@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}
}
