SAGraph: A Large-scale Text-Rich Social Graph Dataset for Advertising Campaigns
<|reference_start|>SAGraph: A Large-scale Text-Rich Social Graph Dataset for Advertising Campaigns: Influencer selection in marketing involves choosing users with a strong online presence to promote products or services, leveraging their credibility and audience reach. This process is vital for its direct impact on brand visibility, consumer trust, and ultimately, sales conversion. Current research simplifies complex elements like user attitudes, thought processes, and advertising content into numerical values. This kind of approach risks missing the dynamic and contextual nuances crucial for developing effective influencer marketing strategies. To bridge this gap, we introduce a text-rich large Social Advertisement Graph (SAGraph) dataset collected from Weibo, a real-world influencer advertising platform. Our dataset centers around the advertising campaign for 6 products, consisting of 317,287 users, each with their profile information, and interaction data including 891,834 comments and 441,836 reposts. By leveraging this rich interaction and textual content, one can gain deeper insights into consumer behavior, refine influencer selection criteria, and develop more targeted and effective marketing strategies. We evaluated existing influencer selection baselines and the latest LLMs on this dataset, demonstrating the importance of textual content in advertising campaigns, as well as the availability and significant potential of LLMs for enhancing advertising strategies. We hope that this dataset will inspire further research: \url{https://github.com/xiaoqzhwhu/SAGraph/}.<|reference_end|>
@article{zhang2024sagraph:,
    title={SAGraph: A Large-scale Text-Rich Social Graph Dataset for Advertising
  Campaigns},
    author={Xiaoqing Zhang, Xiuying Chen, Yuhan Liu, Jianzhou Wang, Zhenxing Hu
  and Rui Yan},
    journal={arXiv preprint arXiv:2403.15105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15105},
    primaryClass={cs.SI}
}