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Computer Science > Computation and Language

arXiv:2408.08379 (cs)
[Submitted on 15 Aug 2024]

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

Authors:Krisztian Balog, John Palowitch, Barbara Ikica, Filip
Radlinski, Hamidreza Alvari, Mehdi Manshadi
View a PDF of the paper titled Towards Realistic Synthetic
User-Generated Content: A Scaffolding Approach to Generating Online
Discussions, by Krisztian Balog and John Palowitch and Barbara Ikica
and Filip Radlinski and Hamidreza Alvari and Mehdi Manshadi
View PDF HTML (experimental)

    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. Large language models (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.

Subjects: Computation and Language (cs.CL); Information Retrieval
          (cs.IR); Machine Learning (cs.LG)
Cite as:  arXiv:2408.08379 [cs.CL]
          (or arXiv:2408.08379v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2408.08379
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Krisztian Balog [view email]
[v1] Thu, 15 Aug 2024 18:43:50 UTC (803 KB)
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    View a PDF of the paper titled Towards Realistic Synthetic
    User-Generated Content: A Scaffolding Approach to Generating
    Online Discussions, by Krisztian Balog and John Palowitch and
    Barbara Ikica and Filip Radlinski and Hamidreza Alvari and Mehdi
    Manshadi
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