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

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.

Exploring Factors of Middle School Teachers' Satisfaction with Online Training for Sustainable Professional Development under the Impact of COVID-19

During the COVID-19 pandemic, as offline learning activities were blocked, teachers' training activities were moved from face-to-face to online training. Therefore, teachers had to join an increasing number of online training sessions. However, few studies have focused on teachers' satisfaction with online training. To address this gap, based on the American user satisfaction theory model (ACSI), this study established the factors of expectation of online training quality, perceived online training quality, perceived online training value, and teacher satisfaction with online learning, and aimed to explore their relationships with six hypotheses. A total of 397 middle school teachers who had online training experience participated in the survey through an online questionnaire. SPSS 26.0 and AMOS 23.0 were used to analyze the data. The results showed that (1) expectation of online training quality was positively correlated with perceived online training quality;(2) expectation of online training quality was negatively correlated with perceived online training value;(3) perceived online training quality was positively correlated with perceived online training value;and (4) perceived online training value was positively correlated with online training satisfaction. The findings imply that teachers should be informed in advance of various difficulties that may be encountered in online training, so as to reduce their expectations of online training quality. In addition, in order to improve teachers' perceived quality and perceived value of online training, intervention strategies should be proposed, online training platforms should be optimized, and online training methods should be innovated to improve teachers' sustainable development ability.

Virtual Reality or Augmented Reality as a Tool for Studying Bystander Behaviors in Interpersonal Violence: Scoping Review.

BACKGROUND: To provide participants with a more real and immersive intervening experience, virtual reality (VR) and/or augmented reality (AR) technologies have been integrated into some bystander intervention training programs and studies measuring bystander behaviors. OBJECTIVE: We focused on whether VR or AR can be used as a tool to enhance training bystanders. We reviewed the evidence from empirical studies that used VR and/or AR as a tool for examining bystander behaviors in the domain of interpersonal violence research. METHODS: Two librarians searched for articles in databases, including APA PsycInfo (Ovid), Criminal Justice Abstracts (EBSCO), Medline (Ovid), Applied Social Sciences Index & Abstracts (ProQuest), Sociological Abstracts (ProQuest), and Scopus till April 15, 2020. Studies focusing on bystander behaviors in conflict situations were included. All study types (except reviews) written in English in any discipline were included. RESULTS: The search resulted in 12,972 articles from six databases, and the articles were imported into Covidence. Eleven studies met the inclusion and exclusion criteria. All 11 articles examined the use of VR as a tool for studying bystander behaviors. Most of the studies were conducted in US young adults. The types of interpersonal violence were school bullying, dating violence, sexual violence/assault, and soccer-associated violence. VR technology was used as an observational measure and bystander intervention program. We evaluated the different uses of VR for bystander behaviors and noted a lack of empirical evidence for AR as a tool. We also discuss the empirical evidence regarding the design, effectiveness, and limitations of implementing VR as a tool in the reviewed studies. CONCLUSIONS: The reviewed results have implications and recommendations for future research in designing and implementing VR/AR technology in the area of interpersonal violence. Future studies in this area may further contribute to the use of VR as an observational measure and explore the potential use of AR to study bystander behaviors.

France is well positioned for a strong post-Covid rebound

Chinese investment into France held steady in 2020 while it dropped across the rest of Europe. Raphaël Chantelot, Ran Hu, Fanny Nguyen, Hubert Bazin and Nicolas Vanderchmitt of LPA-CGR avocats review the jurisdiction’s investment advantages

Teaching Note—Teaching Socially Just Culturally Competent Practice Online: Pedagogical Challenges and Lessons Learned During the Pandemic

Centering social justice into social work practice is vital to the profession, but both what and how to accomplish this task are pedagogical challenges in social work education. This teaching note introduces an elective course open to both Master of Social Work and doctoral students to develop holistic competence for socially just and culturally competent social work practice. We describe how the course originally designed in an in-person format was redesigned and adapted to an online format during the COVID-19 pandemic. Another consideration during this transition is how this requires social work students who are adult learners to balance family, work, and school demands while attending virtual classrooms in a personal space during physical distancing. We explain metacompetence and procedural competence, which are two competence dimensions conceptualized in the holistic competence model. Each competence dimension was targeted through a variety of synchronous and asynchronous virtual teaching approaches (e.g., intergroup dialog, digital storytelling, and virtual simulation). While these teaching approaches may be applicable to other online courses, we highlight specific considerations for teaching social justice online. We close with a discussion of challenges and new lessons from teaching social justice in social work practice courses online and provide recommendations for future teaching and research. [ABSTRACT FROM AUTHOR] Copyright of Journal of Social Work Education is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Teaching Note—Virtual Practice Fridays: Responding to Disruptions Caused by the COVID-19 Pandemic in the Field and Classroom

Preparing students for social work practice is an important responsibility shared by field education and schools of social work. The COVID-19 pandemic has caused disruptions to social work practice training and education in the classroom and field. This teaching note describes an online simulation-based learning activity known as Virtual Practice Fridays that was adapted from an in-person activity in response to the pandemic. Virtual Practice Fridays was a 10-week seminar for two cohorts of master of social work students, each facilitated by two PhD students who were supervised by a faculty member. Master of social work students developed holistic competence, including knowledge, skills, and self-awareness, as they met with two simulated clients over several sessions. Students described unique features of this simulation-based learning activity that supported their learning, such as working with clients virtually, having an opportunity to see simulated clients for several sessions from assessment to termination, and being able to use the time completed at Virtual Practice Fridays toward fulfillment of practicum hours. This was a new experience for the doctoral students and faculty member, and it permitted us to reflect on how to effectively teach students about social work practice. Implications for social work practice and education are discussed. [ABSTRACT FROM AUTHOR] Copyright of Journal of Social Work Education is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.

BACKGROUND: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. OBJECTIVE: The objective of this study is to examine COVID-19-related discussions, concerns, and sentiments using tweets posted by Twitter users. METHODS: We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, "coronavirus," "COVID-19," "quarantine") from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. RESULTS: Popular unigrams included "virus," "lockdown," and "quarantine." Popular bigrams included "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. CONCLUSIONS: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.

The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.

BACKGROUND: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. OBJECTIVE: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. METHODS: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. RESULTS: We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence-related news (eg, Tara Reade, Melissa DeRosa). CONCLUSIONS: This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks.