BACKGROUND Scholars have data from in-person interviews, administrative data, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and under-explored area of study. OBJECTIVE This study aims to provide 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 six 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 3,759 articles that were imported into Covidence. Six independent reviewers screened these articles following two steps: (1) titles and abstracts and (2) full-text screening. The inclusion criteria were (1) empirical research, (2) focusing on sexual violence, (3) analyzing Twitter data (i.e., tweets and/or Twitter metadata), and (4) writing in English. Finally, six authors 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% of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million Tweets. The primary approach to using Twitter as a data source was content text analysis (n=112, 92.5%) and sentiments (n=31, 25.6%). Hashtags (n=77, 83.7%) were the most prominent metadata features, followed by the time and date of the tweet, retweets, replies, URLs, and geotags. More than half of the articles (n=51, 38.3%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (e.g., sentiment analysis, supervised machine learning, unsupervised machine learning, social network analysis), and quantitative analysis. Only ten percent of the studies discussed ethical considerations in their articles. CONCLUSIONS We describe the current state of using Twitter data for sexual violence research, develop a new taxonomy describing Twitter as a data source and evaluate the methodologies. Research recommendations include the following: the development of methods for data collection and analysis, in-depth discussions about ethical norms, specific aspects of sexual violence on Twitter, examinations of tweets in multiple languages, and generalizability of Twitter data. The current review demonstrates the potential of using Twitter data in sexual violence research.

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.

BACKGROUND Artificial Intelligence (AI) conversational agents, such as chatbots, have become ubiquitous in our daily lives, enabling natural language conversations with users through various modes of communication. Conversational agents have the potential to play a significant role in promoting health and well-being. As the number of studies and available products related to conversational agents continues to rise, there is a critical need to assess the product features to enhance the design of conversational agents that effectively promote health and behavioral change. OBJECTIVE This scoping review provides a comprehensive assessment of the efficacy of AI chatbots for digital health, including the chatbots’ characteristics, user backgrounds, communication models, building relational capacity, personalization, interaction, and responses to suicidal thoughts. The second study objective is to understand the users’ in-app experiences during app usage. METHODS This review follows Arksey and O’Malley's scoping review methodology. Two different approaches were employed to identify relevant chatbots and studies: search for AI chatbot apps in the iOS and Android app stores and in scientific journals or conference articles through a search strategy designed by a librarian. After screening the chatbots, 36 chatbots were identified and evaluated by 10 research assistants for analysis through simulated conversations. RESULTS We compiled a dataset of 36 chatbots and evaluated their features, conversational capabilities, and user experiences. Most chatbots were rated for all ages or teenagers, and their sizes and developers varied. Chatbots utilized text, animations, speech, images, and emojis for communication. Personalization options and relational capacity varied, with some chatbots demonstrating empathy and humor. Approximately 44% of chatbots addressed suicidal thoughts effectively. These findings provide insights for improving the design and functionality of AI chatbots in digital health interventions. CONCLUSIONS AI chatbots have the potential to revolutionize digital health interventions by offering scalable and personalized solutions for behavior change. Future research should focus on addressing limitations, exploring real-world user experiences, and evaluating long-term effectiveness to optimize their impact on healthcare delivery and digital health interventions.

BACKGROUND Social media platforms like Twitter have gained popularity as communication tools for organizations to engage with their clients and the public, disseminate information, and raise awareness about social issues. OBJECTIVE This study investigates the current utilization of Twitter by sexual assault centers, crisis lines, and support services in Canada. METHODS We used purposive sampling and analyzed 124 sexual assault centers with Twitter accounts across nine provinces in Canada. Descriptive analysis was conducted to determine the number of centers, their distribution by province, and the average age of Twitter accounts. Geolocation mapping was used to visualize the locations of the centers. RESULTS Ontario had the highest number of sexual assault centers with Twitter accounts, followed by British Columbia and Quebec. Geographic distribution maps visualized the concentration of centers in these provinces. The analysis of Twitter account creation years showed a peak in 2012, followed by a decline in new account creations in subsequent years. The average age of Twitter accounts also differed among provinces, ranging from 3.34 years in Yukon to 12.77 years in the Northwest Territories. Monthly tweet activity showed November as the most active month, while July had the lowest activity. The average monthly tweet count per center ranged from less than 20 to 180. CONCLUSIONS Findings contribute to a better understanding of the social media landscape of sexual assault organizations in Canada and their communication strategies within the nonprofit human services sector.