Social media data can provide valuable information regarding people's behaviors and health outcomes. Previous studies have shown that social media data can be extracted to monitor and predict infectious disease outbreaks. These same approaches can be applied to other fields including physical activity research and forensic science. Social media data have the potential to provide real-time monitoring and prediction of physical activity level in a given region. This tool can be valuable to public health organizations as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (e.g. surveys, observational studies). As a result, this tool could help public health organizations better mobilize and target physical activity interventions. The first part of this paper aims to describe current approaches (e.g. topic modeling, sentiment analysis and social network analysis) that could be used to analyze social media data to provide real-time monitoring of physical activity level. The second aim of this paper was to discuss ways to apply social media analysis to other fields such as forensic sciences and provide recommendations to further social media research.

Background

Freshman experiences can greatly influence students' success. Traditional methods of monitoring the freshman experience, such as conducting surveys, can be resource intensive and time consuming. Social media, such as Twitter, enable users to share their daily experiences. Thus, it may be possible to use Twitter to monitor students' postsecondary experience.

Objective

Our objectives were to (1) describe the proportion of content posted on Twitter by college students relating to academic studies, personal health, and social life throughout the semester; and (2) examine whether the proportion of content differed by demographics and during nonexam versus exam periods.

Methods

Between October 5 and December 11, 2015, we collected tweets from 170 freshmen attending the University of California Los Angeles, California, USA, aged 18 to 20 years. We categorized the tweets into topics related to academic, personal health, and social life using keyword searches. Mann-Whitney U and Kruskal-Wallis H tests examined whether the content posted differed by sex, ethnicity, and major. The Friedman test determined whether the total number of tweets and percentage of tweets related to academic studies, personal health, and social life differed between nonexam (weeks 1-8) and final exam (weeks 9 and 10) periods.

Results

Participants posted 24,421 tweets during the fall semester. Academic-related tweets (n=3433, 14.06%) were the most prevalent during the entire semester, compared with tweets related to personal health (n=2483, 10.17%) and social life (n=1646, 6.74%). The proportion of academic-related tweets increased during final-exam compared with nonexam periods (mean rank 68.9, mean 18%, standard error (SE) 0.1% vs mean rank 80.7, mean 21%, SE 0.2%; Z=-2.1, P=.04). Meanwhile, the proportion of tweets related to social life decreased during final exams compared with nonexam periods (mean rank 70.2, mean 5.4%, SE 0.01% vs mean rank 81.8, mean 7.4%, SE 0.01%; Z=-4.8, P<.001). Women tweeted more often than men during both nonexam (mean rank 95.8 vs 76.8; U=2876, P=.02) and final-exam periods (mean rank 96.2 vs 76.2; U=2832, P=.01). The percentages of academic-related tweets were similar between ethnic groups during nonexam periods (P>.05). However, during the final-exam periods, the percentage of academic tweets was significantly lower among African Americans than whites (χ²₄=15.1, P=.004). The percentages of tweets related to academic studies, personal health, and social life were not significantly different between areas of study during nonexam and exam periods (P>.05).

Conclusions

The results suggest that the number of tweets related to academic studies and social life fluctuates to reflect real-time events. Student's ethnicity influenced the proportion of academic-related tweets posted. The findings from this study provide valuable information on the types of information that could be extracted from social media data. This information can be valuable for school administrators and researchers to improve students' university experience.

Crime monitoring tools are needed for public health and law enforcement officials to deploy appropriate resources and develop targeted interventions. Social media, such as Twitter, has been shown to be a feasible tool for monitoring and predicting public health events such as disease outbreaks. Social media might also serve as a feasible tool for crime surveillance. In this study, we collected Twitter data between May and December 2012 and crime data for the years 2012 and 2013 in the United States. We examined the association between crime data and drug-related tweets. We found that tweets from 2012 were strongly associated with county-level crime data in both 2012 and 2013. This study presents preliminary evidence that social media data can be used to help predict future crimes. We discuss how future research can build upon this initial study to further examine the feasibility and effectiveness of this approach.

Background

College can be stressful for many freshmen as they cope with a variety of stressors. Excess stress can negatively affect both psychological and physical health. Thus, there is a need to find innovative and cost-effective strategies to help identify students experiencing high levels of stress to receive appropriate treatment. Social media use has been rapidly growing, and recent studies have reported that data from these technologies can be used for public health surveillance. Currently, no studies have examined whether Twitter data can be used to monitor stress level and emotional state among college students.

Objective

The primary objective of our study was to investigate whether students' perceived levels of stress were associated with the sentiment and emotions of their tweets. The secondary objective was to explore whether students' emotional state was associated with the sentiment and emotions of their tweets.

Methods

We recruited 181 first-year freshman students aged 18-20 years at University of California, Los Angeles. All participants were asked to complete a questionnaire that assessed their demographic characteristics, levels of stress, and emotional state for the last 7 days. All questionnaires were completed within a 48-hour period. All tweets posted by the participants from that week (November 2 to 8, 2015) were mined and manually categorized based on their sentiment (positive, negative, neutral) and emotion (anger, fear, love, happiness) expressed. Ordinal regressions were used to assess whether weekly levels of stress and emotional states were associated with the percentage of positive, neutral, negative, anger, fear, love, or happiness tweets.

Results

A total of 121 participants completed the survey and were included in our analysis. A total of 1879 tweets were analyzed. A higher level of weekly stress was significantly associated with a greater percentage of negative sentiment tweets (beta=1.7, SE 0.7; P=.02) and tweets containing emotions of fear (beta=2.4, SE 0.9; P=.01) and love (beta=3.6, SE 1.4; P=.01). A greater level of anger was negatively associated with the percentage of positive sentiment (beta=-1.6, SE 0.8; P=.05) and tweets related to the emotions of happiness (beta=-2.2, SE 0.9; P=.02). A greater level of fear was positively associated with the percentage of negative sentiment (beta=1.67, SE 0.7; P=.01), particularly a greater proportion of tweets related to the emotion of fear (beta=2.4, SE 0.8; P=.01). Participants who reported a greater level of love showed a smaller percentage of negative sentiment tweets (beta=-1.3, SE 0.7; P=0.05). Emotions of happiness were positively associated with the percentage of tweets related to the emotion of happiness (beta=-1.8, SE 0.8; P=.02) and negatively associated with percentage of negative sentiment tweets (beta=-1.7, SE 0.7; P=.02) and tweets related to the emotion of fear (beta=-2.8, SE 0.8; P=.01).

Conclusions

Sentiment and emotions expressed in the tweets have the potential to provide real-time monitoring of stress level and emotional well-being in college students.

Young children receiving outpatient cancer care are vulnerable to undermanaged pain. App-based solutions that provide pain treatment advice to parents in real-time and in all environments may improve access to quality pain care. We used a parent co-design approach involving iterative rounds of user testing and software modification to develop a usable Pain Caregiver Resource (PainCaRe) real-time pediatric cancer pain management app. Parents of children (2-11 years) with cancer completed three standardized modules using a PainCaRe prototype. App usability and acceptability were evaluated using the validated System Usability Scale and a thematic analysis of app testing sessions and interviews. Iterative testing sessions were conducted until data saturation. Interview themes were synthesized into action items that guided revisions to PainCaRe and additional testing rounds were conducted as necessary. Twenty-two parents participated in three testing cycles. Overall, parents described PainCaRe as an acceptable and potentially clinically useful pain management tool. Mean system usability scores were in the acceptable scale range during each testing cycle. Usability issues identified and resolved included those related to software malfunction, complicated app navigation logic, lack of clarity on pain assessment questions, and the need for pain management advice specifically tailored to child developmental stage. Using co-design methods, the PainCaRe cancer pain management app was successfully refined for its acceptability and utility to parents. Next steps will include a PainCaRe pilot study before evaluating the impact of the app on younger childrens pain outcomes in a randomized controlled trial.