DiPD: Disruptive event Prediction Dataset from Twitter

TL;DR

This paper introduces DiPD, a comprehensive Twitter dataset for predicting disruptive events like riots and protests, aiming to enable early detection and mitigation through machine learning models.

Contribution

The creation of a large, labeled Twitter dataset specifically designed for predicting disruptive events, including feature extraction for improved model performance.

Findings

Dataset contains 263,561 records with event and non-event tweets.

Features include user follower count and location, aiding impact analysis.

Potential to improve early warning systems for social disruptions.

Abstract

Riots and protests, if gone out of control, can cause havoc in a country. We have seen examples of this, such as the BLM movement, climate strikes, CAA Movement, and many more, which caused disruption to a large extent. Our motive behind creating this dataset was to use it to develop machine learning systems that can give its users insight into the trending events going on and alert them about the events that could lead to disruption in the nation. If any event starts going out of control, it can be handled and mitigated by monitoring it before the matter escalates. This dataset collects tweets of past or ongoing events known to have caused disruption and labels these tweets as 1. We also collect tweets that are considered non-eventful and label them as 0 so that they can also be used to train a classification system. The dataset contains 94855 records of unique events and 168706 records of unique non-events, thus giving the total dataset 263561 records. We extract multiple features from the tweets, such as the user's follower count and the user's location, to understand the impact and reach of the tweets. This dataset might be useful in various event related machine learning problems such as event classification, event recognition, and so on.