Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience. Keywords Methodological innovation · Text analysis · Semantic representation · Dictionary-based text analysisElectronic supplementary material The online version of this article

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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.

Endoglin (ENG), a co-receptor for several TGFβ-family cytokines, is expressed in dividing endothelial cells alongside ALK1, the ACVRL1 gene product. ENG and ACVRL1 are both required for angiogenesis and mutations in either gene are associated with Hereditary Hemorrhagic Telangectasia, a rare genetic vascular disorder. ENG and ALK1 function in the same genetic pathway but the relative contribution of TGFβ and BMP9 to SMAD1/5/8 activation and the requirement of ENG as a co-mediator of SMAD phosphorylation in endothelial cells remain debated. Here, we show that BMP9 and TGFβ1 induce distinct SMAD phosphorylation responses in primary human endothelial cells and that, unlike BMP9, TGFβ only induces SMAD1/5/8 phosphorylation in a subset of immortalized mouse endothelial cell lines, but not in primary human endothelial cells. We also demonstrate, using siRNA depletion of ENG and novel anti-ENG antibodies, that ENG is required for BMP9/pSMAD1 signaling in all human and mouse endothelial cells tested. Finally, anti-ENG antibodies that interfere with BMP9/pSMAD1 signaling, but not with TGFβ1/pSMAD3 signaling, also decrease in vitro HUVEC endothelial tube formation and inhibit BMP9 binding to recombinant ENG in vitro. Our data demonstrate that BMP9 signaling inhibition is a key and previously unreported mechanism of action of TRC105, an anti-angiogenic anti-Endoglin antibody currently evaluated in clinical trials.

Research has shown that accounting for moral sentiment in natural language can yield insight into a variety of on- and off-line phenomena such as message diffusion, protest dynamics, and social distancing. However, measuring moral sentiment in natural language is challenging, and the difficulty of this task is exacerbated by the limited availability of annotated data. To address this issue, we introduce the Moral Foundations Twitter Corpus, a collection of 35,108 tweets that have been curated from seven distinct domains of discourse and hand annotated by at least three trained annotators for 10 categories of moral sentiment. To facilitate investigations of annotator response dynamics, we also provide psychological and demographic metadata for each annotator. Finally, we report moral sentiment classification baselines for this corpus using a range of popular methodologies.