Abstract
This research explores relationships between social network structure (as inferred from Twitter posts) and the occurrence of domestic protests following the 2016 US Presidential Election. A hindcasting method is presented which exploits Random Forest classification models to generate predictions about protest occurrence that are then compared to ground truth data. Results show a relationship between social network structure and the occurrence of protests that is stronger or weaker depending on the time frame of prediction.
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Acknowledgments
This research is sponsored by the Army Research Laboratory, accomplished under Cooperative Agreement Number W911NF-09-2-0053 (the ARL Network Science CTA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
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Renaud, M., Korolov, R., Mendonça, D., Wallace, W. (2019). Social Network Structure as a Predictor of Social Behavior: The Case of Protest in the 2016 US Presidential Election. In: Chertov, O., Mylovanov, T., Kondratenko, Y., Kacprzyk, J., Kreinovich, V., Stefanuk, V. (eds) Recent Developments in Data Science and Intelligent Analysis of Information. ICDSIAI 2018. Advances in Intelligent Systems and Computing, vol 836. Springer, Cham. https://doi.org/10.1007/978-3-319-97885-7_27
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DOI: https://doi.org/10.1007/978-3-319-97885-7_27
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