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

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.

Archives and AI: An Overview of Current Debates and Future Perspectives

The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural—yet critically aware—integration of artificial intelligence into archival systems and practice.

Unlocking digital archives: cross-disciplinary perspectives on AI and born-digital data

Co-authored by a Computer Scientist and a Digital Humanist, this article examines the challenges faced by cultural heritage institutions in the digital age, which have led to the closure of the vast majority of born-digital archival collections. It focuses particularly on cultural organizations such as libraries, museums and archives, used by historians, literary scholars and other Humanities scholars. Most born-digital records held by cultural organizations are inaccessible due to privacy, copyright, commercial and technical issues. Even when born-digital data are publicly available (as in the case of web archives), users often need to physically travel to repositories such as the British Library or the Bibliothèque Nationale de France to consult web pages. Provided with enough sample data from which to learn and train their models, AI, and more specifically machine learning algorithms, offer the opportunity to improve and ease the access to digital archives by learning to perform complex human tasks. These vary from providing intelligent support for searching the archives to automate tedious and time-consuming tasks. In this article, we focus on sensitivity review as a practical solution to unlock digital archives that would allow archival institutions to make non-sensitive information available. This promise to make archives more accessible does not come free of warnings for potential pitfalls and risks: inherent errors, "black box" approaches that make the algorithm inscrutable, and risks related to bias, fake, or partial information. Our central argument is that AI can deliver its promise to make digital archival collections more accessible, but it also creates new challenges - particularly in terms of ethics. In the conclusion, we insist on the importance of fairness, accountability and transparency in the process of making digital archives more accessible.

Archives, linked data and the digital humanities: increasing access to digitised and born-digital archives via the semantic web

Mass digitisation and the exponential growth of born-digital archives over the past two decades have resulted in an enormous volume of archives and archival data being available digitally. This has produced a valuable but under-utilised source of large-scale digital data ripe for interrogation by scholars and practitioners in the Digital Humanities. However, current digitisation approaches fall short of the requirements of digital humanists for structured, integrated, interoperable, and interrogable data. Linked Data provides a viable means of producing such data, creating machine-readable archival data suited to analysis using digital humanities research methods. While a growing body of archival scholarship and praxis has explored Linked Data, its potential to open up digitised and born-digital archives to the Digital Humanities is under-examined. This article approaches Archival Linked Data from the perspective of the Digital Humanities, extrapolating from both archival and digital humanities Linked Data scholarship to identify the benefits to digital humanists of the production and provision of access to Archival Linked Data. It will consider some of the current barriers preventing digital humanists from being able to experience the benefits of Archival Linked Data evidenced, and to fully utilise archives which have been made available digitally. The article argues for increased collaboration between the two disciplines, challenges individuals and institutions to engage with Linked Data, and suggests the incorporation of AI and low-barrier tools such as Wikidata into the Linked Data production workflow in order to scale up the production of Archival Linked Data as a means of increasing access to and utilisation of digitised and born-digital archives.

Openness and privacy in born-digital archives: reflecting the role of AI development

Galleries, libraries, archives and museums (GLAMs) are striving to retain audience attention to issues related to cultural heritage, by implementing various novel opportunities for audience engagement through technological means online. Although born-digital assets for cultural heritage may have inundated the Internet in some areas, most of the time they are stored in “digital warehouses,” and the questions of the digital ecosystem’s sustainability, meaningful public participation and creative reuse of data still remain. Emerging technologies, such as artificial intelligence (AI), are used to bring born-digital archives to light, aiming to enhance the public’s engagement and participation. At the core of this debate lies both the openness of data and issues of privacy. How open to the public should born-digital archives be? Should everything be open and available online, and what does it take to achieve balance between openness and privacy, especially through AI initiatives? The study is qualitative and builds on the rationale of grounded theory. The role of AI development is critically investigated in relation to opening up born-digital archives online, by considering privacy and ethics issues. Grounded in the context of the author’s PhD research, the paper proposes a human-centred approach to AI development for democratising its development towards fairness and social inclusion, contrary to the stereotypical cliché of blackboxing, allowing space for the plurality of born-digital archives to flourish.

Towards a Noncompliant Pedagogy of the Image: Reading Negentropic Bifurcatory Potentials in Video Images

The authors explore the noncompliant pedagogy of the image based on their video Autopoietic Veering: Schizo Socius of Tokyo and Vancouver (2021). It is not the kind of trendy modelized video abstract or kinetic presentation eagerly promoted by international publishers; it is a cross-cultural collaborative work intended to generate affirmative temporal ruptures of entropic habitual modes of seeing, memorizing, and thinking of human and nonhuman life in the cities of Tokyo (Japan) and Vancouver (Canada). The authors elucidate Stiegler’s (2015b) concept of a “global mnemotechnical system” that stores and produces human memories in vast digital archives and databases (tertiary retentions) through “mnemonic control” (Parisi & Goodman, 2011). The authors repurpose video images to interrupt and recontrol human perception and memories as “living engines” (Lazzarato, 2006). They foreground the philosophical work of Deleuze, Heidegger, and Virilio to rethink and revive the creative act of “critique” (Foucault, 1997) through “metamodelization” (Guattari, 1995; Manning, 2020); therefore, they plug these apparently incommensurable modes of thinking into their readings of the video’s images. They read the images as “time-images” and focus on their five dimensions that possibly activate “spiritual automation” (Deleuze, 1989), which they assess as “negentropic bifurcatory” potentials (Bradley & Kennedy, 2019).

Analysis of User Personalized Retrieval of Multimedia Digital Archives Dependent on BP Neural Network Algorithm

This paper briefly introduces the characteristics of content-based multimedia retrieval under the information background, analyzes the implementation process of these technologies in the multimedia archives retrieval system including video and image information of digital archives, and points out that the content-based multimedia retrieval technology is bound to be organically combined with the traditional text retrieval methods. The information retrieval technologies in the past can only comply with the specific requirements of customers. Due to their characteristics of universality, they can hardly meet the demands of different environments, various purposes, and different times at the same time yet. Researchers have put forward personalized retrieval of multimedia files based on the BP neural network computing. In this way, the interest model of customers can be analyzed based on the characteristics of the different classification areas of users. Subsequently, the corresponding calculations are carried out, and the model is updated accordingly. Through the experiments, it is verified that the probability model put forward in this paper is the optimal solution to express the interest of customers and its changes.

Integrated High-Definition Visualization of Digital Archives for Borobudur Temple

The preservation and analysis of tangible cultural heritage sites have attracted enormous interest worldwide. Recently, establishing three-dimensional (3D) digital archives has emerged as a critical strategy for the permanent preservation and digital analysis of cultural sites. For extant parts of cultural sites, 3D scanning is widely used for efficient and accurate digitization. However, in many historical sites, many parts that have been damaged or lost by natural or artificial disasters are unavailable for 3D scanning. The remaining available data sources for these destroyed parts are photos, computer-aided design (CAD) drawings, written descriptions, etc. In this paper, we achieve an integrated digital archive of a UNESCO World Heritage site, namely, the Borobudur temple, in which buried reliefs and internal foundations are not available for 3D scanning. We introduce a digitizing framework to integrate three different kinds of data sources and to create a unified point-cloud-type digital archive. This point-based integration enables us to digitally record the entire 3D structure of the target cultural heritage site. Then, the whole site is visualized by stochastic point-based rendering (SPBR) precisely and comprehensibly. The proposed framework is widely applicable to other large-scale cultural sites.