Objective. What effect does the extent of economic inequality within a country have on the religiosity of the people who live there? As inequality increases, does religion serve primarily as a source of comfort for the deprived and impoverished or as a tool of social control for the rich and powerful? Methods. This article examines these questions with two complementary analyses of inequality and religiosity: a multilevel analysis of countries around the world over two decades and a time-series analysis of the United States over a half-century. Results. Economic inequality has a strong positive effect on the religiosity of all members of a society regardless of income. Conclusions. These results support relative power theory, which maintains that greater inequality yields more religiosity by increasing the degree to which wealthy people are attracted to religion and have the power to shape the attitudes and beliefs of those with fewer means.Recent work in the sociology of religion has largely neglected the role of economic inequality. This study illustrates the benefits to be gained by reincorporating economic inequality into our understanding of religion by examining whether and how greater inequalities in the distributions of economic resources within societies affect the religiosity of their members. We examine two competing theories of how the extent of economic inequality may influence levels of religiosity: deprivation theory and relative power theory. The first focuses on religion's value to the poor, the second on its utility to the rich. As a result, they yield distinctly different predictions of inequality's effects. We also consider whether increased religiosity could be the source of greater inequality rather than its consequence.To test these rival theories, we present a multilevel analysis of religiosity across dozens of countries over two decades and a time-series analysis of trends in religiosity over half a century in the United States. Our findings provide strong support only for the relative power theory, which maintains n

This paper replicates and extends Groseclose, Levitt, and Snyder, “Comparing Interest Group Scores Across Time and Chambers: Adjusted ADA Scores for the U.S. Congress,” which appeared in the American Political Science Review (1999/93:33–50). We replicate the most recent unpublished extension by Dr. Groseclose and research assistants for years 1947–1999, and then we extend the analysis to include years 2000 through 2007. We make available inflation-adjusted ADA scores from 1947 through 2007, allowing scholars to incorporate the most recent interest group scores into their analyses.

Scholars often seek to understand topics discussed on Twitter using topic modelling approaches. Several coherence metrics have been proposed for evaluating the coherence of the topics generated by these approaches, including the pre-calculated Pointwise Mutual Information (PMI) of word pairs and the Latent Semantic Analysis (LSA) word representation vectors. As Twitter data contains abbreviations and a number of peculiarities (e.g. hashtags), it can be challenging to train effective PMI data or LSA word representation. Recently, Word Embedding (WE) has emerged as a particularly effective approach for capturing the similarity among words. Hence, in this paper, we propose new Word Embedding-based topic coherence metrics. To determine the usefulness of these new metrics, we compare them with the previous PMI/LSA-based metrics. We also conduct a large-scale crowdsourced user study to determine whether the new Word Embedding-based metrics better align with human preferences. Using two Twitter datasets, our results show that the WE-based metrics can capture the coherence of topics in tweets more robustly and efficiently than the PMI/LSA-based ones.

Abstract. Twitter offers scholars new ways to understand the dynamics of public opinion and social discussions. However, in order to understand such discussions, it is necessary to identify coherent topics that have been discussed in the tweets. To assess the coherence of topics, several automatic topic coherence metrics have been designed for classical document corpora. However, it is unclear how suitable these metrics are for topic models generated from Twitter datasets. In this paper, we use crowdsourcing to obtain pairwise user preferences of topical coherences and to determine how closely each of the metrics align with human preferences. Moreover, we propose two new automatic coherence metrics that use Twitter as a separate background dataset to measure the coherence of topics. We show that our proposed Pointwise Mutual Information-based metric provides the highest levels of agreement with human preferences of topic coherence over two Twitter datasets.

In the recent Scottish Independence Referendum (hereafter, IndyRef ), Twitter offered a broad platform for people to express their opinions, with millions of IndyRef tweets posted over the campaign period. In this paper, we aim to classify people's voting intentions by the content of their tweetstheir short messages communicated on Twitter. By observing tweets related to the IndyRef, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions ("Yes"-in favour of Independence vs. "No"-Opposed). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline.