Background Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users’ perspectives in the development of ethical norms and regulations. Objective This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. Methods We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. Results While nearly half the respondents—on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)—indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents’ general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents’ Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people’s concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. Conclusions The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines.
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Crowdsourcing to analyze belief systems underlying social issues
People’s beliefs and attitudes about social and scientific
issues, such as capital punishment and climate change, appear
to form complex but generally coherent networks.
Understanding the nature of these networks is a prerequisite
for designing interventions for changing beliefs on the basis
of rational arguments and evidence. It is therefore important
to develop methods to represent and analyze the form and
nature of belief networks, which may not be explicitly
verbalizable. Adopting an emerging approach that utilizes
crowdsourcing to develop educational interventions, we
mined discussions from the Reddit forum Change My View to
determine which beliefs and types of information underlie
people’s attitudes about capital punishment. By combining
computational analyses based on a topic model with more
qualitative assessments of the extracted topics, we found that
moral arguments are more prevalent than statistical or
data-based arguments. The present study serves as a test case
for the open sourced software crowdpy, a Python toolkit for
running naturalistic studies on the web, which will enable
other researchers to use crowdsourcing in their research. This
approach sets the stage for research exploring potential
interventions to change people’s beliefs.
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- Award ID(s):
- 1827374
- NSF-PAR ID:
- 10157636
- Date Published:
- Journal Name:
- Proceedings of the Annual Conference of the Cognitive Science Society
- ISSN:
- 1069-7977
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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