Time-aware topic identification in social media with pre-trained language models: A case study of electric vehicles

TL;DR

This paper introduces a novel time-aware topic identification method using pre-trained language models to analyze evolving social media discussions, demonstrated through a case study on electric vehicles on Reddit.

Contribution

It presents a two-stage approach combining dynamic tracking and emergence scoring with pre-trained models for time-sensitive social media analysis.

Findings

Feasibility of capturing emerging topics in social media.

Effective tracking of time-varying customer interests.

Application to electric vehicle discussions on Reddit.

Abstract

Recent extensively competitive business environment makes companies to keep their eyes on social media, as there is a growing recognition over customer languages (e.g., needs, interests, and complaints) as source of future opportunities. This research avenue analysing social media data has received much attention in academia, but their utilities are limited as most of methods provide retrospective results. Moreover, the increasing number of customer-generated contents and rapidly varying topics have made the necessity of time-aware topic evolution analyses. Recently, several researchers have showed the applicability of pre-trained semantic language models to social media as an input feature, but leaving limitations in understanding evolving topics. In this study, we propose a time-aware topic identification approach with pre-trained language models. The proposed approach consists of two stages: the dynamics-focused function for tracking time-varying topics with language models and the emergence-scoring function to examine future promising topics. Here we apply the proposed approach to reddit data on electric vehicles, and our findings highlight the feasibility of capturing emerging customer topics from voluminous social media in a time-aware manner.