SAGraph: A Large-Scale Social Graph Dataset with Comprehensive Context for Influencer Selection in Marketing

Abstract: Influencer marketing campaign success heavily depends on identifying key opinion leaders who can effectively leverage their credibility and reach to promote products or services. The selecting influencers process is vital for boosting brand visibility, fostering consumer trust, and driving sales. While traditional research often simplifies complex factors like user attitudes, interaction frequency, and advertising content, into simple numerical values. However, this reductionist approach fails to capture the dynamic nature of influencer marketing effectiveness. To bridge this gap, we present SAGraph, a novel comprehensive dataset from Weibo that captures multi-dimensional marketing campaign data across six product domains. The dataset encompasses 345,039 user profiles with their complete interaction histories, including 1.3M comments and 554K reposts across 44K posts, providing unprecedented granularity in influencer marketing dynamics. SAGraph uniquely integrates user profiles, content features, and temporal interaction patterns, enabling in-depth analysis of influencer marketing mechanisms. Experimental results using both traditional baselines and state-of-the-art LLMs demonstrate the crucial role of content analysis in predicting advertising effectiveness. Our findings reveal that LLM-based approaches achieve superior performance in understanding and predicting campaign success, opening new avenues for data-driven influencer marketing strategies. We hope that this dataset will inspire further research https://github.com/xiaoqzhwhu/SAGraph/.

• The paper introduces SAGraph, a novel dataset combining extensive textual and network data to refine influencer selection for advertising campaigns.
• It constructs a dense social graph by leveraging seed influencers and iterative user interactions, adhering to the Six Degrees of Separation principle.
• LLMs enhanced with user profiling and chain-of-thought strategies achieved superior precision in influencer prediction, outperforming traditional models.

Insights into SAGraph: A Text-Rich Dataset for Enhancing Advertising Strategies

The paper "SAGraph: A Large-scale Text-Rich Social Graph Dataset for Advertising Campaigns" addresses a critical gap in current influencer marketing research by presenting a novel dataset garnered from Weibo, a major social media platform in China. The primary focus is on improving influencer selection processes by leveraging the textual richness of social media interactions.

Key Contributions and Methodology

The authors introduce SAGraph, a dataset featuring a comprehensive collection of textual and network data across six product campaigns. It encompasses 317,287 users, detailed profiles, and interaction records including 891,834 comments and 441,836 reposts. This dataset stands out as it captures the nuanced text-based interactions often overlooked in numerical analyses prevalent in existing datasets.

Dataset Construction: The dataset's architecture begins with seed influencers from varied domains and iteratively expands using user interaction data. By focusing on users with substantial followings, the authors construct a dense and informatively rich social graph. This iterative method adheres to the Six Degrees of Separation principle, ensuring an expansive yet contextually pertinent data collection.

Evaluation and Findings

The authors evaluated state-of-the-art influencer selection models and several LLMs, including GPT-4, against SAGraph. The study underscores the limitations of traditional models which often fail to capture semantic richness. In contrast, LLMs, bolstered with user profiling and CoT strategy, demonstrate superior precision and recall in influencer prediction tasks. This highlights the potential of LLMs in parsing and understanding complex social media interactions for advertising effectiveness.

Numerical Results and Implications

Impressively, LLMs enhanced with profile information and chain-of-thought methodologies notably outperformed traditional baselines. For instance, utilizing user profiles and CoT strategies led to improved rankings, with enhanced LLMs achieving top precision scores across varied domains. This emphasizes the significance of text in comprehending consumer behavior and intention.

Practical and Theoretical Implications

Practically, SAGraph offers advertisers deeper insights into the mechanics of influence within digital platforms, allowing for more refined marketing strategies. Theoretically, it sets the stage for advanced algorithmic developments in opinion dynamics and consumer influence modeling. This dataset invites researchers to explore the intersections between network theory and semantic analysis, ultimately driving precision in digital advertising.

Future Directions

SAGraph's introduction paves the way for future explorations into influencer marketing optimization. Researchers might further investigate the longitudinal impact of text-based interactions or develop more sophisticated models that assimilate contextual semantics with network topologies. Additionally, adapting the dataset's methodologies to other platforms could offer broader insights into digital consumer behavior across different cultural contexts.

In summary, SAGraph represents a significant advancement for research in influencer marketing, providing a robust framework to analyze and harness the nuanced semantic interactions within social networks. This work not only enriches the field of advertising and AI but also catalyzes future innovations in leveraging text-rich data for strategic marketing decisions.