A Semi-automatic Method for Efficient Detection of Stories on Social Media

TL;DR

This paper introduces a semi-automatic tool designed to help users efficiently identify and track stories about real-world events on Twitter, improving speed and accuracy compared to traditional methods.

Contribution

The paper presents a novel semi-automatic method and tool for tracking Twitter stories during real-world events, validated through a user study.

Findings

Increased speed in story tracking

Improved accuracy over conventional methods

Effective in real-world event scenarios

Abstract

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.