Abstract
Amidst the growth of harmful content on social media platforms, encompassing abusive language, disrespect, and hate speech, efforts to tackle this issue persist. However, effectively preventing the impact of such content on individuals and communities remains a challenging endeavor. In this paper, we present a study using Reddit data, where we employ a tree structure to visually and comprehensively examine the impact of toxic content on communities. By applying various machine learning algorithms, we classify the toxicity of each leaf node based on its parent and grandparent nodes, as well as the overall tree’s average toxicity. Our methodology can help policymakers detect early warning signs of toxicity and redirect potentially harmful comments to less toxic directions. Our research provides a comprehensive analysis of toxicity on social media platforms, allowing for a better understanding of differences and similarities across platforms, and a deeper exploration of the impact of toxic content on individual communities. Our findings provide valuable perspectives on the prevalence and consequences of toxic content on social media platforms, and our approach can be used in future studies to provide a more nuanced understanding of this complex issue.
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Acknowledgement
This research is funded in part by the U.S. National Science Foundation (OIA-1946391, OIA-1920920, IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of the Under Secretary of Defense for Research and Engineering (FA9550-22-1-0332), 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, N00014-17-1-2675, N00014-17-1-2605, N68335-19-C-0359, N00014-19-1-2336, N68335-20-C-0540, N00014-21-1-2121, N00014-21-1-2765, N00014-22-1-2318), U.S. Air Force Research Laboratory, U.S. Army Research Office (W911NF-20-1-0262, W911NF-16-1-0189, W911NF-23-1-0011), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059), Arkansas Research Alliance, the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock, and the Australian Department of Defense Strategic Policy Grants Program (SPGP) (award number: 2020-106-094). 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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Yousefi, N., Noor, N.B., Spann, B., Agarwal, N. (2024). Examining Toxicity’s Impact on Reddit Conversations. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_33
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