Social innovation has increasingly become a hot topic in China, a process in which multiple sectors collaborate with each other, aiming to change the status quo through creative, effective, efficient and sustainable ways. InterBoxes is an innovative project in the form of a social enterprise that commits to improving physical school conditions by building libraries, classrooms, schools and dormitories with refurbished cargo shipping containers called "Boxes". Within the scope of this project the word "Inter" signifies that each Box is equipped with an Internet connection to the outside world. Conceptually, as a social enterprise, the for-profit, business arm of InterBoxes is projected to operate within metropolitan areas, building structures such as cafés, gyms, bookstores, etc., all which will generate revenue to support the nonprofit operation in rural areas. This descriptive case study examines the implementation and use of InterBoxes as a library in a rural primary school and addresses the promises and challenges facing the project. The findings indicate that InterBoxes demonstrates much potential as a social innovation to improve physical school conditions and other rural education issues through the creative use of space and place. Recommendations for scaling up its operation in connection with a larger global network of innovation using shipping containers are discussed.

Many migrants are vulnerable due to noncitizenship, linguistic or cultural barriers, and inadequate safety-net infrastructures. Immigrant-oriented nonprofits can play an important role in improving immigrant well-being. However, progress on systematically evaluating the impact of nonprofits has been hampered by the difficulty in efficiently and accurately identifying immigrant-oriented nonprofits in large administrative data sets. We tackle this challenge by employing natural language processing (NLP) and machine learning (ML) techniques. Seven NLP algorithms are applied and trained in supervised ML models. The bidirectional encoder representations from transformers (BERT) technique offers the best performance, with an impressive accuracy of .89. Indeed, the model outperformed two nonmachine methods used in existing research, namely, identification of organizations via National Taxonomy of Exempt Entities codes or keyword searches of nonprofit names. We thus demonstrate the viability of computer-based identification of hard-to-identify nonprofits using organizational name data, a technique that may be applicable to other research requiring categorization based on short labels. We also highlight limitations and areas for improvement.

China’s migrant population has significantly contributed to its economic growth; however, the impact on the well-being of left-behind children (LBC) has become a serious public health problem. Text mining is an effective tool for identifying people’s mental state, and is therefore beneficial in exploring the psychological mindset of LBC. Traditional data collection methods, which use questionnaires and standardized scales, are limited by their sample sizes. In this study, we created a computational application to quantitively collect personal narrative texts posted by LBC on Zhihu, which is a Chinese question-and-answer online community website; 1475 personal narrative texts posted by LBC were gathered. We used four types of words, i.e., first-person singular pronouns, negative words, past tense verbs, and death-related words, all of which have been associated with depression and suicidal ideations in the Chinese Linguistic Inquiry Word Count (CLIWC) dictionary. We conducted vocabulary statistics on the personal narrative texts of LBC, and bilateral t-tests, with a control group, to analyze the psychological well-being of LBC. The results showed that the proportion of words related to depression and suicidal ideations in the texts of LBC was significantly higher than in the control group. The differences, with respect to the four word types (i.e., first-person singular pronouns, negative words, past tense verbs, and death-related words), were 5.37, 2.99, 2.65, and 2.00 times, respectively, suggesting that LBC are at a higher risk of depression and suicide than their counterparts. By sorting the texts of LBC, this research also found that child neglect is a main contributing factor to psychological difficulties of LBC. Furthermore, mental health problems and the risk of suicide in vulnerable groups, such as LBC, is a global public health issue, as well as an important research topic in the era of digital public health. Through a linguistic analysis, the results of this study confirmed that the experiences of left-behind children negatively impact their mental health. The present findings suggest that it is vital for the public and nonprofit sectors to establish online suicide prevention and intervention systems to improve the well-being of LBC through digital technology.

BACKGROUND Contraceptive choice is central to reproductive autonomy, and the internet, including online communities like those formed on Reddit, is an important resource for people seeking contraceptive information and peer support. A subreddit dedicated to contraception, r/birthcontrol, provides a platform for people to share narratives, offering real-time insights in contraceptive decision-making processes and use experiences. OBJECTIVE This study explored use of r/birthcontrol, from the inception of the subreddit through the end of 2020, to describe the online community, identify distinctive interests and themes based upon the textual content of posts, and identify and explore the content of posts with the most user engagement (i.e. ‘popular’ posts). METHODS Data were obtained from the PushShift Reddit API from the establishment of r/birthcontrol to the start date of this analysis (July 21, 2011-December 31, 2020). User interactions within the subreddit were analyzed to describe use of this community over time, specifically the commonality of use based on the volume of posts, the length of posts (character count), and the proportion of posts with any and each flair applied. ‘Scores’, or upvotes minus downvotes serving as a proxy for the popularity of each post, were used to determine ‘popular’ posts on r/birthcontrol (posts with 9 comments and a score of ≥3). TF-IDF analyses were run on all posts with flairs applied, posts within each flair group, and popular posts within each flair group to characterize and compare distinctive language used in each group of posts. RESULTS There were 105,485 posts to r/birthcontrol during the study period, with use of the subreddit increasing over time. The majority of posts were exclusively textual content (96%), had comments (86%), and had a score (96%). Posts averaged 731 characters in length, with a median of 555 characters. Within the timeframe that flairs were available on r/birthcontrol (since February 4, 2016), users applied flairs to 78% of posts with increasing use over time. “SideEffects?” was most frequently used flair among all posts (40% of posts), while “Experience” and “Side Effects” were most frequently applied among popular posts (31% and 29%, respectively). TF-IDF analyses of all posts showed interest in contraceptive methods, menstrual experiences, timing, feelings, and unprotected sex. While n-gram results for posts with each flair varied, the contraceptive pill, menstrual experiences, and timing were discussed across flair groups. Among popular posts, IUDs and contraceptive use experiences were often discussed. CONCLUSIONS This study provides insights into how r/birthcontrol has been used as a resource for contraceptive information and support since 2018 and presents a case study of how public health researchers can use Machine Learning methods to study social networking sites, contributing to and expanding public health research and discourse.

Background Contraceptive choice is central to reproductive autonomy. The internet, including social networking sites like Reddit, is an important resource for people seeking contraceptive information and support. A subreddit dedicated to contraception, r/birthcontrol, provides a platform for people to post about contraception. Objective This study explored the use of r/birthcontrol, from the inception of the subreddit through the end of 2020. We describe the web-based community, identify distinctive interests and themes based upon the textual content of posts, and explore the content of posts with the most user engagement (ie, “popular” posts). Methods Data were obtained from the PushShift Reddit application programming interface from the establishment of r/birthcontrol to the start date of analysis (July 21, 2011, to December 31, 2020). User interactions within the subreddit were analyzed to describe community use over time, specifically the commonality of use based on the volume of posts, the length of posts (character count), and the proportion of posts with any and each flair applied. “Popular” posts on r/birthcontrol were determined based on the number of comments and “scores,” or upvotes minus downvotes; popular posts had 9 comments and a score of ≥3. Term Frequency-Inverse Document Frequency (TF-IDF) analyses were run on all posts with flairs applied, posts within each flair group, and popular posts within each flair group to characterize and compare the distinctive language used in each group. Results There were 105,485 posts to r/birthcontrol during the study period, with the volume of posts increasing over time. Within the time frame for which flairs were available on r/birthcontrol (after February 4, 2016), users applied flairs to 78% (n=73,426) of posts. Most posts contained exclusively textual content (n=66,071, 96%), had comments (n=59,189, 86%), and had a score (n=66,071, 96%). Posts averaged 731 characters in length (median 555). “SideEffects!?” was the most frequently used flair overall (n=27,530, 40%), while “Experience” (n=719, 31%) and “SideEffects!?” (n=672, 29%) were most common among popular posts. TF-IDF analyses of all posts showed interest in contraceptive methods, menstrual experiences, timing, feelings, and unprotected sex. While TF-IDF results for posts with each flair varied, the contraceptive pill, menstrual experiences, and timing were discussed across flair groups. Among popular posts, intrauterine devices and contraceptive use experiences were often discussed. Conclusions People commonly wrote about contraceptive side effects and experiences using methods, highlighting the value of r/birthcontrol as a space to post about aspects of contraceptive use that are not well addressed by clinical contraceptive counseling. The value of real-time, open-access data on contraceptive users’ interests is especially high given the shifting landscape of and increasing constraints on reproductive health care in the United States.