miladrogha / youdrawit-visual-elicitation

The supplementary repository for the paper The Impact of Elicitation and Contrasting Narratives on Engagement, Recall and Attitude Change with News Articles Containing Data Visualization

The Impact of Elicitation and Contrasting Narratives on Engagement, Recall and Attitude Change with News Articles Containing Data Visualization

Milad Rogha , Subham Sah, Alireza Karduni , Douglas Markant, and Wenwen Dou

Abstract

News articles containing data visualizations play an important role in informing the public on issues ranging from public health to politics. Recent research on the persuasive appeal of data visualizations suggests that prior attitudes can be notoriously difficult to change. Inspired by an NYT article, we designed two experiments to evaluate the impact of elicitation and contrasting narratives on attitude change, recall, and engagement. We hypothesized that eliciting prior beliefs leads to more elaborative thinking that ultimately results in higher attitude change, better recall, and engagement. Our findings revealed that visual elicitation leads to higher engagement in terms of feelings of surprise. While there is an overall attitude change across all experiment conditions, we did not observe a significant effect of belief elicitation on attitude change. With regard to recall error, while participants in the draw trend elicitation exhibited significantly lower recall error than participants in the categorize trend condition, we found no significant difference in recall error when comparing elicitation conditions to no elicitation. In a follow-up study, we added contrasting narratives with the purpose of making the main visualization (communicating data on the focal issue) appear strikingly different. Compared to the results of study 1, we found that contrasting narratives improved engagement in terms of surprise and interest but interestingly resulted in higher recall error and no significant change in attitude. We discuss the effects of elicitation and contrasting narratives in the context of topic involvement and the strengths of temporal trends encoded in the data visualization.

Please find the pre-print here: Link to the pre-print

Replicability Instructions

In order to reproduce the analysis depicted in the paper, please follow the steps below. Here, we provide step-by-step instructions to reproduce one of the figures in the paper (Figure 8.)

1. Install R and R Studio

All analyses were conducted in R version 4.2.3. Download and Install R and R Studio from RStudio Desktop.

2. Load the Project file

In the R studio, go to file -> Open project... -> select and open the file Data and Analysis.Rproj from 1_analysis_and_results/R_and_Py/Data and Analysis.Rproj YOu do not need to run the rest of the scripts. They will be running inside the mentioned scripts.

3. Run the scripts

Scripts are located in 1_analysis_and_results/R_and_Py/analysis/

You can run trendelicitation_study1.R or trendelicitation_study2.R in any order. However, to be able to run trendelicitation_combined.R, you need to run the previous scripts first.

3.1: Install required packages

To install the required packages, first, run packages.R located in the analysis folder. Note: When opening each script, there may be packages missing. In such cases, a notification appears on the top of the screen, prompting to install the missing packages. Simply click on the Install missing packages link, and the program will automatically download the missing packages.

4. Plots

After running each script, the results and plots appear in their respective window in R Studio. YOu can also access the figures here: 1_analysis_and_results\results_figures\. For example, after running the script combined_plots.R you can get the result figure in 1_analysis_and_results\results_figures\combined\

Reproducing Study 1 analysis

To reproduce Study 1 analysis, please visit our Study 1 repository