Twitter as a Tool for Health Research: A Systematic Review

American journal of public health - Tập 107 Số 1 - Trang e1-e8 - 2017
Lauren Sinnenberg1, Alison M. Buttenheim1, Kevin Padrez1, Christina Mancheno1, Lyle Ungar1, Raina M. Merchant1
1Lauren Sinnenberg, Kevin Padrez, Christina Mancheno, and Raina M. Merchant are with Penn Medicine Social Media and Health Innovation Lab, University of Pennsylvania, Philadelphia. Lyle Ungar is with the Department of Computer and Information Science, University of Pennsylvania. Alison M. Buttenheim is with Department of Family and Community Health, School of Nursing, University of Pennsylvania, and Center for Health Incentives and Behavioral Economics, University of Pennsylvania.

Tóm tắt

Background. Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose. Objectives. To systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. Search methods. We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. Selection criteria. We searched for peer-reviewed original research studies that primarily used Twitter for health research. Data collection and analysis. Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. Main results. Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%). Conclusions. We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user’s Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.

Từ khóa


Tài liệu tham khảo

10.1016/j.healthpol.2013.02.005

10.1371/journal.pone.0014118

10.4269/ajtmh.2012.11-0597

10.1177/1524839912469378

Paul MJ, 2011, Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, 265

Duggan M, 2013, Pew Research Center, 1

10.2196/jmir.3050

10.2196/jmir.3662

10.1371/journal.pone.0139701

10.2196/jmir.2912

10.1136/bmjopen-2013-002819

10.1097/ACM.0b013e31829eb91c

10.1080/17538068.2015.1107308

10.1093/bmb/ldt028

10.2196/jmir.4304

10.2196/jmir.2952

10.2196/jmir.1933

10.1177/1524839911433467

10.1016/j.jbi.2015.02.004

10.1111/bcp.12717

10.1111/1468-0009.12038

10.2196/jmir.1376

10.7326/0003-4819-151-4-200908180-00135

10.1186/1742-7622-5-23

10.1016/j.bodyim.2015.03.006

10.1186/s12889-015-1396-z

10.2105/AJPH.2012.301166

10.1186/1471-2458-13-1129

10.1186/1471-2458-11-583

10.2196/jmir.2972

10.2196/jmir.2775

10.1080/10810730.2013.811321

10.1186/1756-0500-5-699

van Genderen PJ, 2013, Neth J Med, 71, 437

10.1016/j.ijmedinf.2014.01.002

10.2196/jmir.1841

10.1016/j.schres.2015.04.009

10.2196/jmir.3812

10.1145/1871985.1871993

10.1145/2488388.2488401

10.1145/2020408.2020477

Burger JD, 2011, Proceedings of the Conference on Empirical Methods in Natural Language Processing, 1301

10.1016/j.knosys.2013.06.020

Bruns A, 2014, Twitter and Society, 15

10.1007/s13181-013-0299-6

10.3726/978-1-4539-1170-9

10.1016/j.neucom.2015.05.066

10.1080/15230406.2015.1059251

Vo DT, 2015, Computational Intelligence and Neuroscience, 2015

Centers for Disease Control and Prevention. National diabetes statistics report: estimates of diabetes and its burden in the United States, 2014. Atlanta, GA: US Department of Health and Human Services; 2014.

Nwankwo T, 2013, NCHS Data Brief, 1

10.1161/CIR.0000000000000152

10.1093/aje/kwv011

10.2196/publichealth.3953

10.1177/183335831204100204

10.1371/journal.pcbi.1003904

10.1007/s10676-010-9227-5

Thông tin
Thông tin xuất bản

American journal of public health

Tập 107 Số 1

e1-e8

Thông tin tác giả