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Web archive collections are created with a particular purpose in mind. A curator selects seeds, or original resources, which are then captured by an archiving system and stored as archived web pages, or mementos. The systems that build web archive collections are often configured to revisit the same original resource multiple times. This is incredibly useful for understanding an unfolding news story or the evolution of an organization. Unfortunately, over time, some of these original resources can go off-topic and no longer suit the purpose for which the collection was originally created. They can go off-topic due to web site redesigns, changes in domain ownership, financial issues, hacking, technical problems, or because their content has moved on from the original topic. Even though they are off-topic, the archiving system will still capture them, thus it becomes imperative to anyone performing research on these collections to identify these off-topic mementos. Hence, we present the Off-Topic Memento Toolkit, which allows users to detect off-topic mementos within web archive collections. The mementos identified by this toolkit can then be separately removed from a collection or merely excluded from downstream analysis. The following similarity measures are available: byte count, word count, cosine similarity, Jaccard distance, Sørensen-Dice distance, Simhash using raw text content, Simhash using term frequency, and Latent Semantic Indexing via the gensim library. We document the implementation of each of these similarity measures. We possess a gold standard dataset generated by manual analysis, which contains both off-topic and on-topic mementos. Using this gold standard dataset, we establish a default threshold corresponding to the best F1 score for each measure. We also provide an overview of potential future directions that the toolkit may take.


© The Authors.

This work is licensed under a Creative Commons Attribution 3.0 United States (CC BY 3.0 US) license.

Original Publication Citation

Jones, S. M., Jayanetti, H. R., Osborne, A., Koerbin, P., Klein, M., Weigle, M. C., & Nelson, M. L. (2022). The DSA toolkit shines light into dark and stormy archives. Code4Lib Journal 53, 1-31, Article 16441.


0000-0003-4748-9176 (Jayanetti), 0000-0002-2787-7166 (Weigle), 0000-0003-3749-8116 (Nelson)