Explore the vast range of titles available.

In 2016, 44,965 people in the United States died by suicide. It is common to see people with suicidal ideation seek help or leave suicide notes on social media before attempting suicide. Many prefer to express their feelings with longer passages on forums such as Reddit and blogs. Because these expressive posts follow regular language patterns, potential suicide attempts can be prevented by detecting suicidal posts as they are written. This study aims to build a classifier that differentiates suicidal and nonsuicidal forum posts via text mining methods applied on post titles and bodies. A total of 508,398 Reddit posts longer than 100 characters and posted between 2008 and 2016 on SuicideWatch, Depression, Anxiety, and ShowerThoughts subreddits were downloaded from the publicly available Reddit dataset. Of these, 10,785 posts were randomly selected and 785 were manually annotated as suicidal or nonsuicidal. Features were extracted using term frequency-inverse document frequency, linguistic inquiry and word count, and sentiment analysis on post titles and bodies. Logistic regression, random forest, and support vector machine (SVM) classification algorithms were applied on resulting corpus and prediction performance is evaluated. The logistic regression and SVM classifiers correctly identified suicidality of posts with 80% to 92% accuracy and F1 score, respectively, depending on different data compositions closely followed by random forest, compared to baseline ZeroR algorithm achieving 50% accuracy and 66% F1 score. This study demonstrated that it is possible to detect people with suicidal ideation on online forums with high accuracy. The logistic regression classifier in this study can potentially be embedded on blogs and forums to make the decision to offer real-time online counseling in case a suicidal post is being written.

Drawing on Bronfenbrenner’s ecological theory and prior empirical research, the current study examines the way that blogging and social networking may impact feelings of connection and social support, which in turn could impact maternal well-being (e.g., marital functioning, parenting stress, and depression). One hundred and fifty-seven new mothers reported on their media use and various well-being variables. On average, mothers were 27 years old (SD = 5.15) and infants were 7.90 months old (SD = 5.21). All mothers had access to the Internet in their home. New mothers spent approximately 3 hours on the computer each day, with most of this time spent on the Internet. Findings suggested that frequency of blogging predicted feelings of connection to extended family and friends which then predicted perceptions of social support. This in turn predicted maternal well-being, as measured by marital satisfaction, couple conflict, parenting stress, and depression. In sum, blogging may improve new mothers’ well-being, as they feel more connected to the world outside their home through the Internet.

Journal clubs are an essential tool in promoting clinical evidence-based medical education to all medical and allied health professionals. Twitter represents a public, microblogging forum that can facilitate traditional journal club requirements, while also reaching a global audience, and participation for discussion with study authors and colleagues. The aim of the current study was to evaluate the current state of social media-facilitated journal clubs, specifically Twitter, as an example of continuing professional development. A systematic review of literature databases (Medline, Embase, CINAHL, Web of Science, ERIC via ProQuest) was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search of Twitter, the followers of identified journal clubs, and Symplur was also performed. Demographic and monthly tweet data were extracted from Twitter and Symplur. All manuscripts related to Twitter-based journal clubs were included. Statistical analyses were performed in MS Excel and STATA. From a total of 469 citations, 11 manuscripts were included and referred to five Twitter-based journal clubs (#ALiEMJC, #BlueJC, #ebnjc, #urojc, #meded). A Twitter-based journal club search yielded 34 potential hashtags/accounts, of which 24 were included in the final analysis. The median duration of activity was 11.75 (interquartile range [IQR] 19.9, SD 10.9) months, with 7 now inactive. The median number of followers and participants was 374 (IQR 574) and 157 (IQR 272), respectively. An overall increasing establishment of active Twitter-based journal clubs was observed, resulting in an exponential increase in total cumulative tweets (R(2)=.98), and tweets per month (R(2)=.72). Cumulative tweets for specific journal clubs increased linearly, with @ADC_JC, @EBNursingBMJ, @igsjc, @iurojc, and @NephJC, and showing greatest rate of change, as well as total impressions per month since establishment. An average of two tweets per month was estimated for the majority of participants, while the \"Top 10\" tweeters for @iurojc showed a significantly lower contribution to overall tweets for each month (P<.005). A linearly increasing impression:tweet ratio was observed for the top five journal clubs. Twitter-based journal clubs are free, time-efficient, and publicly accessible means to facilitate international discussions regarding clinically important evidence-based research.

Background Several disease-specific information exchanges now exist on Facebook and other online social networking sites. These new sources of knowledge, support, and engagement have become important for patients living with chronic disease, yet the quality and content of the information provided in these digital arenas are poorly understood. Objective To qualitatively evaluate the content of communication in Facebook communities dedicated to diabetes. Design We identified the 15 largest Facebook groups focused on diabetes management. For each group, we downloaded the 15 most recent “wall posts” and the 15 most recent discussion topics from the 10 largest groups. Patients Four hundred eighty unique users were identified in a series of 690 comments from wall posts and discussion topics. Main Measures Posts were abstracted and aggregated into a database. Two investigators evaluated the posts, developed a thematic coding scheme, and applied codes to the data. Key Results Patients with diabetes, family members, and their friends use Facebook to share personal clinical information, to request disease-specific guidance and feedback, and to receive emotional support. Approximately two-thirds of posts included unsolicited sharing of diabetes management strategies, over 13% of posts provided specific feedback to information requested by other users, and almost 29% of posts featured an effort by the poster to provide emotional support to others as members of a community. Approximately 27% of posts featured some type of promotional activity, generally presented as testimonials advertising non-FDA approved, “natural” products. Clinically inaccurate recommendations were infrequent, but were usually associated with promotion of a specific product or service. Thirteen percent of posts contained requests for personal information from Facebook participants. Conclusions Facebook provides a forum for reporting personal experiences, asking questions, and receiving direct feedback for people living with diabetes. However, promotional activity and personal data collection are also common, with no accountability or checks for authenticity.

Women are concerned about reducing their breast cancer risk, particularly if they have daughters. Social media platforms, such as blogs written by mothers, are increasingly being recognized as a channel that women use to make personal and family health-related decisions. Government initiatives (eg, Interagency Breast Cancer and Environmental Research Coordinating Committee) and researchers have called for scientists and the community to partner and disseminate scientifically and community-informed environmental risk information. We developed and evaluated a blog intervention to disseminate breast cancer and environmental risk information to mothers. We teamed with mommy bloggers to disseminate a message that we developed and tailored for mothers and daughters based on scientific evidence from the Breast Cancer and the Environment Research Program (BCERP). We posited that the intervention would influence women's exposure to, acceptance of, and beliefs about environmental risks while promoting their intention to adopt risk-reducing behaviors. Using a quasi-experimental design, we recruited 75 mommy bloggers to disseminate the breast cancer risk message on their respective blogs and examined the impact of the intervention on (1) readers exposed to the intervention (n=445) and (2) readers not exposed to the intervention (comparison group; n=353). Following the intervention, blog reader scores indicating exposure to the breast cancer risk and prevention information were greater than scores of blog readers who were not exposed (or did not recall seeing the message; mean 3.92, SD 0.85 and mean 3.45, SD 0.92, respectively; P<.001). Readers who recalled the intervention messages also had higher breast cancer risk and prevention information satisfaction scores compared with readers who did not see (or recall) the messages (mean 3.97, SD 0.75 and mean 3.57, SD 0.94, respectively; P<.001). Blog readers who recalled seeing the intervention messages were significantly more likely to share the breast cancer risk and prevention information they read, with their daughters specifically, than readers who did not recall seeing them (χ =8.1; P=.004). Those who recalled seeing the intervention messages reported significantly higher breast cancer risk and prevention information influence scores, indicative of behavioral intentions, than participants who did not recall seeing them (mean 11.22, SD 2.93 and mean 10.14, SD 3.24, respectively; P=.003). Most women ranked Facebook as their first choice for receiving breast cancer risk information. Results indicated that blog readers who were exposed to (and specifically recalled) the BCERP-adapted intervention messages from mommy bloggers had higher breast cancer risk and prevention information exposure scores and higher breast cancer risk and prevention information satisfaction and influence scores than those who did not see (or recall) them. Mommy bloggers may be important opinion leaders for some women and key to enhancing the messaging, delivery, and impact of environmental breast cancer risk information on mothers.

Use these ten principles to guide research evaluation, urge Diana Hicks, Paul Wouters and colleagues.

With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society.

Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user’s mental state based on his/her posting information. To this end, we collected posts from mental health communities in Reddit . By analyzing and learning posting information written by users, our proposed model could accurately identify whether a user’s post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. We believe our model can help identify potential sufferers with mental illness based on their posts. This study further discusses the implication of our proposed model, which can serve as a supplementary tool for monitoring mental health states of individuals who frequently use social media.

Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 839 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory [corrected]. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.