Document Type

Conference Paper

Publication Date

2024

DOI

10.1145/3627673.3679988

Publication Title

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

Pages

3762-3766

Conference Name

33rd ACM International Conference on Information and Knowledge Management, 21-25 October 2024, Boise, ID

Abstract

Changes made to webpages can affect their retrievability. Often this is done with the intention of increasing the page's search engine ranking to improve overall access to information on the page. The Environmental Data and Governance Initiative (EDGI) created a dataset that describes changes on US federal environmental webpages between 2016 and 2020. EDGI noted that many environmental terms were deleted from the pages, but without user data, claims that page retrievability and public information access were lowered are only anecdotal. The Open Resource for Click Analysis in Search (ORCAS) dataset was created during the same time frame, from 2017 to 2020, and enables high quality user intent analysis without compromising on user privacy protection. We present an analysis of the intersection of the EDGI dataset and the ORCAS dataset, matching changes on federal environmental webpages with their associated queries. We use web archives and a change-text indexing system to link changes in term frequency on the pages with the queries. We find that the pages contain fewer query terms in 2020 than in 2016, lowering the pages' retrievability. The analysis provides substantive support of EDGI's claim that federal environmental pages were made less accessible between 2016 and 2020.

Rights

© 2024 the Owner/Authors.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 (CC BY-NC-SA 4.0) License.

Original Publication Citation

Frew, L., Nelson, M. L., & Weigle, M. C. (2024) . Retrogressive document manipulation of US federal environmental websites. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 3762-3766). Association for Computing Machinery. https://doi.org/10.1145/3627673.3679988

ORCID

0000-0003-0929-049X (Frew), 0000-0003-3749-8116 (Nelson), 0000-0002-2787-7166 (Weigle)

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