This paper explores the accuracy, reliability, and bias of the Large Language Model (LLM) ChatGPT-4 in classifying the political affiliation of a Twitter poster based on tweet content. The study compares ChatGPT-4 to manual annotation by both expert classifiers and crowd workers, which are considered the gold standard for such tasks. The research uses Twitter messages from United States politicians during the 2020 election as a ground truth to measure accuracy. The findings reveal that ChatGPT-4 achieves higher accuracy, higher reliability, and equal or lower bias than human classifiers. The LLM is capable of correctly annotating messages that require reasoning based on contextual knowledge and inferences around author intentions - traditionally seen as uniquely human abilities. These findings suggest that LLMs will have a substantial impact on using textual data in social sciences by enabling interpretive research at scale. The study also highlights how texts are culturally and socially situated within ideas, values, and beliefs about the world within which humans operate. Their interpretation requires deep contextual knowledge and the ability to "put ourselves in the shoes of others" - seen as distinctly human resources and capacities. While interpretation lies at the heart of social sciences, it has been viewed as almost intrinsically qualitative with an interpretive or hermeneutic element. Researchers seeking to use textual data have had to pursue one of two strategies: expert coders or crowd-workers on online labor platforms like Amazon's MTurk. However, these strategies have limitations such as being slow, costly, biased, having limited attention-spans hence restricting interpretive research to relatively small-N studies leading to complaints of lacking rigor and replicability while limiting potential in Big Data research. Conventional forms of interpretive textual research tend to miss patterns of language use that are not directly observable because they are realized across thousands or millions of words of running text and not categorical but probabilistic. Computational methods for analyzing textual data such as Natural Language Processing and Machine Learning have evolved quickly in recent years.
- The paper explores the accuracy, reliability, and bias of the Large Language Model (LLM) ChatGPT-4 in classifying political affiliation on Twitter.
- The study compares ChatGPT-4 to manual annotation by expert classifiers and crowd workers.
- ChatGPT-4 achieves higher accuracy, higher reliability, and equal or lower bias than human classifiers.
- LLMs will have a substantial impact on using textual data in social sciences by enabling interpretive research at scale.
- Traditional forms of interpretive textual research tend to miss patterns of language use that are not directly observable because they are realized across thousands or millions of words of running text and not categorical but probabilistic.
The paper talks about a computer program called ChatGPT-4 that can tell if someone on Twitter is more likely to be a Democrat or Republican. The researchers compared the program's results to those of people who are really good at this kind of thing and found that the computer did just as well or even better! This is important because it means we can use computers to study what people say online in a big way. Before, it was too hard for people to read everything and figure out what it all meant. But now, computers can help us do this!
Accuracy: how correct something is Reliability: how consistent something is Bias: when someone has an unfair preference for one thing over another
The Impact of Large Language Models on Political Affiliation Classification
In recent years, the use of computational methods for analyzing textual data such as Natural Language Processing and Machine Learning has evolved quickly. This paper explores the accuracy, reliability, and bias of one such method - the Large Language Model (LLM) ChatGPT-4 - in classifying the political affiliation of a Twitter poster based on tweet content. The study compares ChatGPT-4 to manual annotation by both expert classifiers and crowd workers, which are considered the gold standard for such tasks.
Background
Researchers seeking to use textual data have had to pursue one of two strategies: expert coders or crowd-workers on online labor platforms like Amazon's MTurk. However, these strategies have limitations such as being slow, costly, biased, having limited attention-spans hence restricting interpretive research to relatively small-N studies leading to complaints of lacking rigor and replicability while limiting potential in Big Data research. Conventional forms of interpretive textual research tend to miss patterns of language use that are not directly observable because they are realized across thousands or millions of words of running text and not categorical but probabilistic.
Research Methodology
The research uses Twitter messages from United States politicians during the 2020 election as a ground truth to measure accuracy. The findings reveal that ChatGPT-4 achieves higher accuracy, higher reliability, and equal or lower bias than human classifiers. The LLM is capable of correctly annotating messages that require reasoning based on contextual knowledge and inferences around author intentions - traditionally seen as uniquely human abilities.
Findings & Implications
These findings suggest that LLMs will have a substantial impact on using textual data in social sciences by enabling interpretive research at scale. The study also highlights how texts are culturally and socially situated within ideas, values, and beliefs about the world within which humans operate. Their interpretation requires deep contextual knowledge and the ability to "put ourselves in the shoes
