Feature Driven and Point Process Approaches for Popularity Prediction (1608.04862v2)

Abstract: Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularity prediction using a collection of cascade history. To our surprise, Hawkes process with a predictive overlay outperform recent feature-driven and generative approaches on existing tweet data [43] and a new public benchmark on news tweets. We also found that a basic set of user features and event time summary statistics performs competitively in both classification and regression tasks, and that adding point process information to the feature set further improves predictions. From these observations, we argue that future work on popularity prediction should compare across feature-driven and generative modeling approaches in both classification and regression tasks.

• The paper introduces a hybrid model that combines feature-driven metrics with a marked Hawkes point process to improve prediction accuracy in social networks.
• It demonstrates superior performance on Twitter datasets by reducing prediction errors and effectively modeling cascade dynamics.
• The open-access dataset and rigorous evaluation highlight the benefits of integrating generative and data-driven approaches for robust social media analytics.

Overview of "Feature Driven and Point Process Approaches for Popularity Prediction"

Introduction

The paper "Feature Driven and Point Process Approaches for Popularity Prediction" addresses the problem of predicting the popularity of information cascades, particularly in social media contexts. This task is vital for understanding collective behaviors in networks, aiding both content consumers in managing information overload and content producers in effectively disseminating information. Two main approaches have emerged in popularity prediction: feature-driven models leveraging extensive data features, and generative models based on self-exciting point processes, each possessing distinct advantages and limitations. This research proposes a hybrid approach that combines the strengths of both methods to enhance prediction accuracy.

Hybrid Model and Generative Approach

The authors introduce a marked Hawkes self-exciting point process to model social cascades, capturing essential dynamics such as content virality, memory decay, and user influence. The model implements a triggering kernel that accounts for the magnitude of influence, memory over time, and inherent content quality. The paper demonstrates that a Hawkes process augmented with a predictive layer can outperform existing feature-driven and generative models on Twitter datasets, including a new benchmark focusing on news tweets. Surprisingly, even basic user features and event time statistics provide competitive results in both classification and regression tasks, and incorporating generative model information further enhances predictions.

Features and Dataset

The feature-driven approach involves non-proprietary, publicly accessible data such as user statistics, temporal features, volume of early cascade activity, and past user success, curated from previous studies. Recognizing the challenges of using specialized data features, the authors construct a large dataset from public Twitter API data, containing 49.7 million tweets linked to top news sites over four months. This endeavor provides an open dataset enabling robust comparative testing of both feature-driven and generative prediction models.

Comparative Analysis and Results

The paper outlines extensive experiments on two datasets, evaluating the proposed hybrid model against state-of-the-art methods such as SEISMIC. On tasks predicting total cascade size, the hybrid model consistently achieves superior performance, displaying lower mean absolute relative errors. Moreover, it demonstrates improved robustness across different cascade popularity scales and dataset contexts, indicating the complementary utility of feature-driven and generative modeling.

Implications and Future Directions

The findings underscore the potential of hybrid approaches in enhancing popularity prediction by integrating data-rich features with generative modeling capabilities. Practically, this holds promise for optimizing content dissemination strategies and better understanding network dynamics. Theoretically, the research calls for future explorations into dynamic user influence distributions, content-type specific behaviors, and cross-cascade interactions—all pivotal for advancing prediction models in complex social networks.

Conclusion

This paper contributes a methodological synthesis of feature-driven and generative models, backed by a comprehensive dataset and rigorous evaluation. The results advocate for a balanced integration of data-driven insights and stochastic modeling, setting a foundation for new benchmarks in information cascade prediction. Moreover, the open-access domain-specific dataset enriches the field by permitting shared progress and reproducibility across research initiatives.

The collaborative adaptability of the proposed hybrid model offers promising avenues for AI development in social media analytics, inviting further inquiry into the nuanced dynamics of information diffusion and influence.