English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Trustfulness -- one's general tendency to have confidence in unknown peopleor situations -- predicts many important real-world outcomes such as mentalhealth and likelihood to cooperate with others such as clinicians. Whiledata-driven measures of interpersonal trust have previously been introduced,here, we develop the first language-based assessment of the personality traitof trustfulness by fitting one's language to an accepted questionnaire-basedtrust score. Further, using trustfulness as a type of case study, we explorethe role of questionnaire size as well as word count in developinglanguage-based predictive models of users' psychological traits. We find thatleveraging a longer questionnaire can yield greater test set accuracy, while,for training, we find it beneficial to include users who took smallerquestionnaires which offers more observations for training. Similarly, afternoting a decrease in individual prediction error as word count increased, wefound a word count-weighted training scheme was helpful when there were veryfew users in the first place.

Predicting Human Trustfulness from Facebook Language