AiGen-FoodReview: A Multimodal Dataset of Machine-Generated Restaurant
  Reviews and Images on Social Media
Alessandro Gambetti Qiwei Han
Online reviews in the form of user-generated content (UGC) significantly
impact consumer decision-making. However, the pervasive issue of not only human
fake content but also machine-generated content challenges UGC's reliability.
Recent advances in Large Language Models (LLMs) may pave the way to fabricate
indistinguishable fake generated content at a much lower cost. Leveraging
OpenAI's GPT-4-Turbo and DALL-E-2 models, we craft AiGen-FoodReview, a
multi-modal dataset of 20,144 restaurant review-image pairs divided into
authentic and machine-generated. We explore unimodal and multimodal detection
models, achieving 99.80% multimodal accuracy with FLAVA. We use attributes from
readability and photographic theories to score reviews and images,
respectively, demonstrating their utility as hand-crafted features in scalable
and interpretable detection models, with comparable performance. The paper
contributes by open-sourcing the dataset and releasing fake review detectors,
recommending its use in unimodal and multimodal fake review detection tasks,
and evaluating linguistic and visual features in synthetic versus authentic
data.