@inbook{a02417f665c044648a6486fa0ed4f8eb,
title = "Early estimation of protest time spans: A novel approach using topic modeling and decision trees",
abstract = "Protests and agitations have long been used as means for showing dissident toward social, political, and economic issues in civil societies. In recent years, we have witnessed a large number of protests across various geographies. Not to be left behind by similar trends in the rest of the world, South Africa in recent years has witnessed a large number of protests. This paper uses the English text description of the protests to predict their time spans/durations. The descriptions consist of multiple causes of the protests, courses of actions, etc. Next, we use unsupervised (topic modeling) and supervised learning (decision trees) to predict the duration of protests. The results are very promising and close to 90% of accuracy in early prediction of the duration of protests. We expect the work to help public services departments to better plan and manage their resources while handling protests in future.",
keywords = "Decision trees, Duration, Early prediction, Latent Dirichlet allocation, Perplexity, Protests and agitations, South Africa, Topic modeling",
author = "Satyakama Paul and Madhur Hasija and Mangipudi, {Ravi Vishwanath} and Tshilidzi Marwala",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.",
year = "2018",
doi = "10.1007/978-981-13-0514-6_11",
language = "English",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "107--116",
booktitle = "Advances in Intelligent Systems and Computing",
address = "Germany",
}