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
2101196063 2008803468 2108073218 2094373579 1629520119 2091325569 2088337754 2064178288
Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing. We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. We have classified the trending topics based on the active period and the tweets and show that the majority (over 85 ) of topics are headline news or persistent news in nature. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1,000 users no matter what the number of followers is of the original tweet. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet. To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it. We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses—which is consistent with the effects of sleep and circadian rhythm—and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends. 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. Finding the ''right people'' is a central aspect of social media systems. Twitter has millions of users who have varied interests, professions and personalities. For those in fields such as advertising and marketing, it is important to identify certain characteristics of users to target. However, Twitter users do not generally provide sufficient information about themselves on their profile which makes this task difficult. In response, this work sets out to automatically infer professions (e.g., musicians, health sector workers, technicians) and personality related attributes (e.g., creative, innovative, funny) for Twitter users based on features extracted from their content, their interaction networks, attributes of their friends and their activity patterns. We develop a comprehensive set of latent features that are then employed to perform efficient classification of users along these two dimensions (profession and personality). Our experiments on a large sample of Twitter users demonstrate both a high overall accuracy in detecting profession and personality related attributes as well as highlighting the benefits and pitfalls of various types of features for particular categories of users. This article investigates the impact of user homophily on the social process of information diffusion in online social media. Over several decades, social scientists have been interested in the idea that similarity breeds connection: precisely known as "homophily". Homophily has been extensively studied in the social sciences and refers to the idea that users in a social system tend to bond more with ones who are similar to them than to ones who are dissimilar. The key observation is that homophily structures the ego-networks of individuals and impacts their communication behavior. It is therefore likely to effect the mechanisms in which information propagates among them. To this effect, we investigate the interplay between homophily along diverse user attributes and the information diffusion process on social media. In our approach, we first extract diffusion characteristics---corresponding to the baseline social graph as well as graphs filtered on different user attributes (e.g. location, activity). Second, we propose a Dynamic Bayesian Network based framework to predict diffusion characteristics at a future time. Third, the impact of attribute homophily is quantified by the ability of the predicted characteristics in explaining actual diffusion, and external variables, including trends in search and news. Experimental results on a large Twitter dataset demonstrate that choice of the homophilous attribute can impact the prediction of information diffusion, given a specific metric and a topic. In most cases, attribute homophily is able to explain the actual diffusion and external trends by 15-25 over cases when homophily is not considered. The 5-year-old social media Web site Twitter now claims that more than 100 million users post 230 million "tweets" (text messages up to 140 characters long) every day. In that torrent of data, some social scientists see an unprecedented opportunity to study human communication and social networks. On page [1878][1] of this week's issue of Science , researchers report their effort to use Twitter to study the collective moods of millions of people in diverse cultures around the world in real time. Their findings paint a portrait of humanity's mood swings. @PARASPLIT [1]: http: www.sciencemag.org cgi content full 333 6051 1878 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 importance of quantifying the nature and intensity of emotional states at the level of populations is evident: we would like to know how, when, and why individuals feel as they do if we wish, for example, to better construct public policy, build more successful organizations, and, from a scientific perspective, more fully understand economic and social phenomena. Here, by incorporating direct human assessment of words, we quantify happiness levels on a continuous scale for a diverse set of large-scale texts: song titles and lyrics, weblogs, and State of the Union addresses. Our method is transparent, improvable, capable of rapidly processing Web-scale texts, and moves beyond approaches based on coarse categorization. Among a number of observations, we find that the happiness of song lyrics trends downward from the 1960s to the mid 1990s while remaining stable within genres, and that the happiness of blogs has steadily increased from 2005 to 2009, exhibiting a striking rise and fall with blogger age and distance from the Earth’s equator.