  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.
