ctavolazzi / ai_fact_checking

We conduct a preregistered experiment to investigate whether fact checks provided by a large language model can serve as an effective misinformation intervention.

Fact-checking information generated by a large language model can decrease news discernment

Paper

For more details, you can find the paper here. It should be cited as:

• Matthew R. DeVerna, Harry Yaojun Yan, Kai-Cheng Yang, Filippo Menczer (2023). Fact-checking information generated by a large language model can decrease news discernment, ArXiv preprint:2308.10800. doi: https://doi.org/10.48550/arXiv.2308.10800

@article{deverna2023artificial,
  title={Fact-checking information generated by a large language model can decrease news discernment},
  author={DeVerna, Matthew R. and Yan, Harry Yaojun and Yang, Kai-Cheng and Menczer, Filippo},
  journal={Preprint arXiv:2308.10800},
  year={2023}
}

Project aim

We conduct a preregistered experiment to investigate whether fact checks provided by a large language model (ChatGPT) can serve as an effective misinformation intervention.

Directories

• code: scripts to generate results, figures, etc.
• data: data used in the project
• environment: python environment files
• figures: all generated figures
• results: output files that include processed data and statistical reports

Replication

We utilize both Python and R coding languages in this project.

Requirements

• Python: see the environment/ directory for virtual environment details
  • Used for most data wrangling/manipulation/basic stats
• R: version 4.3.0 (2023-04-21)
  • Used for supplementary regression analyses

Python analysis and figure generation

To replicate the Python analysis, please set up an environment as described in the environment/ directory. Once that is completed successfully, you should be able to run the below code to conduct all analyses and generate all figures:

cd code # Change directory to `code`
bash run_pipeline.sh # Runs entire pipeline

R analysis and figure generation

The version of R that is utilized in this project is: R version 4.3.0 (2023-04-21). We also utilized RStudio Version 2023.06.0+421 (2023.06.0+421).

If you want to check what version of R you currently have, you can start an R session and run version, giving you something like the below:

> version
               _
platform       aarch64-apple-darwin20
arch           aarch64
os             darwin20
system         aarch64, darwin20
status
major          4
minor          3.0
year           2023
month          04
day            21
svn rev        84292
language       R
version.string R version 4.3.0 (2023-04-21)
nickname       Already Tomorrow

If you want to check what version of RStudio you are running, start up RStudio and (on Macs, atleast) you can click the RStudio > About RStudio dropdown.

All analyses and figures are created with a single script: code/r_code/2023_08_11_Final.Rmd.

After installing the versions of R and RStudio indicated above, you should be able to open the script with RStudio and "Knit" the file, creating the HTML output currently saved in the same location.