Search

BackgroundSocial media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users’ demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. ObjectiveWe aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. MethodsThis study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users’ age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. ResultsThe gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. ConclusionsWe created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users’ posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

Background: Mass shootings result in widespread psychological trauma for survivors and members of the affected community. However, less is known about the broader effects of indirect exposure (eg, media) to mass shootings. Crisis lines offer a unique opportunity to examine real-time data on the widespread psychological effects of mass shootings., Objective: Crisis Text Line is a not-for-profit company that provides 24/7 confidential SMS text message-based mental health support and crisis intervention service. This study examines changes in the volume and composition of firearm-related conversations at Crisis Text Line before and after the mass school shooting at Robb Elementary School on May 24, 2022, in Uvalde, Texas., Methods: A quasi-experimental event study design was used to compare the actual volume of firearm-related conversations received by Crisis Text Line post shooting to forecasted firearm conversation volume under the counterfactual scenario that a shooting had not occurred. Conversations related to firearms were identified among all conversations using keyword searches. Firearm conversation volume was predicted using a seasonal autoregressive integrated moving average model trained on the 3 months of data leading up to the shooting. Additionally, proportions of issue tags (topics coded post conversation by volunteer crisis counselors at Crisis Text Line after the exchange) were compared in the 4 days before (n=251) and after (n=417) the shooting to assess changes in conversation characteristics. The 4-day window was chosen to reflect the number of days conversation volume remained above forecasted levels., Results: There was a significant increase in the number of conversations mentioning firearms following the shooting, with the largest spike (compared to forecasted numbers) occurring the day after the shooting (n=159) on May 25, 2022. By May 28, the volume reverted to within the 95% CI of the forecasted volume (n=77). Within firearm conversations, "grief" issue tags showed a significant increase in proportion in the week following the shooting, while "isolation/loneliness," "relationships," and "suicide" issue tags showed a significant decrease in proportions the week following the shooting., Conclusions: The results suggest that the Uvalde school shooting may have contributed to an increase in demand for crisis services, above what would be expected given historical trends. Additionally, we found that these firearm-related crises conversations immediately post event are more likely to be related to grief and less likely to be related to suicide, loneliness, and relationships. Our findings provide some of the first data showing the real-time repercussions for the broader population exposed to school shooting events. This work adds to a growing evidence base documenting and measuring the rippling effects of mass shootings outside of those directly impacted., (©Kirsty J Weitzel, Robert F Chew, Adam Bryant Miller, Caroline W Oppenheimer, Ashley Lowe, Anna Yaros. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 08.02.2023.)

Background: Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette-related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette-related content on Twitter., Objective: The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette-related topics into distinct categories., Methods: We collected approximately 11.5 million e‑cigarette-related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles' profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users' metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types., Results: Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F 1 score=83.3%). Accuracy varied by user type, with F 1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance-an overall F 1 score gain of 10.6%-beyond metadata features alone., Conclusions: This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts., (©Annice Kim, Thomas Miano, Robert Chew, Matthew Eggers, James Nonnemaker. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 26.09.2017.)

Background: Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks., Objectives: This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California., Methods: From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities., Results: The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles., Conclusions: The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time., (©Nicholas C Peiper, Peter M Baumgartner, Robert F Chew, Yuli P Hsieh, Gayle S Bieler, Georgiy V Bobashev, Christopher Siege, Gary A Zarkin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.07.2017.)