The Complexity of Social Media Response: Statistical Evidence For One-Dimensional Engagement Signal in Twitter

TL;DR

This paper introduces a new one-dimensional engagement metric for Twitter that better predicts content performance and simplifies influence analysis, aiding social media monitoring and influencer identification.

Contribution

It proposes a novel holistic engagement signal that outperforms previous predictors and explains half of the variance using early available features.

Findings

The new engagement metric is more predictable than existing influence predictors.

It explains 50% of the variance in engagement with early features.

The model achieves strong ranking performance for content engagement.

Abstract

Many years after online social networks exceeded our collective attention, social influence is still built on attention capital. Quality is not a prerequisite for viral spreading, yet large diffusion cascades remain the hallmark of a social influencer. Consequently, our exposure to low-quality content and questionable influence is expected to increase. Since the conception of influence maximization frameworks, multiple content performance metrics became available, albeit raising the complexity of influence analysis. In this paper, we examine and consolidate a diverse set of content engagement metrics. The correlations discovered lead us to propose a new, more holistic, one-dimensional engagement signal. We then show it is more predictable than any individual influence predictors previously investigated. Our proposed model achieves strong engagement ranking performance and is the first to explain half of the variance with features available early. We share the detailed numerical workflow to compute the new compound engagement signal. The model is immediately applicable to social media monitoring, influencer identification, campaign engagement forecasting, and curating user feeds.