This data was collected in an experiment aiming to establish whether trust in large language models (LLMs) may be inflated in relation to other forms of artificial intelligence, with a particular focus on the content and forms of natural language used. One hundred and ninety-nine residents of the United States were recruited online and presented with a series of general knowledge questions. For each question they also received a recommendation from either an LLM or a non-LLM AI-assistant. The accuracy of this recommendation was also varied. All data is deidentified and there is no missing data. This deidentified data may be used by researchers for the purposes of verifying published results or advancing other research on this topic. Lineage: Data was collected on the Qualtrics survey platform from participants sourced on online recruitment platform, Prolific.

Data for a Brief Report/Short Communication published in Body Image (2021). Details of the study are included below via the abstract from the manuscript. The dataset includes online experimental data from 167 women who were recruited via social media and institutional participant pools. The experiment was completed in Qualtrics.Women viewed either neutral travel images (control), body positivity posts with an average-sized model (e.g., ~ UK size 14), or body positivity posts with a larger model (e.g., UK size 18+); which images women viewed is show in the ‘condition’ variable in the data.The data includes the age range, height, weight, calculated BMI, and Instagram use of participants. After viewing the images, women responded to the Positive and Negative Affect Schedule (PANAS), a state version of the Body Satisfaction Scale (BSS), and reported their immediate social comparison with the images (SAC items). Women then selected a lunch for themselves from a hypothetical menu; these selections are detailed in the data, as are the total calories calculated from this and the proportion of their picks which were (provided as a percentage, and as a categorical variable [as used in the paper analyses]). Women also reported whether they were on a special diet (e.g., vegan or vegetarian), had food intolerances, when they last ate, and how hungry they were.

Women also completed trait measures of Body Appreciation (BAS-2) and social comparison (PACS-R). Women also were asked to comment on what they thought the experiment was about. Items and computed scales are included within the dataset.This item includes the dataset collected for the manuscript (in SPSS and CSV formats), the variable list for the CSV file (for users working with the CSV datafile; the variable list and details are contained within the .sav file for the SPSS version), and the SPSS syntax for our analyses (.sps). Also included are the information and consent form (collected via Qualtrics) and the questions as completed by participants (both in pdf format).Please note that the survey order in the PDF is not the same as in the datafiles; users should utilise the variable list (either in CSV or SPSS formats) to identify the items in the data.The SPSS syntax can be used to replicate the analyses reported in the Results section of the paper. Annotations within the syntax file guide the user through these.

A copy of SPSS Statistics is needed to open the .sav and .sps files.

Manuscript abstract:

Body Positivity (or ‘BoPo’) social media content may be beneficial for women’s mood and body image, but concerns have been raised that it may reduce motivation for healthy behaviours. This study examines differences in women’s mood, body satisfaction, and hypothetical food choices after viewing BoPo posts (featuring average or larger women) or a neutral travel control. Women (N = 167, 81.8% aged 18-29) were randomly assigned in an online experiment to one of three conditions (BoPo-average, BoPo-larger, or Travel/Control) and viewed three Instagram posts for two minutes, before reporting their mood and body satisfaction, and selecting a meal from a hypothetical menu. Women who viewed the BoPo posts featuring average-size women reported more positive mood than the control group; women who viewed posts featuring larger women did not. There were no effects of condition on negative mood or body satisfaction. Women did not make less healthy food choices than the control in either BoPo condition; women who viewed the BoPo images of larger women showed a stronger association between hunger and calories selected. These findings suggest that concerns over BoPo promoting unhealthy behaviours may be misplaced, but further research is needed regarding women’s responses to different body sizes.

As governing bodies continue to explore mileage fees as an alternative to the gas tax, of the uncertainty surrounding public support remains a critical barrier to policy uptake. This study examines the extent to which public perceptions of mileage fees are guided by misinformation or lack of information using a national, internet-based survey. We use hypothetical voting opportunities to gather respondent support for mileage fees, coupled with educational treatments that address mileage fee fairness, privacy, and costs. The findings indicate that respondents are largely misinformed or lack information about mileage fees and the gas tax. Pre-education, only 32% of respondents supported the policy, but post-education, 46% of respondents supported the policy. Through binomial, multinomial, and fixed effect modeling, we examined the factors associated with policy support, changes in policy support, and the educational treatments. Ultimately, our findings indicate that education can play a key role in increasing support for a mileage fee policy as an alternative to the gas tax. Methods An internet-based survey was used to assess nation-wide support for replacing state gas taxes with a mileage fee. Respondents were given three opportunities to vote for or against a mileage fee replacement, with educational treatments in between votes. The impact of education on respondent voting was evaluated using a variety of regression modelling methods. Respondents were recruited to the survey through Qualtrics. This company used quota-based sampling schemes to field the survey to every U.S. state. Since this research hypothesized that mileage fee opinions may be in part due to low information about mileage fees, we opted to omit respondents from states where widespread mileage fee education or mileage fee policies were implemented. As of July 2023, we identified California, Oregon, Utah and Hawaii as states where residents were likely meaningfully more educated about mileage fees and chose not to survey those populations. Three versions of the survey were released, each proposing mileage fees are collected using a different method. The three options proposed collecting mileage fees using (1) an annual odometer reading, (2) a plug-in device without GPS technology, and (3) a plug-in device with GPS technology. Besides differing in the method displayed for collecting mileage information, the surveys were identical.