TY - GEN
T1 - Understanding information operations using YouTubeTracker
AU - Marcoux, Thomas
AU - Agarwal, Nitin
AU - Adewale, Obadimu
AU - Hussain, Muhammad Nihal
AU - Galeano, Katrin Kania
AU - Al-Khateeb, Samer
N1 - Funding Information:
This research is funded in part by the U.S. National Science Foundation IIS-1636933, ACI-1429160, IIS-1110868), U.S. Office of Naval ResearchN00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1-2605, N00014-17-1-2675, N00014-19-1-2336), U.S. Air Force Research Lab U.S. Army Research OfficeW911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency W31P4Q-17-C-0059), Arkansas Research Alliance and Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The researchers gratefully acknowledge the support.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/10/14
Y1 - 2019/10/14
N2 - YouTube is the second most popular website in the world. Over 300 hours worthof videos are uploaded every minute and 5 billion videos are watched every day - almost one video per person worldwide. Because videos can deliveracomplex messageinaway that captures the audience's attention more effectively than text-based platforms, it has become one of the most relevant platforms in the age of digital mass communication. This makes the analysis of YouTube content and user behavior invaluable not only to information scientists but also communication researchers, journalists, sociologists, and many more. There exists a number of YouTube analysis tools but few of them provide an in-depth qualitative and quantitative insights into user behavior or networks from massive aggregated data. Towards that direction, we introduce YouTubeTracker - a tool designed to gather YouTube data and gain insights on content and users. This tool can help identify leading actors, networks and spheres of influence, emerging popular trends, as well as user opinion. This analysis can also be used to understand user engagement and social networks. This can help reveal suspicious and inorganic behaviors (e.g., trolling, botting) causing algorithmic manipulations. Utility of the YouTubeTracker application is demonstrated via a case study on NATO's 2018 Trident Juncture Exercise.
AB - YouTube is the second most popular website in the world. Over 300 hours worthof videos are uploaded every minute and 5 billion videos are watched every day - almost one video per person worldwide. Because videos can deliveracomplex messageinaway that captures the audience's attention more effectively than text-based platforms, it has become one of the most relevant platforms in the age of digital mass communication. This makes the analysis of YouTube content and user behavior invaluable not only to information scientists but also communication researchers, journalists, sociologists, and many more. There exists a number of YouTube analysis tools but few of them provide an in-depth qualitative and quantitative insights into user behavior or networks from massive aggregated data. Towards that direction, we introduce YouTubeTracker - a tool designed to gather YouTube data and gain insights on content and users. This tool can help identify leading actors, networks and spheres of influence, emerging popular trends, as well as user opinion. This analysis can also be used to understand user engagement and social networks. This can help reveal suspicious and inorganic behaviors (e.g., trolling, botting) causing algorithmic manipulations. Utility of the YouTubeTracker application is demonstrated via a case study on NATO's 2018 Trident Juncture Exercise.
UR - http://www.scopus.com/inward/record.url?scp=85074373926&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074373926&partnerID=8YFLogxK
U2 - 10.1145/3358695.3360917
DO - 10.1145/3358695.3360917
M3 - Conference contribution
AN - SCOPUS:85074373926
T3 - Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WI 2019 Companion
SP - 309
EP - 313
BT - Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WI 2019 Companion
A2 - Barnaghi, Payam
A2 - Gottlob, Georg
A2 - Katsaros, Dimitrios
A2 - Manolopoulos, Yannis
A2 - Pandey, Rahul
A2 - Tzouramanis, Theodoros
A2 - Vakali, Athena
PB - Association for Computing Machinery, Inc
T2 - 19th IEEE/WIC/ACM International Conference on Web Intelligence Workshop, WI 2019
Y2 - 14 October 2019 through 17 October 2019
ER -