Analyzing Political Text at Scale with Online Tensor LDA

TL;DR

This paper introduces Tensor LDA, a scalable, efficient topic modeling method capable of analyzing billions of documents, enabling large-scale social science research on social media conversations.

Contribution

We develop Tensor LDA with theoretical guarantees, demonstrate its computational efficiency, and provide an open-source GPU implementation for large-scale text analysis.

Findings

Analyzed the evolution of the #MeToo movement on Twitter over two years.

Studied social media discussions on election fraud in the 2020 US presidential election.

Achieved 3-4x faster speeds than prior LDA methods on large datasets.

Abstract

This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Dirichlet Allocation (TLDA), that has identifiable and recoverable parameter guarantees and sample complexity guarantees for large data; ii) we show that this method is computationally and memory efficient (achieving speeds over 3-4x those of prior parallelized Latent Dirichlet Allocation (LDA) methods), and that it scales linearly to text datasets with over a billion documents; iii) we provide an open-source, GPU-based implementation, of this method. This scaling enables previously prohibitive analyses, and we perform two real-world, large-scale new studies of interest to political scientists: we provide the first thorough analysis of the evolution of the #MeToo movement through the lens of over two years of Twitter conversation and a detailed study of social media conversations about election fraud in the 2020 presidential election. Thus this method provides social scientists with the ability to study very large corpora at scale and to answer important theoretically-relevant questions about salient issues in near real-time.