Abstract
This chapter provides an overview of the evolving YouTube information environment during the NATO Trident Juncture 2018 exercise. The spread of information is no longer solely driven by information actors publishing content on social media, but also by the audience that interacts with it. Engagement features, such as comments and replies, allow an audience to interact with the publisher and other users. This research focuses on the impact that commenters on YouTube have on boosting influence for channels. YouTube channels are able to interact with their audience in the comment section which can be used and abused to spread messages and disinformation. This study focuses specifically on YouTube comments posted around the 2018 Trident Juncture exercise, the largest NATO exercise in recent decades, and identifies how commenters propel video’s popularity while potentially shaping human behavior through perception. YouTube is the most popular social media site for video sharing. With that, YouTube channels influence human network behavior by shaping perceptions; simultaneously, commenters on these channels boost search engine results which promulgates higher returns on search engines. Presented is an in-depth analysis of comments and commenters on YouTube channels covering Trident Juncture. Comments by individuals drove both popularity and perception. Additionally, commenters helped in amplifying the messages of the channels. This research reveals effective communication strategies that are often overlooked but highly effective to gain tempo and increase legitimacy in the overall information environment.
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References
YouTube. About YouTube – YouTube. https://www.youtube.com/yt/about/
NATO – Exercise Trident Juncture 2018. In: NATO. https://www.nato.int/cps/en/natohq/157833.htm (2018)
Stelzner, M.: YouTube ranking: how to get more views on YouTube (2019). https://www.socialmediaexaminer.com/youtube-ranking-how-to-get-more-views-on-youtube-sean-cannell/
Cheng, X., Dale, C., Liu, J.: Statistics and social network of YouTube videos. In: 2008 16th International Workshop on Quality of Service, pp. 229–238 (2008)
Bodine-Baron, E., Helmus, T., Radin, A., Treyger, E.: Countering Russian social media influence. https://doi.org/10.7249/RR2740 (2018)
Helmus, T.C., Baron, E.B., Radin, A.A., Magnuson, M., Mendelsohn, J.: Russian Social Media Influence: Understanding Russian Propaganda in Eastern Europe. Santa Monica, CA: RAND Corporation. https://doi.org/10.7249/RR2237 (2018)
Tran, K.: Viewers find objectionable content on YouTube kids. https://www.businessinsider.com/viewers-find-objectionable-content-on-youtube-kids-2017-11 (2017)
YouTube. YouTube for press. https://www.youtube.com/yt/about/press/
Weber, P.: Discussions in the comments section: factors influencing participation and interactivity in online newspapers’ reader comments. New Media Soc. 16, 941–957 (2014)
Ernst, J., Schmitt, J.B., Rieger, D., Beier, A.K., Vorderer, P., Bente, G., Roth, H.: Hate beneath the counter speech? A qualitative content analysis of user comments on YouTube related to counter speech videos. J. Deradicalization. 10, 1–49 (2017)
ORA-LITE: Software | CASOS. http://www.casos.cs.cmu.edu/projects/ora/software.php
Everton, S.: Disrupting Dark Networks. Cambridge University Press, New York (2012)
Although the density metric is useful, it has its limitations since this density score is sensitive to the size of the network. Larger networks have lower density scores than smaller networks; this metric should only be used when comparing networks of similar size
Acknowledgment
This research is funded in part by the U.S. National Science Foundation (OIA-1920920, IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, 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 Office (W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059), Arkansas Research Alliance, and the 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.
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Galeano, K., Galeano, R., Agarwal, N. (2020). An Evolving (Dis)Information Environment – How an Engaging Audience Can Spread Narratives and Shape Perception: A Trident Juncture 2018 Case Study. In: Shu, K., Wang, S., Lee, D., Liu, H. (eds) Disinformation, Misinformation, and Fake News in Social Media. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-42699-6_13
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DOI: https://doi.org/10.1007/978-3-030-42699-6_13
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