Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study

Twitter-based crowdsourcing: What kind of measures can help to end the COVID-19 pandemic faster?

Background

Crowdsourcing is a low-cost, adaptable, and innovative method to collect ideas from numerous contributors with diverse backgrounds. Crowdsourcing from social media like Twitter can be used for generating ideas in a noticeably brief time based on contributions from globally distributed users. The world has been challenged by the COVID-19 pandemic in the last several years. Measures to combat the pandemic continue to evolve worldwide, and ideas and opinions on optimal counteraction strategies are of high interest.

Objective

This study aimed to validate the use of Twitter as a crowdsourcing platform in order to gain an understanding of public opinion on what measures can help to end the COVID-19 pandemic faster.

Methods

This cross-sectional study was conducted during the period from December 22, 2021, to February 4, 2022. Tweets were posted by accounts operated by the authors, asking “How to faster end the COVID-19 pandemic?” and encouraging the viewers to comment on measures that they perceive would be effective to achieve this goal. The ideas from the users' comments were collected and categorized into two major themes – personal and institutional measures. In the final stage of the campaign, a Twitter poll was conducted to get additional comments and to estimate which of the two groups of measures were perceived to be important amongst Twitter users.

Results

The crowdsourcing campaign generated seventeen suggested measures categorized into two major themes (personal and institutional) that received a total of 1,727 endorsements (supporting comments, retweets, and likes). The poll received a total of 325 votes with 58% of votes underscoring the importance of both personal and institutional measures, 20% favoring personal measures, 11% favoring institutional measures, and 11% of the votes given just out of curiosity to see the vote results.

Conclusions

Twitter was utilized successfully for crowdsourcing ideas on strategies how to end the COVID-19 pandemic faster. The results indicate that the Twitter community highly values the significance of both personal responsibility and institutional measures to counteract the pandemic. This study validates the use of Twitter as a primary tool that could be used for crowdsourcing ideas with healthcare significance.

Effects of Social Media Usage on Consumers' Purchase Intention in Social Commerce: A Cross-Cultural Empirical Analysis

Social commerce has produced enormous economic benefits as well as challenges for organizations, individuals, and industries. However, social media usage does not necessarily generate users' intention to purchase on social commerce websites. How social media usage influences users' purchase intention on social commerce websites still deserves more scholarly attention and this seems particularly important when social commerce transcends borders and countries. Taking an interdisciplinary perspective, the current study adopted a survey research method and identified the roles of social media usage in arousing users' purchase intention on social commerce websites in a culturally diversified environment. The data was collected from 2,058 international students coming from 135 countries and was analyzed using MPLUS based structural equation modeling. The research unveils the pathway whereby social media usage serves to generate users' purchase intention on social commerce websites. Importantly, users' cultural intelligence has been found to play a significant role mediating the effects of social media usage on users' intention. Further, cultural distance was found to attenuate the effects of social media usage on cultural intelligence. Based on the research findings, the study suggests that social commerce practitioners should be fully aware of the enabling roles of social media and cultural intelligence as well as the deterring role of cultural distance when arousing customers' purchasing intention in cross-cultural business operations. Any measures facilitated by social media usage to improve international consumers' cultural intelligence and mitigate the negative effects of cultural distance are supposed to be effective to enhance their purchasing intention. Accordingly, the study confirms the mutually melt and integrative relationships between information technology advancement and business prosperity in cross-cultural environment, which eventually contribute to sustainable development of society.

Ethical Issues in Social Media Recruitment for Clinical Studies: Ethical Analysis and Framework

BACKGROUND

Social media recruitment for clinical studies holds the promise of being a cost-effective way of attracting traditionally marginalized populations and promoting patient engagement with researchers and a particular study. However, using social media for recruiting clinical study participants also poses a range of ethical issues.

OBJECTIVE

This study aims to provide a comprehensive overview of the ethical benefits and risks to be considered for social media recruitment in clinical studies and develop practical recommendations on how to implement these considerations.

METHODS

On the basis of established principles of clinical ethics and research ethics, we reviewed the conceptual and empirical literature for ethical benefits and challenges related to social media recruitment. From these, we derived a conceptual framework to evaluate the eligibility of social media use for recruitment for a specific clinical study.

RESULTS

We identified three eligibility criteria for social media recruitment for clinical studies: information and consent, risks for target groups, and recruitment effectiveness. These criteria can be used to evaluate the implementation of a social media recruitment strategy at its planning stage. We have discussed the practical implications of these criteria for researchers.

CONCLUSIONS

The ethical challenges related to social media recruitment are context sensitive. Therefore, social media recruitment should be planned rigorously, taking into account the target group, the appropriateness of social media as a recruitment channel, and the resources available to execute the strategy.

Toward Using Twitter for PrEP-Related Interventions: An Automated Natural Language Processing Pipeline for Identifying Gay or Bisexual Men in the United States

BACKGROUND

Pre-exposure prophylaxis (PrEP) is highly effective at preventing the acquisition of HIV. There is a substantial gap, however, between the number of people in the United States who have indications for PrEP and the number of them who are prescribed PrEP. Although Twitter content has been analyzed as a source of PrEP-related data (eg, barriers), methods have not been developed to enable the use of Twitter as a platform for implementing PrEP-related interventions.

OBJECTIVE

Men who have sex with men (MSM) are the population most affected by HIV in the United States. Therefore, the objectives of this study were to (1) develop an automated natural language processing (NLP) pipeline for identifying men in the United States who have reported on Twitter that they are gay, bisexual, or MSM and (2) assess the extent to which they demographically represent MSM in the United States with new HIV diagnoses.

METHODS

Between September 2020 and January 2021, we used the Twitter Streaming Application Programming Interface (API) to collect more than 3 million tweets containing keywords that men may include in posts reporting that they are gay, bisexual, or MSM. We deployed handwritten, high-precision regular expressions-designed to filter out noise and identify actual self-reports-on the tweets and their user profile metadata. We identified 10,043 unique users geolocated in the United States and drew upon a validated NLP tool to automatically identify their ages.

RESULTS

By manually distinguishing true- and false-positive self-reports in the tweets or profiles of 1000 (10%) of the 10,043 users identified by our automated pipeline, we established that our pipeline has a precision of 0.85. Among the 8756 users for which a US state-level geolocation was detected, 5096 (58.2%) were in the 10 states with the highest numbers of new HIV diagnoses. Among the 6240 users for which a county-level geolocation was detected, 4252 (68.1%) were in counties or states considered priority jurisdictions by the Ending the HIV Epidemic initiative. Furthermore, the age distribution of the users reflected that of MSM in the United States with new HIV diagnoses.

CONCLUSIONS

Our automated NLP pipeline can be used to identify MSM in the United States who may be at risk of acquiring HIV, laying the groundwork for using Twitter on a large scale to directly target PrEP-related interventions at this population.

Privacy concerns with using public data for suicide risk prediction algorithms: a public opinion survey of contextual appropriateness

Purpose

Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms.

Design/methodology/approach

A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose.

Findings

The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic.

Originality/value

Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question.

Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study

BACKGROUND

Failure to find and attract clinical trial participants remains a persistent barrier to clinical research. Researchers increasingly complement recruitment methods with social media-based methods. We hypothesized that user-generated data from cancer survivors and their family members and friends on the social network Twitter could be used to identify, engage, and recruit cancer survivors for cancer trials.

OBJECTIVE

This pilot study aims to examine the feasibility of using user-reported health data from cancer survivors and family members and friends on Twitter in Los Angeles (LA) County to enhance clinical trial recruitment. We focus on 6 cancer conditions (breast cancer, colon cancer, kidney cancer, lymphoma, lung cancer, and prostate cancer).

METHODS

The social media intervention involved monitoring cancer-specific posts about the 6 cancer conditions by Twitter users in LA County to identify cancer survivors and their family members and friends and contacting eligible Twitter users with information about open cancer trials at the University of Southern California (USC) Norris Comprehensive Cancer Center. We reviewed both retrospective and prospective data published by Twitter users in LA County between July 28, 2017, and November 29, 2018. The study enrolled 124 open clinical trials at USC Norris. We used descriptive statistics to report the proportion of Twitter users who were identified, engaged, and enrolled.

RESULTS

We analyzed 107,424 Twitter posts in English by 25,032 unique Twitter users in LA County for the 6 cancer conditions. We identified and contacted 1.73% (434/25,032) of eligible Twitter users (127/434, 29.3% cancer survivors; 305/434, 70.3% family members and friends; and 2/434, 0.5% Twitter users were excluded). Of them, 51.4% (223/434) were female and approximately one-third were male. About one-fifth were people of color, whereas most of them were White. Approximately one-fifth (85/434, 19.6%) engaged with the outreach messages (cancer survivors: 33/85, 38% and family members and friends: 52/85, 61%). Of those who engaged with the messages, one-fourth were male, the majority were female, and approximately one-fifth were people of color, whereas the majority were White. Approximately 12% (10/85) of the contacted users requested more information and 40% (4/10) set up a prescreening. Two eligible candidates were transferred to USC Norris for further screening, but neither was enrolled.

CONCLUSIONS

Our findings demonstrate the potential of identifying and engaging cancer survivors and their family members and friends on Twitter. Optimization of downstream recruitment efforts such as screening for digital populations on social media may be required. Future research could test the feasibility of the approach for other diseases, locations, languages, social media platforms, and types of research involvement (eg, survey research). Computer science methods could help to scale up the analysis of larger data sets to support more rigorous testing of the intervention.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561.

Data Curation Implications of Qualitative Data Reuse and Big Social Research

Objective: Big social data (such as social media and blogs) and archived qualitative data (such as interview transcripts, field notebooks, and diaries) are similar, but their respective communities of practice are under-connected. This paper explores shared challenges in qualitative data reuse and big social research and identifies implications for data curation. Methods: This paper uses a broad literature search and inductive coding of 300 articles relating to qualitative data reuse and big social research. The literature review produces six key challenges relating to data use and reuse that are present in both qualitative data reuse and big social research—context, data quality, data comparability, informed consent, privacy & confidentiality, and intellectual property & data ownership. Results: This paper explores six key challenges related to data use and reuse for qualitative data and big social research and discusses their implications for data curation practices. Conclusions: Data curators can benefit from understanding these six key challenges and examining data curation implications. Data curation implications from these challenges include strategies for: providing clear documentation; linking and combining datasets; supporting trustworthy repositories; using and advocating for metadata standards; discussing alternative consent strategies with researchers and IRBs; understanding and supporting deidentification challenges; supporting restricted access for data; creating data use agreements; supporting rights management and data licensing; developing and supporting alternative archiving strategies. Considering these data curation implications will help data curators support sounder practices for both qualitative data reuse and big social research.

Social Media, Research, and Ethics: Does Participant Willingness Matter?

Objective : To summarise the state of the art published in 2019 in consumer health informatics and education, with a special emphasis on “Ethics and Health Informatics”.

Methods : We conducted a systematic search of articles published in PubMed using a predefined set of queries, which identified 368 potential articles for review. These articles were screened according to topic relevance and 15 were selected for consideration of best paper candidates, which were then presented to a panel of international experts for full paper review and scoring. The top five papers according to the external reviewers’ ranking were discussed in a consensus meeting. Finally, the paper that received the highest score from four of the five experts was selected as the best paper on social media and ethics for patients and consumers of the year 2019.

Results : Despite using the terms “ethics” and “ethical” in the search query, we retrieved very few articles. The bibliometric analysis identified three major clusters centred on “social”, “health”, and “study”. Among the top five papers, one was a review where the authors identified ethical issues across four areas at the intersection of social media and health: 1) the impact of social networking sites on the doctor-patient relationship; 2) the development of e-health platforms to deliver care; 3) the use of online data and algorithms to inform health research; and 4) the broader public health consequences of widespread social media use. The other papers highlighted ethical concerns in using social media to interact with patients at different phases of a clinical research protocol, such as recruitment phase, participant engagement, data linkage, and detection and monitoring of adverse events.

Conclusions : Findings suggest that most users do not think that using social media for patient monitoring in clinical research, for example using Twitter for clinical trial recruitment, constitutes inappropriate surveillance or a violation of privacy. However, further research is needed to identify whether and how views on ethical concerns differed between social media platforms and across populations.

Human-Computer Interaction, Ethics, and Biomedical Informatics

Objectives : To provide an overview of recent work at the intersection of Biomedical Informatics, Human-Computer Interaction, and Ethics.

Methods : Search terms for Human-Computer Interaction, Biomedical Informatics, and Ethics were used to identify relevant papers published between 2017 and 2019.Relevant papers were identified through multiple methods, including database searches, manual reviews of citations, recent publications, and special collections, as well as through peer recommendations. Identified articles were reviewed and organized into broad themes.

Results : We identified relevant papers at the intersection of Biomedical Informatics, Human-Computer Interactions, and Ethics in over a dozen journals. The content of these papers was organized into three broad themes: ethical issues associated with systems in use, systems design, and responsible conduct of research.

Conclusions : The results of this overview demonstrate an active interest in exploring the ethical implications of Human-Computer Interaction concerns in Biomedical Informatics. Papers emphasizing ethical concerns associated with patient-facing tools, mobile devices, social media, privacy, inclusivity, and e-consent reflect the growing prominence of these topics in biomedical informatics research. New questions in these areas will likely continue to arise with the growth of precision medicine and citizen science.