Automatic Detection and Categorization of Election-Related Tweets
This provides political scientists with a tool to analyze public narratives in political campaigns, though it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the challenge of analyzing election-related conversations on Twitter by developing a deep neural network framework that detects such tweets with an F-score of 0.92 and categorizes them into 22 topics with an F-score of 0.90.
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.