PoSSUM: A Protocol for Surveying Social-media Users with Multimodal LLMs (2503.05529v1)

Abstract: This paper introduces PoSSUM, an open-source protocol for unobtrusive polling of social-media users via multimodal LLMs. PoSSUM leverages users' real-time posts, images, and other digital traces to create silicon samples that capture information not present in the LLM's training data. To obtain representative estimates, PoSSUM employs Multilevel Regression and Post-Stratification (MrP) with structured priors to counteract the observable selection biases of social-media platforms. The protocol is validated during the 2024 U.S. Presidential Election, for which five PoSSUM polls were conducted and published on GitHub and X. In the final poll, fielded October 17-26 with a synthetic sample of 1,054 X users, PoSSUM accurately predicted the outcomes in 50 of 51 states and assigned the Republican candidate a win probability of 0.65. Notably, it also exhibited lower state-level bias than most established pollsters. These results demonstrate PoSSUM's potential as a fully automated, unobtrusive alternative to traditional survey methods.

An Expert Review of PoSSUM: Polling Social Media Users with Multimodal LLMs

The paper "PoSSUM: A Protocol for Surveying Social-media Users with Multimodal LLMs" introduces an innovative framework aimed at leveraging multimodal LLMs for the purpose of public opinion polling based on social media data. The focus here is on creating automated, unobtrusive, and representative surveys from social media platforms, specifically targeting the dynamics during the 2024 U.S. Presidential Election. The protocol achieves this by melding multimodal input, including textual and visual social media data, with advanced LLM capabilities to infer public sentiment and demographic characteristics.

Methodology Overview

PoSSUM—Protocol for Surveying Social-media Users with Multimodal LLMs—extends traditional polling techniques by moving beyond rudimentary demographic panels to infer political attitudes based on an overview of digital signals, such as posts and images. The paper highlights several architectural components pivotal to this protocol:

1. Data Collection: Leveraging $\mathbb{X}$ App Programming Interface (API) to gather user posts based on specified trends and political discourses.
2. User Filters: Employing a sequence of filters that prune the input set by excluding non-human profiles and ensuring geographic relevance.
3. Quota Sampling: Introducing dynamic stratification frames and quota fulfilling mechanisms to achieve demographic representativeness.
4. Inference with MrP: Implementing a sophisticated Multilevel Regression and Post-Stratification (MrP) model to adjust the silicon samples into population-level estimates.

Key Results and Insights

The protocol was validated during the 2024 U.S. Presidential election period, yielding polling results that closely matched the actual election outcomes. Notably, the PoSSUM polls accurately predicted the results in 50 of 51 states and demonstrated a Republican candidate win probability of 0.65, with a statistical performance competitive with leading traditional pollsters.

Numerical Precision: The PoSSUM methodology was able to capture intricate polling data nuances, offering improvements in root mean square error (RMSE) over various mainstream methodologies. Moreover, the polling results reflected reduced biases, notably surpassing several traditional polls in accurately assessing the state-level Republican-Democrat margin.

Human Alignment and Novelty: A notable portion of the paper is allocated to assessing PoSSUM's capacity for "novel learning" and alignment. The LLMs demonstrated an ability to capture shifts in populace trends beyond the scope of their original training data. This capability underscores PoSSUM's practical potential in dynamically capturing evolving social attitudes.

Time-Sensitivity: The approach also enabled temporal comparisons and captured evolving political opinions—a facet crucial for understanding and forecasting electoral dynamics.

Implications and Future Directions

The combination of LLM capabilities and social media traces to conduct polls introduces an intriguing alternative to conventional methods beset by escalating nonresponse rates and associated biases. The findings foster compelling opportunities for researchers in modeling dynamic opinion shifts and integrating multimedia inputs into polling mechanisms.

However, challenges remain, primarily concerning the representativeness of social media users vis-à-vis the wider electorate and the necessity of refining mechanisms to correct for potential biases introduced by stereotypical inferences.

Conclusion

Overall, the research advances a significant step toward employing AI-enhanced protocol for public sentiment assessment in a non-intrusive manner. Moving forward, the potential integration of other social media platforms, alongside further enhancement in LLM-driven feature extraction accuracy, could refine this framework into a robust tool for real-time public opinion polling. The success of PoSSUM in the electoral context provides a strong testimony of AI's burgeoning role in social science methodologies, heralding a new era of sophisticated, scalable, and flexible natural language processing applications in polling research.