The Complexity of Social Media Response: Statistical Evidence For One-Dimensional Engagement Signal in Twitter (1910.02807v3)

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.