A Bibliometric Analysis of Communication Research on Artificial Intelligence and Big Data

DOI

10.2991/assehr.k.200428.097How to use a DOI?

Keywords

big data, artificial intelligence, social media, machine learning, privacy, politics

Abstract

The development of artificial intelligence (AI), big data and other communication technologies have multiple implications for the consumption and production of media content. Communication researchers have recently discussed several methodological and thematic challenges, ranging from the ways in which they disrupt the news production and consumption, to the digital transformation of the media and content ecosystems. With the aim to provide a systematic overview of the latest development of communication research in relation to AI and Big Data, this paper has mapped out the main sources, disciplines, and keywords based on a bibliometric analysis of 685 articles collected from Web of Science database. The findings show that the main clusters are “Communication and Sociology”, “Journalism”, and “Information and Telecommunications”, with the cluster of “Communication and Political Science” being in-between them. Also, the visualization of the author keywords indicates the main “Communication and Sociology” cluster includes the main research topics such as surveillance, algorithms, datafication, privacy, ethics, etc., and the “Journalism” cluster includes computational journalism, data journalism, automated journalism. Although future work is needed to provide detailed synthesis, the paper nonetheless provides a succinct summary for communication researchers and professionals to have an overview of the latest discussions.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yujin Zhou
AU  - Han-Teng Liao
PY  - 2020
DA  - 2020/05/01
TI  - A Bibliometric Analysis of Communication Research on Artificial Intelligence and Big Data
BT  - Proceedings of the 6th International Conference on Humanities and Social Science Research (ICHSSR 2020)
PB  - Atlantis Press
SP  - 456
EP  - 459
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200428.097
DO  - 10.2991/assehr.k.200428.097
ID  - Zhou2020
ER  -