The question of why people are motivated to act altruistically has been an important one for centuries, and across various disciplines. Drawing on previous research on moral regulation, we propose a framework suggesting that moral (or immoral) behavior can result from an internal balancing of moral self-worth and the cost inherent in altruistic behavior. In Experiment 1, participants were asked to write a self-relevant story containing words referring to either positive or negative traits. Participants who wrote a story referring to the positive traits donated one fifth as much as those who wrote a story referring to the negative traits. In Experiment 2, we showed that this effect was due specifically to a change in the self-concept. In Experiment 3, we replicated these findings and extended them to cooperative behavior in environmental decision making. We suggest that affirming a moral identity leads people to feel licensed to act immorally. However, when moral identity is threatened, moral behavior is a means to regain some lost self-worth.

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.

A long tradition in decision making assumes that people usually take a consequentialist perspective, which implies a focus on the outcomes only when making decisions. Such a view largely neglects the existence of a deontological perspective, which implies that people are sensitive to moral duties that require or prohibit certain behaviors, irrespective of the consequences. Similarly, recent research has also suggested that people holding ''protected values'' (PVs) show increased attention to acts versus omissions and less attention to outcomes. The present research investigates the role of deontological versus consequentialist modes of thought and of PVs on framing effects and act versus omission choices. In a modification of Tversky and Kahneman's (1981) risky choice framing paradigm, we manipulated the framing of the outcomes (positive, negative), as well as whether the certain outcome was associated with an act or inaction. The main results suggest that act versus omission tendencies are linked to deontological focus and PVs. Framing effects, on the other hand, are driven by a consequentialist focus.

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-but not other moral content-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.