A Socially Translucent Knowledge Discovery & Communication Framework and Methodology for Social Media Data
Authors:   , Hemmings-Jarrett Kimberley , Blake M. Brian , Agosto Denise E. 
Published: February 2022

Plato's Allegory of the Cave, describes a metaphorical cave where one group of people are shackled facing a wall, they cannot turn to see what is behind them, only the shadows projected on the wall before them by a second group of people. Group 2 is responsible for puppeteering objects before a flame which casts the shadows on the walls for those shackled to see. Although group 1 only saw shadows, they spoke of them as if they were real. To speak of the real object they would have to turn their heads, which they could not. Today, shadows of real life are cast on television and social media and we sit transfixed before them speaking of these shadows as if they are real. This is not unnatural for humans to communicate and ultimately make decisions based on a finite amount of information, according to Herbert Simon's theory, our rationality is bounded within a limited possibilities. Given the limitations of human decision making, and the growing popularity of analysis of online conversations i.e shadow conversations, analysts of virtual conversations are responsible for both understanding the conversations and effectively transferring that knowledge to others who rely on it to make informed decisions. However, their role in communication models have seldom been researched. The theory of Social Translucence provides a viable mechanism for facilitating the boundaries of human decision-making from virtual shadow conversations. This work is especially important in the information age where online conversations are being commoditized. Analytics has evolved from describing what happened (Gen 1 analytics) to predicting what might happen (Gen 2 analytics), to prescribing what should happen in the future (Gen 3 analytics). Likewise, the role of the analyst has devolved to indirect involvement, in favor of machine driven analysis, where human real-world decisions are reliant on the unbounded rationality of a machine. This work investigates a unifying approach aimed at providing a consistent mechanism for analyzing virtual shadow conversations and providing empirical boundaries for human decision-making regardless of the generational analytics employed. The focus on social media platforms is primarily because they provide virtual public spaces that are more intriguing now with the current global pandemic COVID-19 requiring isolation and social distancing. These platforms provide a space for users to discuss everything from entertainment to politics and more recently are used to mobilize, engage and organize people towards a unified goal using hashtags as a connector i.e. "hashtivism". The current forms of conversational analytics on social media are implicitly inspired by models and theories that don't view social media as a "wall of shadows". In preparation for Gen 3 analytics, using the Design Science Research Method (DSRM) for Information Science, the proposed solution provides a consistent validation protocol useful for all forms of analytics and should support navigating some possible real-world theoretical and societal implications of analyzing and relying on the analysis of virtual communications, like hashtivism.