This article contains data collected from self-report surveys of respondents to measure 1) social media usage, 2) age, 3) gender and 4) trust, measured within five major trust constructs of a) Integrity, b) Competence, c) Concern, d) Benevolence and e) Identification. The data includes all instruments used, SPSS syntax, the raw survey data and descriptive statistics from the analyses. Raw data was entered into SPSS software and scrubbed using appropriate techniques in order to prepare the data for analysis. We believe that our dataset and instrument may give important insights related to computers in human behavior, and predicting trust antecedents in social media use such as age, gender, number of hour online and choice of content provider. We have also created a parsimonious five factor trust instrument developed from the extant literature for future research. Hence, this newly developed trust instrument can be used to measure trust not only in social media, but also in other areas such as healthcare, economics and investor relations, CSR, management and education. Moreover, the survey items developed to measure social media use are concise and may be applied to measure social media use in other contexts such as national cultural differences, marketing and tourism. For interpretation and discussion of the data and constructs, please see original article entitled “Who trusts social media” (Warner-Søderholm et al., 2018) [1].

Data

The data presented in this article document the responses to the newly revised 25 trust construct measurement items from 214 respondents from a convenience sample of university students and faculty (see Supplementary file in the Appendix). Data for 3 items developed to measure contemporary social media use are also presented along with 3 demographic items used in the study. In addition, the article provides copies of the instruments that were used to gather the data and the SPSS syntax, making the instrument freely available for future academic and business research. The subsequent raw data, as well as descriptive statistics can be added to new datasets. The responses may also be used to seek correlation or may be selected based on subsets to test new hypotheses in future studies.