Abstract

This chapter introduces the concept of Social Stress Indicators (SSIs) as a computational framework for quantifying and interpreting stress-related patterns in digital discourse. In an era marked by algorithmically amplified information flow, emotional contagion, and the viral spread of misinformation, the measurement of online social stress has become a critical component of digital infoveillance. This chapter situates SSIs within the broader goals of Computational Infodemiology, demonstrating their relevance not only for public health and crisis communication, but also for early warning detection systems, reputation management, and risk sensing across societal domains. By analysing user-generated content, search behaviour, topic emergence, and sentiment signals, SSIs provide a dynamic lens for identifying stress-inducing events, ideological tension, or collective cognitive strain within digitally networked environments. The chapter outlines the core methodologies underpinning SSIs, beginning with search volume indices as proxies for information-seeking behaviour, and extending into the use of natural language processing (NLP) techniques such as sentiment analysis and topic modelling. Together, these components support the creation of a multidimensional and responsive indicator capable of tracing the sociopsychological pulse of online communities. By bridging behavioural analytics with computational metrics, this chapter presents SSIs as an essential tool for detecting, analysing, and ultimately mitigating the effects of social stress in digital communication ecosystems.