studies were searched using the keywords 'social media' in combination with 'data analytics' and 'food' or 'nutrition' and screened for those with general population health using SMA on public domain, social media data between 2014 and 2020.

RESULTS

The review identified 34 studies involving SMA in the investigation of dietary behaviours. Nutrition topics included population nutrition health investigations, alcohol consumption, dieting and eating out of the home behaviours. All studies involved content analysis with evidence of surveillance and engagement. Twitter was predominant with data sets in tens of millions. SMA tools were observed in data discovery, collection and preparation, but less so in data analysis. Approximately, a third of the studies involved interdisciplinary collaborations with health representation and only two studies involved nutrition disciplines. Less than a quarter of studies obtained formal human ethics approval.

CONCLUSIONS

SMA in nutrition research with the investigation of dietary behaviours is emerging, nevertheless, if consideration is taken with technological capabilities and ethical integrity, the future shows promise at a broad population census level and as a scoping tool or complementary, triangulation instrument.

Peer-to-Peer Health Communication in Older Adults' Online Communities: Protocol for a Qualitative Netnographic Study and Co-Design Approach

Background

Online communities provide an environment in which people with similar health concerns can interact and access content that can support the self-management of long-term conditions (LTCs). Recently, the importance of online social networks as sources of health information and social support has been brought into focus with the emergence and widespread societal impacts of COVID-19. Although online communities exist for older adults, little is known about the specific health and self-care topics that older people discuss in such environments and how these relate to users’ support needs and outcomes. A better understanding of users’ needs and peer-to-peer communication in these communities is necessary to inform the design of information and communication technology (ICT) interventions that are relevant to older people and their peer supporters.

Objective

This study aims to use a two-phase, web-based ethnographic (netnography) and co-design approach to explore specific health care and self-care topics that older adults discuss in a UK-based online community and how peer supporters respond to these queries with informational and/or social support and engage with stakeholders to define the needs and requirements for new ICT-based interventions capable of reducing social isolation and facilitating LTC self-management support.

Methods

The first phase of the research will involve a qualitative netnographic analysis of posts in discussion forums in a publicly accessible online community. The second phase will involve co-design workshops with health care consumers (ie, older adults and carers) and service providers to determine the needs and requirements for new ICT-based interventions and digital innovations. Constructivist grounded theory will be used in the first phase; in the second phase, the co-design workshops will be audiorecorded and analyzed thematically.

Results

This research project is in progress. Permission was obtained from the website administrator to use materials from the social media forum; data collection for the first phase began in April 2020. The second phase of the study is expected to begin in late 2020. This study is due to be completed by the end of 2021.

Conclusions

This study is the first, to the best of our knowledge, to combine qualitative netnography with an iterative co-design framework to specify the needs and requirements for new ICT-based interventions. The findings from this study will inform the next phase of the multiphase knowledge translation project and will provide insights into the potential of online peer health communities to reduce social isolation and facilitate chronic illness self-management support and self-care.

International Registered Report Identifier (IRRID)

PRR1-10.2196/19834

Coping by metaphors: the versatile function of metaphors in blogs about living with advanced cancer

Living with a life-limiting cancer illness can entail a turmoil of feelings such as constant fear of loss, suffering and dying. Because patients live longer with life-limiting illness, there is a need for enhanced understanding of how people make sense of and cope with the complicated aspects that this life situation brings on. In this article, we explore how bloggers with advanced cancer use metaphors as ways of making sense of their experiences. Our study is theoretically grounded in Conceptual Metaphor Theory, where metaphors are seen as a powerful phenomenon that both reflects and affects our thinking. The data consist of a corpus of blogs written in Swedish by individuals with advanced cancer, and the findings from our linguistic metaphor analysis are consistently interpreted against the backdrop of literature on coping. Our study thus highlights the intersection of linguistic metaphor analysis and psychological theories of coping by illustrating the many and complex functions metaphors can have as part of sense-making processes. Our hermeneutic approach enables us to show some differences among the three most pervasive metaphor domains in our material, battle, journey and imprisonment: the journey and imprisonment domains are more flexible than the battle domain in terms of the different kinds of coping strategies that are actualised by the bloggers' use of metaphors. One particular finding from our analysis is the way in which the bloggers make use of metaphors to compartmentalise experiences and emotions. Our contention is that careful attention to the metaphors used by patients can improve communication in healthcare and enhance understanding of the complex role language use plays in coping processes more generally. By highlighting the relation between metaphor use and coping, our analysis also provides a way to discuss coping strategies based on the patient's own use of language.

The Ethics Ecosystem: Personal Ethics, Network Governance and Regulating Actors Governing the Use of Social Media Research Data

This paper examines the consequences of a culture of "personal ethics" when using new methodologies, such as the use of social media (SM) sites as a source of data for research. Using SM research as an example, this paper explores the practices of a number of actors and researchers within the "Ethics Ecosystem" which as a network governs ethically responsible research behaviour. In the case of SM research, the ethical use of this data is currently in dispute, as even though it is seemingly publically available, concerns relating to privacy, vulnerability, potential harm and consent blur the lines of responsible ethical research behaviour. The findings point to the dominance of a personal, bottom-up, researcher-led, 'ethical barometer' for making decisions regarding the permissibility of using SM data. We show that the use of different barometers by different researchers can lead to wide disparities in ethical practice - disparities which are compounded by the lack of firm guidelines for responsible practice of SM research. This has widespread consequences on the development of shared norms and understandings at all levels, and by all actors within the Ethics Ecosystem, and risks inconsistencies in their approaches to ethical decision-making. This paper argues that this governance of ethical behaviour by individual researchers perpetuates a negative cycle of academic practice that is dependent on subjective judgements by researchers themselves, rather than governed by more formalised academic institutions such as the research ethics committee and funding council guidelines.

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

Digital Trespass: Ethical and Terms-of-Use Violations by Researchers Accessing Data From an Online Patient Community

With the expansion and popularity of research on websites such as Facebook and Twitter, there has been increasing concern about investigator conduct and social media ethics. The availability of large data sets has attracted researchers who are not traditionally associated with health data and its associated ethical considerations, such as computer and data scientists. Reliance on oversight by ethics review boards is inadequate and, due to the public availability of social media data, there is often confusion between public and private spaces. In addition, social media participants and researchers may pay little attention to traditional terms of use. In this paper, we review four cases involving ethical and terms-of-use violations by researchers seeking to conduct social media studies in an online patient research network. These violations involved unauthorized scraping of social media data, entry of false information, misrepresentation of researcher identities of participants on forums, lack of ethical approval and informed consent, use of member quotations, and presentation of findings at conferences and in journals without verifying accurate potential biases and limitations of the data. The correction of these ethical lapses often involves much effort in detecting and responding to violators, addressing these lapses with members of an online community, and correcting inaccuracies in the literature (including retraction of publications and conference presentations). Despite these corrective actions, we do not regard these episodes solely as violations. Instead, they represent broader ethical issues that may arise from potential sources of confusion, misinformation, inadequacies in applying traditional informed consent procedures to social media research, and differences in ethics training and scientific methodology across research disciplines. Social media research stakeholders need to assure participants that their studies will not compromise anonymity or lead to harmful outcomes while preserving the societal value of their health-related studies. Based on our experience and published recommendations by social media researchers, we offer potential directions for future prevention-oriented measures that can be applied by data producers, computer/data scientists, institutional review boards, research ethics committees, and publishers.

Comprehensive scoping review of health research using social media data

INTRODUCTION

The rising popularity of social media, since their inception around 20 years ago, has been echoed in the growth of health-related research using data derived from them. This has created a demand for literature reviews to synthesise this emerging evidence base and inform future activities. Existing reviews tend to be narrow in scope, with limited consideration of the different types of data, analytical methods and ethical issues involved. There has also been a tendency for research to be siloed within different academic communities (eg, computer science, public health), hindering knowledge translation. To address these limitations, we will undertake a comprehensive scoping review, to systematically capture the broad corpus of published, health-related research based on social media data. Here, we present the review protocol and the pilot analyses used to inform it.

METHODS

A version of Arksey and O'Malley's five-stage scoping review framework will be followed: (1) identifying the research question; (2) identifying the relevant literature; (3) selecting the studies; (4) charting the data and (5) collating, summarising and reporting the results. To inform the search strategy, we developed an inclusive list of keyword combinations related to social media, health and relevant methodologies. The frequency and variability of terms were charted over time and cross referenced with significant events, such as the advent of Twitter. Five leading health, informatics, business and cross-disciplinary databases will be searched: PubMed, Scopus, Association of Computer Machinery, Institute of Electrical and Electronics Engineers and Applied Social Sciences Index and Abstracts, alongside the Google search engine. There will be no restriction by date.

ETHICS AND DISSEMINATION

The review focuses on published research in the public domain therefore no ethics approval is required. The completed review will be submitted for publication to a peer-reviewed, interdisciplinary open access journal, and conferences on public health and digital research.

#Deathbedlive: the end-of-life trajectory, reflected in a cancer patient's tweets

Background

Understanding physical and psycho-social illness trajectories towards the end of life can help in the planning of palliative and supportive care. With terminal patients increasingly seeking and sharing health information and support via social media, it is timely to examine whether these trajectories are reflected in their digital narratives. In this exploratory study, we analysed the Twitter feed of prominent cancer sufferer and physician, Kate Granger, over the final 6 months of her life.

Methods

With the consent of Kate’s widower, Chris Pointon, 1628 Twitter posts from @GrangerKate were manually screened. The 550 tweets judged relevant to her disease were qualitatively content analysed with reference to the six modifiable dimensions of the patient experience in Emanuel and Emanuel’s ‘framework for a good death’. The frequency of each tweet category was charted over time and textual content was examined and cross-referenced with key events, to obtain a deeper understanding of its nature and significance.

Results

Tweets were associated with physical symptoms (N = 270), psychological and cognitive symptoms (N = 213), social relationships and support (N = 85), economic demands and care giving needs (N = 85), hopes and expectations (N = 51) and spiritual beliefs (N = 7). While medical treatments and procedures were discussed in detail, medical information-seeking was largely absent, likely reflecting Kate clinical expertise. Spirituality was expressed more as hope in treatments or “someone out there listening”, than in religious terms. The high value of Kate’s palliative care team was a dominant theme in the support category, alongside the support she received from her online community of fellow sufferers, friends, family and colleagues. Significant events, such as medical procedures and hospital stays generated the densest Twitter engagement. Transitions between trajectory phases were marked by changes in the relative frequency of tweet-types.

Conclusions

In Kate’s words, “the power of patient narrative cannot be underestimated”. While this analysis spanned only 6 months, it yielded rich insights. The results reflect theorised end-of-life dimensions and reveal the potential of social media data and digital bio-ethnography to shine a light on terminal patients’ lived experiences, coping strategies and support needs, suggesting new opportunities for enhancing personalised palliative care and avenues for further research.