@article {bib_71,
	title = {Purity Homophily in Social Networks.},
	journal = {Journal of Experimental Psychology: General},
	volume = {145},
	year = {2016},
	pages = {366-375},
	abstract = {Does sharing moral values encourage people to connect and form communities? The importance of moral homophily (love of same) has been recognized by social scientists, but the types of moral similarities that drive this phenomenon are still unknown. Using both large-scale, observational social-media analyses and behavioral lab experiments, the authors investigated which types of moral similarities influence tie formations. Analysis of a corpus of over 700,000 tweets revealed that the distance between 2 people in a social-network can be predicted based on differences in the moral purity content{\textemdash}but not other moral content{\textemdash}of their messages. The authors replicated this finding by experimentally manipulating perceived moral difference (Study 2) and similarity (Study 3) in the lab and demonstrating that purity differences play a significant role in social distancing. These results indicate that social network processes reflect moral selection, and both online and offline differences in moral purity concerns are particularly predictive of social distance. This research is an attempt to study morality indirectly using an observational big-data study complemented with 2 confirmatory behavioral experiments carried out using traditional social-psychology methodology.},
	doi = {10.1037/xge0000139},
	url = {http://dx.doi.org/10.1037/xge0000139},
	author = {Morteza Dehghani and Kate Johnson and Joe Hoover and Eyal Sagi and Justin Garten and Niki Jitendra Parmar and Stephen Vaisey and Rumen Iliev and Jesse Graham}
}
@article {56,
	title = {Automated Text Analysis in Psychology: Methods, Applications and Future Developments},
	journal = {Language and Cognition},
	volume = {7},
	year = {2015},
	pages = {265-290},
	abstract = {Recent years have seen rapid developments in automated text analysis methods focused on measuring psychological and demographic properties. While this development has mainly been driven by computer scientists and computational linguists, such methods can be of great value for social scientists in general, and for psychologists in particular. In this paper, we review some of the most popular approaches to automated text analysis from the perspective of social scientists, and give examples of their applications in different theoretical domains. After describing some of the pros and cons of these methods, we speculate about future methodological developments, and how they might change social sciences. We conclude that despite the fact that current methods have many disadvantages and pitfalls compared to more traditional methods of data collection, the constant increase of computational power and the wide availability of textual data will inevitably make automated text analysis a common tool for psychologists.},
	doi = {10.1017/langcog.2014.30},
	author = {Rumen Iliev and Morteza Dehghani and Eyal Sagi}
}
@article {bib_1,
	title = {Measuring Moral Rhetoric in Text},
	journal = {Social Science Computer Review},
	volume = {32},
	year = {2014},
	pages = {132-144},
	abstract = {In this paper we present a computational text analysis technique for measuring the moral loading of concepts as they are used in a corpus. This method is especially useful for the study of online corpora as it allows for the rapid analysis of moral rhetoric in texts such as blogs and tweets as events unfold. We use Latent Semantic Analysis to compute the semantic similarity between concepts and moral keywords taken from the Moral Foundation Dictionary. This measure of semantic similarity represents the loading of these concepts on the five moral dimensions identified by Moral Foundation Theory. We demonstrate the efficacy of this method using three different concepts and corpora.},
	issn = {0894-4393},
	doi = {10.1177/0894439313506837},
	url = {http://ssc.sagepub.com/cgi/doi/10.1177/0894439313506837},
	author = {Eyal Sagi and Morteza Dehghani}
}
@conference {bib_53,
	title = {Moral Rhetoric in Twitter: A Case Study of the U.S. Federal Shutdown of 2013},
	booktitle = {Proceedings of the 36th Annual Conference of the Cognitive Science Society},
	year = {2014},
	pages = {1347-1352},
	abstract = {In this paper we apply a computational text analysis technique used for measuring moral rhetoric in text to analyze the moral loadings of tweets. We focus our analysis on tweets regarding the 2013 federal government shutdown; a topic that was at the forefront of U.S. politics in late 2013.
Our results demonstrate that the positions of the members of the two major political parties are mirrored by the positions taken by the Twitter communities that are aligned with them. We also analyze retweeting behavior by examining the differences in the moral loadings of intra-community and inter-community retweets. We find that retweets in our corpus favor rhetoric that enhances the cohesion of the community, and emphasize content over moral rhetoric.
We argue that the method proposed in this paper contributes to the general study of moral cognition and social behavior.},
	author = {Eyal Sagi and Morteza Dehghani},
	editor = {Paul Bello and Marcello Guarini and Marjorie McShane and Brian Scassellati}
}
