Leveraging Archival Theory to Develop A Taxonomy of Online Disinformation

TitleLeveraging Archival Theory to Develop A Taxonomy of Online Disinformation
Publication TypeConference Paper
Year of Publication2018
AuthorsLemieux V, Smith T
Conference Name2018 IEEE International Conference on Big Data (Big Data)
Date PublishedDec
Keywordsarchival problems, archival science, archival theory, Big Data, categorization, classification, contemporary disinformation, Context, disinformation, disinformation classification, document handling, documented information, information creation, information dissemination, information retrieval, Internet, Interviews, machine learning, online disinformation, pattern classification, Receivers, Recruitment, Reliability, Taxonomy
Abstract

One of the principal difficulties in classifying and interpreting online disinformation is that the data arrive rapidly and evolve dramatically. The core challenges of classification and interpretation are not unique to contemporary disinformation; the problem of classifying documented information as authentic or inauthentic has been the focus of archivists for centuries. However, the rate of information creation and dissemination enabled by the Internet requires a new approach to categorization and archival examination of disinformation of documented information. This paper provides a survey of the archival problems facing disinformation researchers and proposes a taxonomy of disinformation that will aid future discussion and classification of disinformation.

DOI10.1109/BigData.2018.8622391