An Online Framework for Civil Unrest Prediction Using Tweet Stream Based on Tweet Weight and Event Diffusion

Abstract

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream in predicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a

Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest

“…weather). Islam et al (2020) use a list of keywords to filter tweets, and then manually label a sample 10,500 tweets from 178 countries from November 26, 2017 to June 25, 2018 to verify their "informative" vs "uninformative" filtration model. De Silva and Riloff (2014) incorporated profile information (i.e.…”

“…The top keywords are in Table 1. The concept of not treating all keywords as equal was also brought up in Islam et al (2020), where they categorized their civil unrest "keyword dictionary" into ranked categories based "negative impact of an unrest event on civil life." The third and final batch was sampled the same way as batch two (using the keyword weights).…”

“…predicting if the label was "yes, a specific event" or "yes, in a non-specific fashion" for question 1 (690 of 4,381 tweets or 16%). The resulting classifier can be used to identify large amounts of data around specific events for further study (Islam et al, 2020;Alsaedi et al, 2017;Edouard, 2018;Korolov et al, 2016;Ranganath et al, 2016). We considered two logistic regression classifiers.…”

“…As an example use case, we create a model that distinguishes between tweets that discuss civil unrest events from those that do not. This filtration model is a popular processing step in civil unrest detection and forecasting pipelines (Islam et al, 2020;Alsaedi et al, 2017;Edouard, 2018;Korolov et al, 2016;Ranganath et al, 2016).…”

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

“…Event prediction has been explored in a variety of applications, including elections [19,20], disease outbreaks [21], stock market movements [22,23], social unrest event prediction [11,13,[24][25][26][27][28][29][30][31], movie earnings [22], crime [32], and failure prediction [33]. Most recent social unrest event prediction techniques can be categorized into three types: planned event forecasting, classification-based prediction, and time series mining.…”