Sexual violence is a global public health problem, threatening individuals’ health and well-being worldwide [1]. It is defined as “any sexual act, attempt to obtain a sexual act, unwanted sexual comments or advances, or acts to traffic, or otherwise directed, against a person’s sexuality using coercion, by any person regardless of their relationship to the victim, in any setting, including but not limited to home and work” [2]. Sexual violence victimization can lead to several adverse health outcomes, including self-perceived poor health [3], sexually transmitted infections (eg, HIV), unintended pregnancies, and mental health issues, such as depression, suicide attempts, and posttraumatic stress disorder [4].

Twitter, launched in 2006, has become one of the most widely used social media platforms and an important source of public health information [5-8] and the COVID-19 pandemic [9,10]. Over the past decade, researchers have conducted numerous studies using Twitter to gain insights into sexual violence. Twitter has become a significant venue for political protest [11], the feminist movement [12,13], digital feminist activism [14], and agenda setting for partner violence [15]. The MeToo campaign has been crucial in facilitating public disclosure of sexual assault experiences on social media, including Twitter. Survivors of sexual violence have used Twitter to disclose and share their experiences and connect with other survivors who have had similar experiences. This has enabled survivors to gain a deeper understanding of their experiences and has been seen as an essential step in the healing process [16]. Twitter has been used extensively to discuss the MeToo campaign [11,17,18] and other related hashtags such as #NotOkay [19], #UsToo [20], #WhyIStayed [21], and #WhyILeft [22].

Research has also explored social reactions to sexual violence disclosures on Twitter, with the use of hashtags such as #NotOkay [11]. There have also been debates regarding high-profile cases of sexual violence, such as the Janay Rice and Ray Rice case [23], as well as reactions to disclosures of sexual violence in the sports context [24]. Researchers have also analyzed hidden topics and thematic structures of sexual violence texts on Twitter [25]. The public response to disclosures of sexual violence can provide valuable insights to researchers on how bystanders react to such disclosures and help develop social media–based bystander programs. Negative social reactions to disclosures of sexual violence may dissuade survivors from disclosing their experiences, thereby making it difficult for them to access online support [26]. Sexual assault survivors have used Twitter to seek health information or build online communities where they can discuss the magnitude of the social problem. Using network analysis, researchers have also explored how topics related to sexual violence are posted and disseminated on Twitter [25].

In the digital age, Twitter has emerged as a crucial platform for understanding sexual violence. Understanding how the Twitter data set can be harnessed is essential to contribute to our understanding of sexual violence research. Therefore, exploring the ways in which Twitter data can be effectively used in sexual violence research can provide valuable insights to practitioners and researchers to effectively use the platform’s potential for implementing violence prevention and intervention strategies.

This study aims to provide a scoping review of the current literature on using Twitter data for sexual violence research. This review focuses primarily on the current state of Twitter data in sexual violence research, including (1) describing the main objectives addressed by previous studies in the field; (2) evaluating the methodology of these studies, including aspects such as study design, data collection, and data analysis strategies; (3) presenting a taxonomy describing the ways in which Twitter is used as a data source in sexual violence research; (4) presenting the various ways in which Twitter metadata features are used in this regard; and (5) discussing the implications and limitations of using Twitter data for sexual violence research. By providing a scoping overview of the existing research, this study contributes to a better understanding of the potential of Twitter data for sexual violence research and provides insights into the evidence-based practices for conducting such research.

This study was guided by Arksey and O’Malley’s scoping review framework [24]. This study was carried out in five stages, including (1) identifying research questions; (2) identifying relevant studies; (3) selecting studies; (4) charting the data; and (5) collating, summarizing, and reporting the results [27].

A social and health sciences librarian (JL) developed a search strategy for this review. The librarian drafted an initial search strategy validated against a predetermined test set of articles and peer-reviewed by an independent librarian colleague using the Peer Review of Electronic Search Strategies (PRESS) framework [28]. This search was structured to retrieve articles examining “sexual violence and Twitter” or “hashtags specific to sexual violence,” such as #MeToo and #TimesUp, using both text words and subject headings. We used a test set of 13 predetermined studies to validate the primary search strategy in APA PsycInfo (Ovid). We iteratively refined the initial search strategy to ensure all 13 studies would pass the initial screening. Once finalized, the search strategy was translated into 5 other databases to locate relevant peer-reviewed studies without geographical restrictions. In total, we searched six databases, including (1) APA PsycInfo (Ovid), (2) Scopus, (3) PubMed, (4) International Bibliography of Social Sciences (ProQuest), (5) Criminal Justice Abstracts (EBSCO), and (6) Communication Abstracts (EBSCO). Results were filtered by date starting from 2006, the year of Twitter’s inception. No other filters were applied. These searches were last run on April 14, 2022, and uploaded to Covidence for deduplication and screening. The search strategy is available in Multimedia Appendix 1.

The team developed a charting guideline for extracting essential information from the articles included in the review. The charting guideline was organized into several main categories, including publication year, study objectives, theoretical or conceptual framework, use of Twitter data in the study, use of Twitter metadata, data collection methods, sample size, data analysis strategy, key findings, ethical considerations, and study limitations. Seven research assistants participated in the data charting process. Prior to the charting launch, all research assistants completed a charting exercise by independently reviewing the same 4 selected articles to familiarize themselves with the charting guideline and identify any areas that needed further clarification. The research team met to debrief the charting exercise and refine the charting guideline accordingly. The intercoder reliability score was excellent (Cohen κ >90%). The PI then assigned each article to 2 research assistants for independent charting. The charting results from all research assistants were compiled into an Excel sheet, and any discrepancies were resolved by the PI. The team spent 3 months completing the screening and another 3 months for data extraction.

Using a scoping review design, this study aimed to provide a comprehensive knowledge synthesis that maps the current state of sexual violence studies using Twitter data and identifies gaps for future research. As per the scoping review framework [27], the quality of the selected studies was not appraised in this study.

In this study, a total of 121 articles were reviewed out of 3759 studies initially identified through the search. We reviewed and described the current state of sexual violence research using Twitter data, including publication year, publication sources (ie, journal or conference), research topic, research methods, data collection methods, sample size, timeframe of tweets collected, data analysis strategy, and institutional review board procedures for using Twitter data in sexual violence research. To characterize Twitter use as a data source in these articles, we developed a codebook to define the taxonomy for different metadata features that can be extracted from Twitter, including Twitter use (eg, content/text, social network, and sentiment/emotions), Twitter functions (eg, #hashtags, URLs, and geotags/locations), and Twitter users (eg, age, gender, and occupation). We also extracted information about the results and discussions of the assessed studies but did not report them in this article.

We extracted information about the publication year, publication source (ie, journal or conference), and research topics. Discussions related to institutional review boards or ethics reviews were also extracted.

We employed a predefined taxonomy codebook to categorize how Twitter data were used as a data source in the reviewed articles. The taxonomy included the following 5 categories: (1) content/text, (2) sentiment/emotions, (3) images/photos, (4) engagement (ie, campaigns and nonprofits), and (5) social networks (Table 1). Among the 121 reviewed articles, 112 (92.5%) analyzed the content/text of the tweets, and 31 (25.6%) focused on the sentiment expressed in the tweets (eg, positive or negative emotions). A small number of articles used Twitter to engage people for campaigns (n=5), and 2 studies involved image analysis in tweets. Furthermore, 14.9% (18/121) of the studies analyzed the relationships between networks of Twitter accounts, such as follower/following ratios, and other network characteristics. It is worth noting that studies that used Twitter as a platform to recruit participants or as an intervention were excluded from this review as we focused solely on using Twitter as a data source in this review.

In the 121 reviewed articles, hashtags were the most common Twitter metadata feature used (103/121, 85.1%), followed by tweet time and date (52/121, 43.0%), retweets (46/121, 38.0%), replies (ie, @mention; 37/121, 30.6%), URLs (22/121,18.2%), and geotags (25/121, 20.7%) (Table 2). Among the 121 articles, the majority (89/121, 73.5%) used multiple hashtags. For example, Rai et al [40] mentioned a total of 53 hashtags in their article. Only a minority of the articles (18/121, 14.9%) used a single hashtag, and a smaller portion (14/121, 11.6%) did not include any hashtags at all.

To determine metadata inclusion, we coded articles with 1 if they explicitly mentioned the 140-character limitation for tweets and 0 if not. Similarly, for URLs and hashtags, articles were coded as 1 if the URLs were explicitly shown as preserved in the data and 0 if the URLs were explicitly stated as cleaned from the data. The image/photo feature was coded as 1 for articles that included tweet images in the data set, regardless of their inclusion in the analysis. Few studies analyzed the demographic characteristics of Twitter users, such as gender (31/121, 25.6%), race (17/121, 14.0%), age (14/121, 11.6%), and occupation (13/121, 10.7%) (Table 3).

Over the past decade, researchers have shown an increased interest in using Twitter data to conduct sexual violence research. This scoping review included 121 articles that were published between 2006 and 2022, with an increase in the number of publications since 2013, peaking in 2019 and 2020. Almost 75% (89/121, 73.6%) of the reviewed articles were published in peer-reviewed journals across different disciplines. This review has a couple of main findings. First, it described the current use of Twitter data in sexual violence. Second, it presented a taxonomy outlining how Twitter data are used in this field, including analyzing content/text, sentiment/emotions, images/photos, engagement (ie, campaigns and nonprofits), and social networks. Third, it described and evaluated the methodology applied in the reviewed studies.

This review developed a taxonomy to describe the use of Twitter data in sexual violence research, which included the following 5 categories: (1) content/text, (2) sentiment/emotions, (3) images/photos, (4) engagement (ie, campaigns and nonprofits), and (5) social networks. More than 90% (112/121, 92.5%) of the reviewed studies used Twitter text for analysis. For example, Aurrekoetxea-Casaus [66] examined tweet discussions around the hashtags #lamanada [#thewolfpack] in Spain, using qualitative thematic and content analysis. The author found that Twitter is used as a “loudspeaker” to spread myths about sexual violence rather than to provide explicit support for victims. Nutbeam and Mereish [67] analyzed 508 tweets containing the hashtag #MeToo with qualitative content analysis to explore negative attitudes of condemnation or ambivalence toward sexual assault abusers in the MeToo movement. They found novel motives to explain the accusations of the MeToo movement and its accusers, such as wealth, malice, political gain, revenge, and fame, which are not frequently addressed in the literature. Another method of analyzing Twitter-generated data is to assess tweets for public positive or negative reactions [68]. For example, Stevens et al [69] studied the uncivil reactions to sexual assault on Twitter and Reddit by measuring the linguistic characteristics of the news media, such as negative emotions. They found a strong correlation between disagreement language and incivility of Twitter posts, and observed that increased negative emotions reduced identity attacks.

The technical paper by Kumar and Aggarwal [70] reported the methods of extracting sentiments from tweets and calculating the percentages of positive, negative, and neutral tweets to assess the safety of various cities for women. For instance, their study found that Chennai is the safest city for women, while Delhi is the least safe based on the sampled tweets. However, it is important to note that sentiment analysis is cross-sectional, meaning that results are influenced by the date and location of the tweets and are subject to changes in public opinion due to trending news or events in the year.

Social network analysis, which examines the relationship and interactions between Twitter users [65], was employed in 18 studies. Sim et al [71] in Singapore collaborated with the Association of Women for Action and Research (AWARE) to examine how they can reach out to the audience and drive greater awareness of sexual harassment on social media. To achieve this, they built an analytics pipeline that performs digital social listening on conversations about sexual harassment. They identified a network of top users whose tweets were most frequently liked and top users who acted as brokers in the network. They suggested that the AWARE can engage these users to spread information and increase their outreach efforts.

The smallest category of studies in our review focused on the analysis of Twitter images (n=2) or engaged people in campaigns (n=5). Hassan et al [18] examined the Face++ API to analyze Twitter users’ profile pictures to identify their gender and age. The study by Navarro and Coromina [12] examined the public reaction on Twitter following the judicial sentence of the La Manada case in Spain on April 26, 2018. They analyzed the images in the posts and found that the images broadened the layers of meaning in the public discourse on Twitter. Despite being a small category, the analysis of Twitter images and campaigns can provide valuable insights into the public’s attitudes and perceptions related to sexual violence.

This review represents sexual violence research that uses Twitter data, with an estimated 79.6 million tweets analyzed. The most common approach used to access Twitter data was Twitter’s API or Twitter’s advanced search function. Other platforms, such as Discover Text, provided access to the entire collected tweet data set at a cost. Twitter’s API has several advantages for academic research, including free access and the ability to retrieve up to 10 million tweets per month using advanced search operators [72]. Notably, there was variation in sample sizes among the reviewed studies, with some researchers claiming a small sample size (eg, n=300) as a limitation that prevented them from making generalized claims when not adopting a big data approach [56].

A previous review stated that 38 data features could be extracted for each tweet, but Twitter metadata was not fully used in the studies [68]. In this review, we found that many data features were still not included in the analyzed studies. In particular, there was a lack of analysis on metadata about Twitter users, such as their demographic characteristics like gender, age, and occupation. One reason for this underuse of Twitter metadata is the limitations of data collection tools. For example, NVivo Addition NCapture does not collect demographic information [20]. In addition, Twitter users often selectively share information in their public social media profiles, which can lead to a considerable number of missing values in the metadata.

Additionally, the passive nature of data analysis can only retrieve demographic information if it is available on the users’ profiles [73]. However, some studies used advanced data science models to classify the gender of the users. For example, Mueller et al [42] used a convolutional neural network model called “NeuralGenderDemographer” to examine demographic features, including gender and racial/ethnic identities, and their intersections. They found that tweets posted by White women were overrepresented in the #MeToo movement, while tweets posted by Black women revealed more criticisms of the unequal treatment in the social justice system.

This review highlights a potential limitation of underutilizing demographic information about Twitter users, which could decontextualize study findings. Understanding the specific communities that contribute to public opinion on Twitter is crucial to fully grasp the social impact of sexual violence on society. However, researchers must balance this with the ethical considerations of protecting Twitter users’ privacy and anonymity, particularly in sensitive topics such as sexual violence. As a result, demographic information, such as gender, race, and location, may be intentionally removed to protect the identity of users. This suggests a research gap and an opportunity to explore ways of better contextualizing social media data while maintaining confidentiality and privacy.

There are several limitations in this review that need to be acknowledged. First, we only included articles that used Twitter data (eg, text and photos) to research sexual violence or assault. Studies that used Twitter for intervention or participation recruitment purposes were excluded. Second, the search terms were limited to certain types of sexual violence, such as sexual assault, rape, sexual abuse, sexual harassment, and gender-based violence (Multimedia Appendix 1). Studies focusing on other forms of violence, such as physical abuse, psychological abuse, economic control, and verbal abuse, were excluded from the scoping review. It is worth noting that some of the reviewed studies examined sexual violence as well as other forms of violence such as physical assault. Third, our search strategy did not include key terms, such as “infoveillance” and “infodemiology,” which are important concepts in the field of public health research [74]. Despite these limitations, this review is the first to comprehensively synthesize and evaluate the current use of social media data in sexual violence research, providing valuable insights into the field.

This scoping review demonstrates the potential of Twitter data as a valuable resource in sexual violence research. This method allows researchers to expand beyond traditional data collection methods like surveys, interviews, and administrative data for sexual violence research. However, it is important to note that gaps exist in the current literature, and a further study is needed to fully understand the potential and limitations of using Twitter as a data source for sexual violence research.

Existing literature shows the potential of using Twitter-based data for sexual violence research, as evidenced by the increasing number of publications after 2018. To contribute to this field, this study reviewed and extracted 121 articles that collectively analyzed about 79.6 million tweets. We described the current state of using Twitter data for sexual violence research, developed a new taxonomy that describes Twitter as a data source, and evaluated the methodologies used. Based on our review, several research recommendations are proposed, such as development of data collection and analysis methods, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, analysis of tweets in multiple languages, and a more inclusive representation of the findings. These recommendations may guide future studies on using Twitter data for sexual violence research.