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Identifying Toxicity Within YouTube Video Comment

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11549)


Online Social Networks (OSNs), once regarded as safe havens for sharing information and providing mutual support among groups of people, have become breeding grounds for spreading toxic behaviors, political propaganda, and radicalizing content. Toxic individuals often hide under the auspices of anonymity to create fruitless arguments and divert the attention of other users from the core objectives of a community. In this study, we examined five recurring forms of toxicity among the comments posted on pro- and anti-NATO channels on YouTube. We leveraged the YouTube Data API to collect video and comment data from eight channels. We then utilized Google’s Perspective API to assign toxic scores to each comment. Our analysis suggests that, on average, commenters on the anti-NATO channels are more likely to be more toxic than those on the pro-NATO channels. We further discovered that commenters on pro-NATO channels tend to use a mixture of toxic and innocuous comments. We generated word clouds to get an idea of word use frequency, as well as applied the Latent Dirichlet Allocation topic model to classify the comments into their overall topics. The topics extracted from the pro-NATO channels’ comments were primarily positive, such as “Alliance” and “United”; whereas, the topics extracted from anti-NATO channels’ comments were more geared towards geographical locations, such as “Russia”, and negative components such as “Profanity” and “Fake News”. By identifying and examining the toxic behaviors of commenters on YouTube, our analysis lends aid to the pressing need for understanding this toxicity.


  • Social network analysis
  • Topic modeling
  • Toxicity analysis

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This research is funded in part by the U.S. National Science Foundation (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), 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), Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock and the Arkansas Research Alliance. 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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Correspondence to Adewale Obadimu , Esther Mead , Muhammad Nihal Hussain or Nitin Agarwal .

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Obadimu, A., Mead, E., Hussain, M.N., Agarwal, N. (2019). Identifying Toxicity Within YouTube Video Comment. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham.

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