Using Twitter-Based Data for Sexual Violence Research: Scoping Review

Overview

Journal J Med Internet Res

Publisher JMIR Publications

Specialty Medical Informatics

Date 2023 May 15

PMID 37184899

Authors

Jia Xue

Bolun Zhang

Qiaoru Zhang

Ran Hu

Jielin Jiang

Nian Liu

Yingdong Peng

Ziqian Li

Judith Logan

Affiliations

Soon will be listed here.

Abstract

Background: Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study.

Objective: We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies.

Methods: We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles.

Results: We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations.

Conclusions: We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research.

Citing Articles

Bridging and Bonding Social Capital by Analyzing the Demographics, User Activities, and Social Network Dynamics of Sexual Assault Centers on Twitter: Mixed Methods Study.

Xue J, Zhang Q, Zhang Y, Shi H, Zheng C, Fan J J Med Internet Res. 2024; 26:e50552.

PMID: 38536222 PMC: 11007606. DOI: 10.2196/50552.

References

Eysenbach G . Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009; 11(1):e11. PMC: 2762766. DOI: 10.2196/jmir.1157. View

Stevens H, Acic I, Taylor L . Uncivil Reactions to Sexual Assault Online: Linguistic Features of News Reports Predict Discourse Incivility. Cyberpsychol Behav Soc Netw. 2021; 24(12):815-821. DOI: 10.1089/cyber.2021.0075. View

Alaggia R, Wang S . "I never told anyone until the #metoo movement": What can we learn from sexual abuse and sexual assault disclosures made through social media?. Child Abuse Negl. 2020; 103:104312. DOI: 10.1016/j.chiabu.2019.104312. View

Baik J, Nyein T, Modrek S . Social Media Activism and Convergence in Tweet Topics After the Initial #MeToo Movement for Two Distinct Groups of Twitter Users. J Interpers Violence. 2021; 37(15-16):NP13603-NP13622. DOI: 10.1177/08862605211001481. View

Su Y, Xue J, Liu X, Wu P, Chen J, Chen C . Examining the Impact of COVID-19 Lockdown in Wuhan and Lombardy: A Psycholinguistic Analysis on Weibo and Twitter. Int J Environ Res Public Health. 2020; 17(12). PMC: 7344534. DOI: 10.3390/ijerph17124552. View

Xue J, Chen J, Hu R, Chen C, Zheng C, Su Y . Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. J Med Internet Res. 2020; 22(11):e20550. PMC: 7690968. DOI: 10.2196/20550. View

Nutbeam M, Mereish E . Negative Attitudes and Beliefs Toward the #MeToo Movement on Twitter. J Interpers Violence. 2021; 37(15-16):NP13018-NP13044. PMC: 9802676. DOI: 10.1177/08862605211001470. View

Fanslow J, Robinson E . Violence against women in New Zealand: prevalence and health consequences. N Z Med J. 2004; 117(1206):U1173. View

Xue J, Chen J, Chen C, Hu R, Zhu T . The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets. J Med Internet Res. 2020; 22(11):e24361. PMC: 7652592. DOI: 10.2196/24361. View

10.

Ayers J, Caputi T, Nebeker C, Dredze M . Don't quote me: reverse identification of research participants in social media studies. NPJ Digit Med. 2019; 1:30. PMC: 6550214. DOI: 10.1038/s41746-018-0036-2. View

11.

Storer H, Rodriguez M, Franklin R . "Leaving Was a Process, Not an Event": The Lived Experience of Dating and Domestic Violence in 140 Characters. J Interpers Violence. 2018; 36(11-12):NP6553-NP6580. DOI: 10.1177/0886260518816325. View

12.

Lowenstein-Barkai H . #Me(n)Too? Online Social Support Toward Male and Female Survivors of Sexual Victimization. J Interpers Violence. 2020; 36(23-24):NP13541-NP13563. DOI: 10.1177/0886260520905095. View

13.

Guidry J, Sawyer A, Carlyle K, Burton C . #WhyIDidntReport: Women Speak Out About Sexual Assault on Twitter. J Forensic Nurs. 2021; 17(3):129-139. DOI: 10.1097/JFN.0000000000000335. View

14.

Krug E, Mercy J, Dahlberg L, Zwi A . The world report on violence and health. Lancet. 2002; 360(9339):1083-8. DOI: 10.1016/S0140-6736(02)11133-0. View

15.

McCauley H, Bonomi A, Maas M, Bogen K, OMalley T . #MaybeHeDoesntHitYou: Social Media Underscore the Realities of Intimate Partner Violence. J Womens Health (Larchmt). 2018; 27(7):885-891. DOI: 10.1089/jwh.2017.6560. View

16.

Lopez G, Bogen K, Meza-Lopez R, Nugent N, Orchowski L . #DomesticViolence During the COVID-19 Global Pandemic: An Analysis of Public Commentary via Twitter. Digit Health. 2022; 8:20552076221115024. PMC: 9340387. DOI: 10.1177/20552076221115024. View

17.

Li M, Turki N, Izaguirre C, DeMahy C, Thibodeaux B, Gage T . Twitter as a tool for social movement: An analysis of feminist activism on social media communities. J Community Psychol. 2020; 49(3):854-868. DOI: 10.1002/jcop.22324. View

18.

Mueller A, Wood-Doughty Z, Amir S, Dredze M, Lynn Nobles A . Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement. Proc ACM Hum Comput Interact. 2022; 5(CSCW1). PMC: 8920314. DOI: 10.1145/3449181. View

19.

McGowan J, Sampson M, Salzwedel D, Cogo E, Foerster V, Lefebvre C . PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement. J Clin Epidemiol. 2016; 75:40-6. DOI: 10.1016/j.jclinepi.2016.01.021. View

20.

Preotiuc-Pietro D, Volkova S, Lampos V, Bachrach Y, Aletras N . Studying User Income through Language, Behaviour and Affect in Social Media. PLoS One. 2015; 10(9):e0138717. PMC: 4578862. DOI: 10.1371/journal.pone.0138717. View