BackgroundCollege 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.ObjectiveThe 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.MethodsWe 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.ResultsA 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).ConclusionsSentiment and emotions expressed in the tweets have the potential to provide real-time monitoring of stress level and emotional w...
This article proposes a revised lexical approach to understand characteristics of computer games and user experience in game play by analyzing online game reviews. The lexical approach was originally used by psychologists to study personality traits. It was based on a lexical hypothesis stating that personality traits are reflected in the adjectives people have been using to describe personality over a long time. A factor analysis was then conducted to identify the patterns of personality related adjectives (i.e., personality traits). The argument here is that game players have used natural languages to describe computer games and their experiences over time and that the most important characteristics of computer games and game play experience would be reflected in player language. Therefore, the traits of computer games and user experience during game play can be studied by examining the adjectives used by players in online reviews. Based on 696,801 reviews downloaded from three major game websites, the lexical approach was adapted to analyze textual content pertaining to computer games. In two consecutive studies, the most important factors concerning traits of computer games and game play experience were discovered and analyzed. The implications of the revised lexical approach and findings from this project were discussed.
This work-in-progress paper reports the development of a dictionary of game-descriptive words. Inspired by the lexical approach [1] used by psychologists to study personality traits, it is proposed that the same approach can be used in computer game research. The premise is that if computer games display some common traits, players ought to use natural language to describe them. By studying the language used by game players, we can explore the common traits of computer games which may reveal playability problems and information about game classification. As the first step to use the lexical approach, this study attempts to build a dictionary of game-descriptive words for future lexical analyses. The detailed development process was discussed.
BackgroundFreshman 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.ObjectiveOur 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.MethodsBetween 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.ResultsParticipants 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).ConclusionsThe 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 experi...
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