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

• URL: http://arxiv.org/abs/2405.07828v1
• Date: Mon, 13 May 2024 15:13:23 GMT
• Title: Can LLMs Help Predict Elections? (Counter)Evidence from the World's Largest Democracy
• Authors: Pratik Gujral, Kshitij Awaldhi, Navya Jain, Bhavuk Bhandula, Abhijnan Chakraborty,
• Abstract summary: 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. We introduce a new method: harnessing the capabilities of Large Language Models (LLMs) to examine social media data and forecast election outcomes.
• Score: 3.0915192911449796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Leveraging AI and Sentiment Analysis for Forecasting Election Outcomes in Mauritius [0.0]
  This study explores the use of AI-driven sentiment analysis as a novel tool for forecasting election outcomes, focusing on Mauritius' 2024 elections. We analyze media sentiment toward two main political parties L'Alliance Lepep and L'Alliance Du Changement by classifying news articles from prominent Mauritian media outlets as positive, negative, or neutral. Findings indicate that positive media sentiment strongly correlates with projected electoral gains, underscoring the role of media in shaping public perception.
  arXiv  Detail & Related papers  (2024-10-28T09:21:15Z)
• ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents [70.17229548653852]
  We introduce ElectionSim, an innovative election simulation framework based on large language models. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario.
  arXiv  Detail & Related papers  (2024-10-28T05:25:50Z)
• Large Language Models Reflect the Ideology of their Creators [73.25935570218375]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. We uncover notable diversity in the ideological stance exhibited across different LLMs and languages.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• LLM-POTUS Score: A Framework of Analyzing Presidential Debates with Large Language Models [33.251235538905895]
  This paper introduces a novel approach to evaluating presidential debate performances using large language models. We propose a framework that analyzes candidates' "Policies, Persona, and Perspective" (3P) and how they resonate with the "Interests, Ideologies, and Identity" (3I) of four key audience groups. Our method employs large language models to generate the LLM-POTUS Score, a quantitative measure of debate performance.
  arXiv  Detail & Related papers  (2024-09-12T15:40:45Z)
• United in Diversity? Contextual Biases in LLM-Based Predictions of the 2024 European Parliament Elections [45.84205238554709]
  Large language models (LLMs) are perceived by some as having the potential to revolutionize social science research. In this study, we examine to what extent LLM-based predictions of public opinion exhibit context-dependent biases. We predict voting behavior in the 2024 European Parliament elections using a state-of-the-art LLM.
  arXiv  Detail & Related papers  (2024-08-29T16:01:06Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Prediction of the 2023 Turkish Presidential Election Results Using Social Media Data [0.5156484100374059]
  We aim to predict the vote shares of parties participating in the 2023 elections in Turkey by combining social media data with traditional polling data. Our approach is a volume-based approach that considers the number of social media interactions rather than content.
  arXiv  Detail & Related papers  (2023-05-28T13:17:51Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• Inferring Political Preferences from Twitter [0.0]
  Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate. During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians. In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning.
  arXiv  Detail & Related papers  (2020-07-21T05:20:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.