# Filtering Tweets for Social Unrest

## TL;DR

This paper presents a supervised classifier for filtering Arabic tweets relevant to social unrest, optimizing performance and cost through data size and confidence threshold adjustments.

## Contribution

It introduces a high-reliability Arabic tweet classifier for unrest detection and analyzes data size and threshold effects to improve efficiency.

## Key findings

- Classifier achieves high accuracy in identifying unrest-related tweets
- Performance improves with increased training data size
- Optimal confidence thresholds balance precision and recall

## Abstract

Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest prediction, we focus on filtering the vast volume of tweets to identify tweets relevant to unrest, which can be provided to downstream users for further analysis. We train a supervised classifier that is able to label Arabic language tweets as relevant to unrest with high reliability. We examine the relationship between training data size and performance and investigate ways to optimize the model building process while minimizing cost. We also explore how confidence thresholds can be set to achieve desired levels of performance.