Web Archives for Verifying Attribution in Twitter Screenshots
Document Type
Conference Paper
Publication Date
2025
DOI
10.1145/3720553.3746682
Publication Title
Proceedings of the 36th ACM Conference on Hypertext and Social Media
Pages
91-99
Conference Name
HT '25: Proceedings of the 36th ACM Conference on Hypertext and Social Media
Abstract
Screenshots of social media posts are a common approach for information sharing. Unfortunately, before sharing a screenshot, users rarely verify whether the attribution of the post is fake or real. There are numerous legitimate reasons to share screenshots. However, sharing screenshots of social media posts is also a vector for mis-/disinformation spread on social media. We are exploring methods to verify the attribution of a social media post shown in a screenshot, using resources found on the live web and in web archives. We focus on the use of web archives, since the attribution of non-deleted posts can be relatively easily verified using the live web. We show how information from a Twitter screenshot (Twitter handle, timestamp, and tweet text) can be extracted and used for locating potential archived tweets in the Internet Archive’s Wayback Machine. We evaluate our method on a dataset of 1,571 single tweet screenshots.
Rights
© 2025 The Authors.
Published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Original Publication Citation
Zaki, T., Nelson, M. L., & Weigle, M. C. (2025) Web archives for verifying attribution in twitter screenshots. In Y. Zheng, L. Boratto, C. Hargood, & D. Lee (Eds.) Proceedings of the 36th ACM Conference on Hypertext and Social Media (pp. 91-99). Association for Computing Machinery. https://doi.org/10.1145/3720553.3746682
Repository Citation
Zaki, T., Nelson, M. L., & Weigle, M. C. (2025) Web archives for verifying attribution in twitter screenshots. In Y. Zheng, L. Boratto, C. Hargood, & D. Lee (Eds.) Proceedings of the 36th ACM Conference on Hypertext and Social Media (pp. 91-99). Association for Computing Machinery. https://doi.org/10.1145/3720553.3746682
ORCID
0000-0002-6439-0744 (Zaki), 0000-0003-3749-8116 (Nelson), 0000-0002-2787-7166 (Weigle)