Uncovering Latent Arguments in Social Media Messaging Using LLMs-in-the-Loop Strategy

Konsep Inti

A framework that leverages the capabilities of Large Language Models (LLMs) to efficiently extract latent arguments from social media messaging.

Abstrak

The article introduces an LLMs-in-the-Loop framework for uncovering latent arguments in social media messaging. The key points are:

The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. However, traditional supervised and unsupervised methods have limitations in capturing the nuanced arguments within the broader themes.

The proposed framework combines Natural Language Processing techniques and the inference capabilities of LLMs to automate the process of discovering latent arguments within predefined themes.

The framework consists of the following steps:

Theme-specific clustering of textual instances
Summarizing the sub-clusters using zero-shot multi-document summarization
Generating and refining arguments by prompting LLMs in a zero-shot manner
Mapping instances to the discovered arguments

The framework is evaluated on two case studies: climate campaigns and COVID-19 vaccine campaigns. The results show that the framework can uncover a comprehensive set of arguments that cover a large portion of the discussion, with accurate mapping of ads to arguments.

The analysis also reveals how messaging is tailored to specific demographics and how the talking points evolve in response to real-world events.

Statistik

"The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion."
"Unsupervised text analysis methods like topic modeling (Latent Dirichlet Allocation (LDA) (Blei et al., 2003), non-negative matrix factorization (NMF) (Lee & Seung, 1999)) can discover topics within data without requiring pre-labeled datasets."
"Recently, large language models (LLMs) have achieved promising progress in learning from prompts via in-context learning (ICL) (Chowdhery et al., 2023; Kojima et al., 2022; Le Scao et al., 2022; Brown et al., 2020)."

Kutipan

"Consequently, a significant portion of research into social media discourse still depends on labor-intensive manual coding techniques and a human-in-the-loop approach, which are both time-consuming and costly."
"To fully comprehend why an opinion is formed, it's crucial to identify the specific arguments within these broader themes."
"Our framework provides a way of clustering talking points motivated by themes."

Wawasan Utama Disaring Dari

Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy

by Tunazzina Is... pada arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10259.pdf

Pertanyaan yang Lebih Dalam

How can the LLMs-in-the-Loop framework be extended to handle real-time social media data and provide timely insights?

The LLMs-in-the-Loop framework can be extended to handle real-time social media data by implementing a continuous monitoring and updating system. This can involve setting up a pipeline that constantly collects new social media data, processes it using the existing framework, and updates the argument extraction model with the latest information. By incorporating real-time data streams, the framework can provide timely insights into evolving discussions and trends on social media platforms. Additionally, leveraging technologies like stream processing and automated data ingestion can help ensure the framework stays up-to-date with the latest information.

What are the potential biases and limitations of using LLMs for argument extraction, and how can they be mitigated?

Using LLMs for argument extraction may introduce biases and limitations due to the inherent biases present in the training data, the model's preconceptions, and the potential for generating misleading or inaccurate arguments. Some common biases include gender bias, racial bias, and cultural bias, which can impact the quality and fairness of the extracted arguments. To mitigate these biases, it is essential to carefully curate and preprocess the training data to reduce bias, employ techniques like debiasing algorithms, and conduct thorough evaluations of the model's performance on diverse datasets.
Additionally, the limitations of LLMs, such as the lack of interpretability and the potential for generating nonsensical or irrelevant arguments, can be addressed by incorporating human oversight and validation in the loop. Human-in-the-loop approaches can help filter out erroneous arguments, provide context-specific insights, and ensure the extracted arguments align with the intended goals of the analysis. Regular model audits, sensitivity analyses, and transparency in the decision-making process can also help mitigate biases and limitations in LLM-based argument extraction.

How can the discovered arguments be further analyzed to understand the underlying drivers of public opinion formation on social media?

To understand the underlying drivers of public opinion formation on social media based on the discovered arguments, a deeper analysis can be conducted using various techniques:

Sentiment Analysis: Analyze the sentiment associated with each argument to gauge public sentiment towards specific topics or themes.

Network Analysis: Explore the connections between different arguments, users, and communities to identify influential nodes and patterns of information flow.

Topic Modeling: Apply topic modeling techniques to categorize arguments into broader themes and identify prevalent topics driving public opinion.

Temporal Analysis: Study how arguments evolve over time to track the dynamics of public opinion and identify key events or trends shaping discussions.

Demographic Analysis: Investigate how different demographic groups engage with and respond to specific arguments to understand the diversity of opinions within the population.

By combining these analytical approaches, researchers can gain a comprehensive understanding of the factors influencing public opinion formation on social media and uncover insights into the drivers behind the emergence and spread of specific viewpoints.