Building Cultural Heritage Reference Collections from Social Media through Pooling Strategies: The Case of 2020’s Tensions Over Race and Heritage

Abstract

Social networks constitute a valuable source for documenting heritage constitution processes or obtaining a real-time snapshot of a cultural heritage research topic. Many heritage researchers use social networks as a social thermometer to study these processes, creating, for this purpose, collections that constitute born-digital archives potentially reusable, searchable, and of interest to other researchers or citizens. However, retrieval and archiving techniques used in social networks within heritage studies are still semi-manual, being a time-consuming task and hindering the reproducibility, evaluation, and open-up of the collections created. By combining Information Retrieval strategies with emerging archival techniques, some of these weaknesses can be left behind. Specifically, pooling is a well-known Information Retrieval method to extract a sample of documents from an entire document set (posts in case of social network’s information), obtaining the most complete and unbiased set of relevant documents on a given topic. Using this approach, researchers could create a reference collection while avoiding annotating the entire corpus of documents or posts retrieved. This is especially useful in social media due to the large number of topics treated by the same user or in the same thread or post. We present a platform for applying pooling strategies combined with expert judgment to create cultural heritage reference collections from social networks in a customisable, reproducible, documented, and shareable way. The platform is validated by building a reference collection from a social network about the recent attacks on patrimonial entities motivated by anti-racist protests. This reference collection and the results obtained from its preliminary study are available for use. This real application has allowed us to validate the platform and the pooling strategies for creating reference collections in heritage studies from social networks.

A corpus-based real-time text classification and tagging approach for social data

“…Thus, selecting a hybrid classification approach on social data totally depends upon the dataset and the problem being investigated (Miao et al, 2009). Similarly, research has suggested a few approaches devised around the concept of pooling wherein the selected documents from the given dataset are annotated with relevant concepts by the human experts to create a document collection (pool) of relevant documents for future referencing, reuse, and other information retrieval related tasks (Aslam et al, 2003;Losada et al, 2017;Otero et al, 2021). This implies that once a pool of relevant documents is collected, a machine-learning algorithm can be used to annotate future documents.…”