Abstract
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.
Highlights
Twitter is a social media platform which allows users to post text messages of up to140-characters in length
We quantitatively explore the expression of positive and negative affective states, performing a cross-linguistic (Arabic/English) sentiment analysis of the United Arab Emirates (UAE) Twitter dataset over a 5 years span (January 1, 2013 to August 31, 2017) comprising over 17 million tweets
The UAE Twitter dataset for five years (January 1, 2013 to August 31, 2017) was used in the present analysis
Summary
Twitter is a social media platform which allows users to post text messages of up to. We quantitatively explore the expression of positive and negative affective states, performing a cross-linguistic (Arabic/English) sentiment analysis of the UAEs Twitter dataset over a 5 years span (January 1, 2013 to August 31, 2017) comprising over 17 million tweets. 2. Based on Hedonometer [10, 11], is language use (Arabic/English) associated with differences in the expression of positive sentiment (Happiness)?. The large quantities of data involved in this type of social media exploration have led to the development of sophisticated analytic techniques based on natural language processing Such affect-focused techniques have generally been referred to as sentiment analysis [18]. Hedonometer is based on a language assessment by Mechanical Turk 1.0 (LabMT-1.0) sentiment lexicon This technique is used to perform a quantitative content analysis for valence (positive vs negative affect) of each tweet across the Twitter dataset. The word “love” is represented by a orange bar, which indicates that it is a happy word, and its use has decreased as “love” is shown on the left side of the graph to represent a decrease in the happiness
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
