Categorizing Social Media Screenshots for Identifying Author Misattribution
<|reference_start|>Categorizing Social Media Screenshots for Identifying Author Misattribution: Mis/disinformation is a common and dangerous occurrence on social media. Misattribution is a form of mis/disinformation that deals with a false claim of authorship, which means a user is claiming someone said (posted) something they never did. We discuss the difference between misinformation and disinformation and how screenshots are used to spread author misattribution on social media platforms. It is important to be able to find the original post of a screenshot to determine if the screenshot is being correctly attributed. To do this we have built several tools to aid in automating this search process. The first is a Python script that aims to categorize Twitter posts based on their structure, extract the metadata from a screenshot, and use this data to group all the posts within a screenshot together. We tested this process on 75 Twitter posts containing screenshots collected by hand to determine how well the script extracted metadata and grouped the individual posts, F1 = 0.80. The second is a series of scrapers being used to collect a dataset that can train and test a model to differentiate between various social media platforms. We collected 16,620 screenshots have been collected from Facebook, Instagram, Truth Social, and Twitter. Screenshots were taken by the scrapers of the web version and mobile version of each platform in both light and dark mode.<|reference_end|>
@article{farris2024categorizing,
    title={Categorizing Social Media Screenshots for Identifying Author
  Misattribution},
    author={Ashlyn M. Farris, Michael L. Nelson},
    journal={arXiv preprint arXiv:2410.06443},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06443},
    primaryClass={cs.IR}
}