Arabia Felix 2.0: a cross-linguistic Twitter analysis of happiness patterns in the United Arab Emirates

The global popularity of social media platforms has given rise to unprecedented amounts of data, much of which reflects the thoughts, opinions and affective states of individual users. Systematic explorations of these large datasets can yield valuable information about a variety of psychological and sociocultural variables. The global nature of these platforms makes it important to extend this type of exploration across cultures and languages as each situation is likely to present unique methodological challenges and yield findings particular to the specific sociocultural context. To date, very few studies exploring large social media datasets have focused on the Arab world. This study examined social media use in Arabic and English across the United Arab Emirates (UAE), looking specifically at indicators of subjective wellbeing (happiness) across both languages. A large social media dataset, spanning 2013 to 2017, was extracted from Twitter. More than 17 million Twitter messages (tweets), written in Arabic and English and posted by users based in the UAE, were analyzed. Numerous differences were observed between individuals posting messages (tweeting) in English compared with those posting in Arabic. These differences included significant variations in the mean number of tweets posted, and the mean size of users networks (e.g. the number of followers). Additionally, using lexicon-based sentiment analytic tools (Hedonometer and Valence Shift Word Graphs), temporal patterns of happiness (expressions of positive sentiment) were explored in both languages across all seven regions (Emirates) of the UAE. Findings indicate that 7:00 am was the happiest hour, and Friday was the happiest day for both languages (the least happy day varied by language). The happiest months differed based on language, and there were also significant variations in sentiment patterns, peaks and troughs in happiness, associated with events of sociopolitical and religio-cultural significance for the UAE.

[1]  Scott A. Golder,et al.  Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .

[2]  Johanna D. Moore,et al.  Twitter Sentiment Analysis: The Good the Bad and the OMG! , 2011, ICWSM.

[3]  Justin Thomas,et al.  Sunshine, Sadness and Seasonality: 25-Hydroxyvitamin D, and Depressive Symptoms in the United Arab Emirates (UAE) , 2011 .

[4]  Yun Ying Zhong,et al.  Twitter sentiment analysis: Capturing sentiment from integrated resort tweets , 2016 .

[5]  Saif Mohammad,et al.  From once upon a time to happily ever after: Tracking emotions in mail and books , 2012, Decis. Support Syst..

[6]  S H Jones,et al.  Circadian rhythms, multilevel models of emotion and bipolar disorder--an initial step towards integration? , 2001, Clinical psychology review.

[7]  James P. Bagrow,et al.  Human language reveals a universal positivity bias , 2014, Proceedings of the National Academy of Sciences.

[8]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[9]  Scott Sanner,et al.  Improving LDA topic models for microblogs via tweet pooling and automatic labeling , 2013, SIGIR.

[10]  Wenyin Liu,et al.  Affective topic model for social emotion detection , 2014, Neural Networks.

[11]  D. Watson,et al.  Toward a consensual structure of mood. , 1985, Psychological bulletin.

[12]  Daniela Hirschfeld.  Twitter data accurately tracked Haiti cholera outbreak , 2012, Nature.

[13]  Christopher M. Danforth,et al.  Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll , 2015, PloS one.

[14]  Padmini Srinivasan,et al.  Life Satisfaction and the Pursuit of Happiness on Twitter , 2016, PloS one.

[15]  Xiao Wang,et al.  World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans' tweets , 2015, Comput. Hum. Behav..

[16]  A. Abdel-Khalek,et al.  Religiosity, Health, and Well-Being among Kuwaiti Personnel , 2008, Psychological reports.

[17]  Christopher M. Danforth,et al.  Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter , 2011, PloS one.

[18]  Jiawei Yuan,et al.  Mining Twitter to Assess the Public Perception of the “Internet of Things” , 2016, PloS one.

[19]  Daniele Quercia,et al.  Tracking "gross community happiness" from tweets , 2012, CSCW.

[20]  Declan Butler,et al.  When Google got flu wrong , 2013, Nature.

[21]  A. Wirz-Justice,et al.  Circadian Rhythm Mechanisms in Affective Illness and in Antidepressant Drug Action , 1982, Pharmacopsychiatria.

[22]  Xun Wang,et al.  What a Nasty Day: Exploring Mood-Weather Relationship from Twitter , 2014, CIKM.

[23]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[24]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[25]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[26]  Christopher M. Danforth,et al.  Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents , 2010, ArXiv.

[27]  Christopher M. Davidson,et al.  Higher education in the Gulf States : shaping economies, politics and culture , 2008 .

[28]  Justin Thomas,et al.  Psychological Well-Being in the Gulf States , 2013 .