Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions (2408.08379v1)

Abstract: The emergence of synthetic data represents a pivotal shift in modern machine learning, offering a solution to satisfy the need for large volumes of data in domains where real data is scarce, highly private, or difficult to obtain. We investigate the feasibility of creating realistic, large-scale synthetic datasets of user-generated content, noting that such content is increasingly prevalent and a source of frequently sought information. LLMs offer a starting point for generating synthetic social media discussion threads, due to their ability to produce diverse responses that typify online interactions. However, as we demonstrate, straightforward application of LLMs yields limited success in capturing the complex structure of online discussions, and standard prompting mechanisms lack sufficient control. We therefore propose a multi-step generation process, predicated on the idea of creating compact representations of discussion threads, referred to as scaffolds. Our framework is generic yet adaptable to the unique characteristics of specific social media platforms. We demonstrate its feasibility using data from two distinct online discussion platforms. To address the fundamental challenge of ensuring the representativeness and realism of synthetic data, we propose a portfolio of evaluation measures to compare various instantiations of our framework.

• The paper presents a scaffolding method that generates synthetic UGC discussion threads with coherent structure and improved validity.
• It employs a multi-step process including topic extraction, scaffold creation, and detailed content generation to mirror real user interactions.
• Experimental results on Reddit and Wikipedia Talk Pages show that scaffold-based methods outperform baselines in both structural and realism metrics.

Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions

Overview and Motivation

The increasing demand for large volumes of high-quality data in machine learning has spotlighted the generation of synthetic data, particularly in domains where real data is scarce, sensitive, or challenging to collect. User-Generated Content (UGC) platforms, such as social media and forums, are valuable sources of data for modeling human interaction dynamics. However, privacy concerns and data collection difficulties impede research progress in these areas. This paper aims to generate realistic, synthetic datasets of UGC, using a novel approach involving LLMs.

Problem Formulation and Approach

The authors address the challenge of generating realistic synthetic discussion threads that mirror real user interactions on UGC platforms. The primary goal is to create synthetic data that is representative in terms of structure, topic, and content while maintaining user privacy. The inherent complexities of UGC, such as nested replies and varied topics, necessitate a sophisticated approach beyond simple LLM-based generation.

To this end, the paper proposes a multi-step generation process, predicated on the concept of "scaffolds"—compact representations of discussion threads. The framework is designed to be adaptable across different UGC platforms, demonstrated using data from Reddit and Wikipedia Talk Pages.

Data Generation Framework

The proposed framework consists of three main stages:

1. Topic Extraction and Sampling: Extracting the main discussion topics from real threads and modeling their distribution.
2. Thread Scaffold Generation: Creating scaffolds which encode the structure and summarized content of threads.
3. Content Generation: Expanding scaffolds by generating detailed post content based on summaries.

For topic extraction, the model uses few-shot learning to identify relevant topics in real threads. Two topic sampling approaches are explored:

• Independent Sampling: Assumes topic independence.
• Conditional Sampling: Accounts for inter-topic relationships, akin to bigram LLMs.

Evaluation Measures

The evaluation framework assesses three primary aspects of the generated synthetic data:

• Validity: Ensuring that generated threads maintain a coherent structure.
• Structural Measures: Comparing various graph properties of threads such as depth, breadth, and virality indices.
• Content Measures: Using the MAUVE metric to compare the distribution of real and synthetic data embeddings.

Additionally, a novel "realism" measure is introduced to quantify the coherence of interactions within threads.

Experimental Results

Experiments were conducted on Reddit and Wikipedia Talk Pages, chosen for their distinct characteristics in terms of user activity and discussion dynamics. Key findings include:

• Validity: Scaffold-based methods significantly outperformed baseline approaches in generating valid thread structures. Fine-tuning the scaffold model within a platform showed further improvements.
• Structure: Scaffold-based methods closely matched real threads in various structural metrics, indicating their ability to capture the underlying dynamics of UGC.
• Topic and Content: Conditional topic sampling and scaffold-based content generation provided the closest match to real data. The MAUVE scores indicated that the fine-tuned scaffold models produced the most realistic synthetic discussions.
• Realism: The novel LLM-based realism measure confirmed the coherence of generated threads, with scaffold-based methods outperforming the baseline.

Implications and Future Directions

The scaffold-based approach presented in this paper shows promise for generating realistic synthetic UGC, which can be pivotal for research and development in social media analytics, conversational AI, and privacy-preserving data generation. The findings emphasize the importance of intermediate scaffolds to better control the generation process and improve realism.

Future work may explore further fine-tuning approaches, enhancing scaffold representations, and exploring cross-thread user behavior modeling. Additionally, investigating privacy-preserving techniques, establishing robust evaluation metrics, and assessing the utility of synthetic data in real-world applications are critical next steps.

By advancing synthetic data generation methodologies, this research opens avenues for safer, scalable, and more efficient machine learning practices in analyzing complex human interactions on UGC platforms.