Religiosity is a powerful force shaping human societies, affecting domains as diverse as economic growth or the ability to cope with illness. As more religious leaders and organizations as well as believers start using social networking sites (e.g., Twitter, Facebook), online activities become important extensions to traditional religious rituals and practices. However, there has been lack of research on religiosity in online social networks. This paper takes a step toward the understanding of several important aspects of religiosity on Twitter, based on the analysis of more than 250k U.S. users who self-declared their religions belief, including Atheism, Buddhism, Christianity, Hinduism, Islam, and Judaism. Specifically, (i) we examine the correlation of geographic distribution of religious people between Twitter and offline surveys. (ii) We analyze users' tweets and networks to identify discriminative features of each religious group, and explore supervised methods to identify believers of different religions. (iii) We study the linkage preference of different religious groups, and observe a strong preference of Twitter users connecting to others sharing the same religion.
While much research effort has been made to understand religious use of Internet technologies, we know very little about religiosity in online social networks. On the other hand, there is recently an explosion of studies on Twitter @cite_33 @cite_17 @cite_23 @cite_24 @cite_14 @cite_21 @cite_26 , yet we do not know much specifically about religiosity on Twitter. @cite_29 develop classifiers to detect Twitter users from different categories, including category ; Nguyen and Lim @cite_5 build classifiers to identify Christian and Muslim users using their Twitter data, but neither of the two studies addresses the analysis of the phenomenon of religion on Twitter. @cite_22 appears to be the most relevant study, which focuses on exploring the relationship between religion and happiness via examining the different use of words (e.g., sentiment words, words related to thinking styles) in tweets between Christians and Atheists. Our present work differs both in scope and purpose.
@cite_14 @cite_26 @cite_33 @cite_22 @cite_29 @cite_21 @cite_24 @cite_23 @cite_5 @cite_17
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We analyze data from nearly 2 million text messages (tweets) across over 16,000 users on Twitter to examine differences between Christians and atheists in natural language. Analyses reveal that Christians use more positive emotion words and less negative emotion words than atheists. Moreover, two independent paths predict differences in expressions of happiness: frequency of words related to an intuitive (vs. analytic) thinking style and frequency of words related to social relationships. These findings provide the first evidence that the relationship between religion and happiness is partially mediated by thinking style. This research also provides support for previous laboratory studies and self-report data, suggesting that social connection partially mediates the relationship between religiosity and happiness. Implications for theory and the future of social science using computational methods to analyze social media are discussed. Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to identify users who are important in the religious user community. We further define features using representative users. We show that SVM classifiers using our proposed features can accurately assign Christian and Muslim labels to a set of Twitter users with known religion labels. The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association. The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby's murder to train and test a supervised machine learning text classifier that distinguishes between hateful and or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.