Automatic Detection and Categorization of Election-Related Tweets

TL;DR

This paper introduces a deep learning framework for automatically detecting and categorizing election-related tweets, achieving high accuracy in identifying relevant tweets and classifying them into multiple topics for political analysis.

Contribution

The paper presents a novel deep neural network-based system for real-time detection and categorization of election-related tweets, improving accuracy over previous methods.

Findings

Detection F-score of 0.92 for election-related tweets

Categorization into 22 topics with an F-score of 0.90

Effective analysis of political discourse on Twitter

Abstract

With the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary. As a consequence, and aided by the public nature of Twitter, political scientists now potentially have the means to analyse and understand the narratives that organically form, spread and decline among the public in a political campaign. However, the volume and diversity of the conversation on Twitter, combined with its noisy and idiosyncratic nature, make this a hard task. Thus, advanced data mining and language processing techniques are required to process and analyse the data. In this paper, we present and evaluate a technical framework, based on recent advances in deep neural networks, for identifying and analysing election-related conversation on Twitter on a continuous, longitudinal basis. Our models can detect election-related tweets with an F-score of 0.92 and can categorize these tweets into 22 topics with an F-score of 0.90.