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.

Detecting that two images are different is faster for highly dissimilar images than for highly similar images. Paradoxically, we showed that the reverse occurs when people are asked to describe how two images differ-that is, to state a difference between two images. Following structuremapping theory, we propose that this disassociation arises from the multistage nature of the comparison process. Detecting that two images are different can be done in the initial (local-matching) stage, but only for pairs with low overlap; thus, ''different'' responses are faster for low-similarity than for high-similarity pairs. In contrast, identifying a specific difference generally requires a full structural alignment of the two images, and this alignment process is faster for high-similarity pairs. We described four experiments that demonstrate this dissociation and show that the results can be simulated using the Structure-Mapping Engine. These results pose a significant challenge for nonstructural accounts of similarity comparison and suggest that structural alignment processes play a significant role in visual comparison.

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.

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.

Research in historical semantics relies on the examination, selection, and interpretation of texts from corpora. Changes in meaning are tracked through the collection and careful inspection of examples that span decades and centuries. This process is inextricably tied to the researcher"s expertise and familiarity with the corpus. Consequently, the results tend to be difficult to quantify and put on an objective footing, and "big-picture" changes in the vocabulary other than the specific ones under investigation may be hard to keep track of. In this paper we present a method that uses Latent Semantic Analysis (Landauer, Foltz & Laham, 1998) to automatically track and identify semantic changes across a corpus. This method can take the entire corpus into account when tracing changes in the use of words and phrases, thus potentially allowing researchers to observe the larger context in which these changes occurred, while at the same time considerably reducing the amount of work required. Moreover, because this measure relies on readily observable co-occurrence data, it affords the study of semantic change a measure of objectivity that was previously difficult to attain. In this paper we describe our method and demonstrate its potential by applying it to several well-known examples of semantic change in the history of the English language.