Can LLMs Help Predict Elections? (Counter)Evidence from the World's Largest Democracy

TL;DR

This paper explores using Large Language Models to analyze social media data, specifically Twitter in India, for predicting election outcomes, demonstrating their potential to outperform traditional polling methods.

Contribution

It introduces a novel LLM-based approach for election prediction from social media data, emphasizing linguistic understanding and contextual analysis in the Indian political landscape.

Findings

LLMs outperform traditional opinion polls in election prediction accuracy.

Sentiment analysis via LLMs effectively captures public opinion shifts.

Social media data significantly influences electoral outcomes in India.

Abstract

The study of how social media affects the formation of public opinion and its influence on political results has been a popular field of inquiry. However, current approaches frequently offer a limited comprehension of the complex political phenomena, yielding inconsistent outcomes. In this work, we introduce a new method: harnessing the capabilities of Large Language Models (LLMs) to examine social media data and forecast election outcomes. Our research diverges from traditional methodologies in two crucial respects. First, we utilize the sophisticated capabilities of foundational LLMs, which can comprehend the complex linguistic subtleties and contextual details present in social media data. Second, we focus on data from X (Twitter) in India to predict state assembly election outcomes. Our method entails sentiment analysis of election-related tweets through LLMs to forecast the actual election results, and we demonstrate the superiority of our LLM-based method against more traditional exit and opinion polls. Overall, our research offers valuable insights into the unique dynamics of Indian politics and the remarkable impact of social media in molding public attitudes within this context.