Enhancing cultural heritage collections by supporting and analyzing participation in Flickr

Abstract

Cultural heritage institutions can enhance their collections by sharing content through popular web services. Drawing on current analyses from the Flickr Feasibility Study, we report on the pronounced increase in use of the IMLS DCC Flickr Photostream in the past year, trends in how users are engaging with the content, and data provider perspectives on participation in Flickr through the DCC. In addition to users providing comments and tags for images, they are increasingly integrating historical images from libraries and museums into new digital objects and special collections. Intermediary services can fill a key role in lowering the burden for institutions to engage in Web 2.0 initiatives and broadening public access to cultural heritage content. To extend the scope of the current DCC services, we propose a feedback framework for transferring user-generated information to institutional data providers.

Organization of Knowledge and Information in Digital Archives of Language Materials

“…An investigation of user-supplied metadata in the Library of Congress collection in the Flickr photo repository indicated that over 50% of user tags provided valuable additions to subject terms that could be assigned from controlled vocabularies and substantially expanded the discoverability of collections, not only due to increased visibility, but also due to more comprehensive representation in the metadata (Stvilia & Jörgensen, 2010). Library of Congress' successes in broadening access to rich historical collections with the help of partially duplicating them on social media inspired other similar projects --most notably, the social media access to the items from large-scale aggregation of valuable cultural heritage content under the auspices of the United States Institute for Museum and Library Services (e.g., Jett et al, 2010Jett et al, , 2012Jett et al, , 2013. One recent study (Benoit, 2018) even reports an experiment on enriching archival records with social media entries, though Liew (2016) notes that this practice is not yet widespread.…”

“…Language ISO 639-2 32 , ISO 639-3 33 , Glottocode 34 , AustLang 35 Genre OLAC Discourse Type Vocabulary 36 , OLAC Linguistic Data Type Vocabulary 37 Type Internet Media Types 38 , DCMItype 39 Subject Library of Congress Subject Headings (LCSH) 40 Names VIAF 41 , LC Name Authority File 42 , OLAC Role Vocabulary 43 , MARC Relator 44 Date W3CDTF 45 , EDTF 46 Place Names ISO 3166 47 , Thesaurus of Geographic Names 48 Other Traditional Knowledge Labels 49 Beyond language names, Genre and Type are the metadata fields with data values most commonly drawn from controlled vocabularies (see Table 1). The OLAC metadata scheme prescribes the use of the OLAC Linguistic Type controlled vocabulary to represent the genre in an instance of the Type element.…”

Exploring Spatiotemporal Change of City and Village from Remote Sensing Using Multi-Branch Networks

“…Hochman, Nadav and Manovich, Lev et.al [10] based on Instagram algorithms, a spatiotemporal pattern analysis method is designed to vi-sualize the characteristics of image content from 13 different cities around the world, and make corresponding comparisons to further describe people's activities, culture, etc. However, in order to conducive interaction of users and existing images datasets and further Extended scale of these images datasets via social media, J Jett and M Senseney et.al [11] present a feedback framework for transferring user-generated information to institutional data providers, which can improving teh service scope of the datasets center, but the methods mainly using cultural heritage institutions that also can enhance collections by sharing content through popular web services. The above-mentioned methods mainly use some simple visual methods to analyze the images of cultural heritage, residents' living conditions and environment circulating on social media during the disaster, although realize quick and simple statistics to further expand the relevant database, but it is not possible to perceive changes from a deeper level, such as damage to residential areas, cultural buildings and other infrastructure in the disaster.…”

Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques

“…The US Library of Congress uses photo sharing platform Flicker to enable users to interact with old photographs [70]. Other cultural institutions in the US such as The Smithsonian carried out similar initiatives [48,52]. In contrast, Terras investigated the growing trend of the creation of digital images of cultural and heritage materials by amateurs on Flickr [87].…”