Using Deductive Reasoning to Identify Unhappy Communities

Abstract

The complex interplay between media and population sentiment is a well-known, yet hard to model, phenomenon. The extrema of individual sentiment (euphoria and depression) are known to be correlated with people’s social interaction, including activity in forums and social-media sites. Yet causality direction of this correlation still requires scrutiny and scientific analysis. The difficulty of such analysis comes, first and foremost, from the complexity of sentiment quantification. It is even more difficult to measure a shared sentiment of a community, due to the echo chamber effect. In addition, media analysis presents its own computational challenges, which were insurmountable until recent times, when we have been presented with unprecedented technical possibilities for quantification of community sentiment and its factors at a level of granularity never seen before. Behavioral psychology suggests that people’s behavior can be used as a measurable proxy for their sentiment. Behaviorist theory has been enhanced with the proliferation of targeted advertising and marketing science in the corporate world, and a number of phenomena connecting people’s sentiment and social behavior have been explained using behavioral psychology. In addition, advances in the theory and practice of data mining and machine learning have enabled non-empirical approaches to quantitative sociological analysis and development of new indicators of communal well-being that rely on the analysis of historical data. These considerations make it possible to investigate the interactions between media sentiment and community. This is the theme of the present submission.