Categorizing Social Media Screenshots for Identifying Author Misattribution
Abstract: 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.
- American Psychological Association, “Misinformation and disinformation.” https://www.apa.org/topics/journalism-facts/misinformation-disinformation, 2022.
- S. B. Parikh, S. R. Khedia, and P. K. Atrey, “A framework to detect fake tweet images on social media,” in 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 104–110, 2019.
- A. Blok, “Twitter API is going behind the paywall.” https://www.cnet.com/news/social-media/twitter-api-is-going-behind-the-paywall/, 2023.
- J. A. Hodges, M. Chaiet, and P. Gupta, “Forensic analysis of memetic image propagation: introducing the SMOC BRISQUEt method,” Proceedings of the Association for Information Science and Technology, vol. 58, no. 1, pp. 196–205, 2021.
- S. Abdali, R. Gurav, S. Menon, D. Fonseca, N. Entezari, N. Shah, and E. E. Papalexakis, “Identifying misinformation from website screenshots,” in Vol. 15 (2021): Fifteenth International AAAI Conference on Web and Social Media, pp. 2–13, 2021.
- T. Zaki, M. L. Nelson, and M. C. Weigle, “Extracting information from twitter screenshots,” Tech. Rep. arXiv:2306.08236, 2023.
- T. Zaki, “Disinformation spread on social media through screenshot sharing: Dataset description.” https://ws-dl.blogspot.com/2022/12/2022-12-12-disinformation-spread-on.html, 2022.
- C. Bradford and M. L. Nelson, “Did they really tweet that? Querying fact-checking sites and Politwoops to determine tweet misattribution,” Tech. Rep. arXiv:2211.09681, 2022.
- A. C. Nwala, M. C. Weigle, and M. L. Nelson, “Using micro-collections in social media to generate seeds for web archive collections,” in Proceedings of the 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 251–260, 2019.
- J. Kaufman, “google-10000-english.” https://github.com/first20hours/google-10000-english, 2021.
- Bot Sentinel Inc., “Twitter’s response to abuse and bigotry directed at vice president Kamala Harris.” https://botsentinel.com/reports/documents/kamala-harris/report-05-26-2022.pdf, 2022.
- M. Chirico, “Cheltenham’s facebook groups.” https://www.kaggle.com/datasets/mchirico/cheltenham-s-facebook-group/data, 2018.
- A. Matusevski, “Instagram images - 1,211,625 posts.” https://www.kaggle.com/datasets/shmalex/instagram-images, 2022.
- P. Gerard, N. Botzer, and T. Weninger, “Truth social dataset [data set].” https://doi.org/10.5281/zenodo.7531625, 2023.
- E. Amigó, J. Carrillo de Albornoz, I. Chugur, A. Corujo, J. Gonzalo, T. Martín, E. Meij, M. de Rijke, and D. Spina, “Overview of RepLab 2013: Evaluating online reputation monitoring systems,” in Proceedings of the Fourth International Conference of the CLEF initiative, pp. 333–352, 2013.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.