“A picture is worth a thousand words”: Reverse image lookup and digital library assessment

Thompson, Santi; Reilly, Michele

doi:10.1002/asi.23847

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…Many problems in the field of computer and Internet technology have not been fully solved. How to organize, extract, acquire, and intelligently and efficiently utilize all kinds of massive digital information, and how to effectively utilize the advantages of the “Internet” are the first problems to be solved at present [ 10 ]. In response to these problems, scientists put forward the concept of the digital library.…”

Section: Theoretical Knowledge and Methodsmentioning

confidence: 99%

Application of Digital Information Technology in Book Classification and Quick Search in University Libraries

Xiao

2022

Computational Intelligence and Neuroscience

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A digital library is a digital information resource system supported by modern high technology, a next-generation information resource management model on the Internet, and the result of the digitization of library collections, and with the development of society and the accelerated pace of people’s lives, people cannot spend too much time classifying and finding books, so the study of book classification and quick finding in university libraries is very important. This paper mainly researches and analyzes the classification and quick search of books in the university library through the algorithms and methods of digital information technology and finds a better algorithm. This paper mainly conducts experiments on automatic text and support vector machine (one-to-many and global optimization) methods and compares the obtained experimental data, such as classification accuracy, classification time, search time, and other data. The experimental results show that the classification accuracy of these three classification methods is in the range of 86%–94%. However, compared with the two methods of automatic text classification and one-to-many classification, the global optimization classification has the highest accuracy in the sample size of each interval. Among them, the classification time is the lowest for automatic text classification, which is less than 30s, and the one-to-many classification sample takes the most time, and their average fitness is in the range of 24%–27%.

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Section: Theoretical Knowledge and Methodsmentioning

confidence: 99%

Application of Digital Information Technology in Book Classification and Quick Search in University Libraries

Xiao

2022

Computational Intelligence and Neuroscience

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“…Various studies focus on the reuse behavior of specific audience types. Thompson and Reilly (2017) document how general users download digital objects to repurpose as artwork or decoration in physical format and how social media users choose objects in GLAMR digital repositories to share across social media platforms. Johnston and Köntges (2016) and Terras (2015) articulate the need for GLAMR institutions to make bibliographic metadata, described by Terras as "open cultural data," available to researchers to reconfigure and repurpose.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Important themes emerge in the literature dedicated to digital object reuse . Thompson and Reilly (2017) and Matusiak et al (2019) emphasize the need for a digital image to be re-contextualized, including changing the virtual location or intellectual meaning, for reuse to occur. Thompson and Reilly explain recontextualization as “using a digital image in a new setting that is not its original platform, purpose, or context” (2017, p. 2264).…”

Section: Literature Reviewmentioning

confidence: 99%

Toward a definition of digital object reuse

Kenfield

Woolcott

Thompson

et al. 2022

DLP

Self Cite

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Purpose The purpose of this paper is to present conceptual definitions for digital object use and reuse. Typically, assessment of digital repository content struggles to go beyond traditional usage metrics such as clicks, views or downloads. This is problematic for galleries, libraries, archives, museums and repositories (GLAMR) practitioners because use assessment does not tell a nuanced story of how users engage with digital content and objects. Design/methodology/approach This paper reviews prior research and literature aimed at defining use and reuse of digital content in GLAMR contexts and builds off of this group’s previous research to devise a new model for defining use and reuse called the use-reuse matrix. Findings This paper presents the use-reuse matrix, which visually represents eight categories and numerous examples of use and reuse. Additionally, the paper explores the concept of “permeability” and its bearing on the matrix. It concludes with the next steps for future research and application in the development of the Digital Content Reuse Assessment Framework Toolkit (D-CRAFT). Practical implications The authors developed this model and definitions to inform D-CRAFT, an Institute of Museum and Library Services National Leadership Grant project. This toolkit is being developed to help practitioners assess reuse at their own institutions. Originality/value To the best of the authors’ knowledge, this paper is one of the first to propose distinct definitions that describe and differentiate between digital object use and reuse in the context of assessing digital collections and data.

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“…Within information retrieval, the idea of object reverse search is called Content Based Information Retrieval (CBIR), and has primarily been applied to images and other multi-media [32]. Allegedly, the idea of CBIR was born in a 1992 National Science Foundation Workshop [49]. Information professionals often use CBIR for 1) discovering digital content on the web and 2) understanding how images are reused [49].…”

Section: Content Based Information Retrievalmentioning

confidence: 99%

“…Allegedly, the idea of CBIR was born in a 1992 National Science Foundation Workshop [49]. Information professionals often use CBIR for 1) discovering digital content on the web and 2) understanding how images are reused [49]. Few CBIR examples exist outside of multi-media search and analysis.…”

Section: Content Based Information Retrievalmentioning

confidence: 99%

Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search

Beskow¹,

Carley²

2020

Preprint

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Social bots have emerged over the last decade, initially creating a nuisance while more recently used to intimidate journalists, sway electoral events, and aggravate existing social fissures. This social threat has spawned a bot detection algorithms race in which detection algorithms evolve in an attempt to keep up with increasingly sophisticated bot accounts. This cat and mouse cycle has illuminated the limitations of supervised machine learning algorithms, where researchers attempt to use yesterday's data to predict tomorrow's bots. This gap means that researchers, journalists, and analysts daily identify malicious bot accounts that are undetected by state of the art supervised bot detection algorithms. These analysts often desire to find similar bot accounts without labeling/training a new model, where similarity can be defined by content, network position, or both. A similarity based algorithm could complement existing supervised and unsupervised methods and fill this gap. To this end, we present the Bot-Match methodology in which we evaluate social media embeddings that enable a semi-supervised recursive nearest neighbors search to map an emerging social cybersecurity threat given one or more seed accounts.

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“A picture is worth a thousand words”: Reverse image lookup and digital library assessment

Cited by 6 publications

References 6 publications

Application of Digital Information Technology in Book Classification and Quick Search in University Libraries

Application of Digital Information Technology in Book Classification and Quick Search in University Libraries

Toward a definition of digital object reuse

Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search

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