Longitudinal Sentiment Analyses for Radicalization Research: Intertemporal Dynamics on Social Media Platforms and their Implications

TL;DR

This paper explores how longitudinal sentiment analysis of social media data, specifically Tweets around the January 6th Capitol storming, can reveal intertemporal dynamics and improve radicalization research insights.

Contribution

It demonstrates the application of longitudinal sentiment analysis to social media data in radicalization research and discusses its benefits and limitations.

Findings

Longitudinal sentiment analysis can reveal intertemporal dynamics in social media comments.

Tools can simplify qualitative data analysis but face challenges in identifying supporters and hate speech.

Under certain conditions, longitudinal analysis improves evidence-based predictions in radicalization studies.

Abstract

This discussion paper demonstrates how longitudinal sentiment analyses can depict intertemporal dynamics on social media platforms, what challenges are inherent and how further research could benefit from a longitudinal perspective. Furthermore and since tools for sentiment analyses shall simplify and accelerate the analytical process regarding qualitative data at acceptable inter-rater reliability, their applicability in the context of radicalization research will be examined regarding the Tweets collected on January 6th 2021, the day of the storming of the U.S. Capitol in Washington. Therefore, a total of 49,350 Tweets will be analyzed evenly distributed within three different sequences: before, during and after the U.S. Capitol in Washington was stormed. These sequences highlight the intertemporal dynamics within comments on social media platforms as well as the possible benefits of a longitudinal perspective when using conditional means and conditional variances. Limitations regarding the identification of supporters of such events and associated hate speech as well as common application errors will be demonstrated as well. As a result, only under certain conditions a longitudinal sentiment analysis can increase the accuracy of evidence based predictions in the context of radicalization research.