Examining Adaptation in Complex Online Social Systems
Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Online social systems, comprised of social media services and platforms including social networking (e.g. Facebook, LinkedIn) and microblogging (e.g. Twitter, Sina Weibo) applications, continue to gain traction among an ever-increasing global user base. The growing reliance upon online social systems to augment an individual’s daily workflow and the resulting interdependence between human and technical systems provide sufficient evidence to classify them as socio-technical systems. These interdependencies are complex in nature and are best defined from a complex adaptive system (CAS) perspective. It is through a CAS lens that this dissertation examines two types of adaptation in online social systems using an array of Computational Social Science (CSS) tools. In the first type of adaptation, human actors are no longer the sole participants in online social systems, since social bots, or automated software mimicking humans, have emerged as potential threats to stifle or amplify certain online conversation narratives. The first part of the dissertation addresses adaptation to these new types of actors by presenting a novel social bot analysis framework designed to determine the pervasiveness and relative importance of social bots within various online conversations. In the second form of adaptation, individual citizens and government entities modify their behaviors in relation to each other through censorship circumvention or detection. This second form of adaptation in the dissertation investigates the rise of digital censorship in online social systems, creating a new agent-based model inspired by the findings from an evaluation of a Turkish digital censorship campaign. The social bot analysis framework results consistently showed that while users identified as social bots only comprised a small portion of total accounts within the overall research corpus, they account for a significantly large portion of prominent centrality rankings across all observed online conversations. Furthermore, bot classification results, when using multiple bot detection platforms, exhibited minimal overlap, thus affirming that different bot detection algorithms focus on the various types of bots that exist. Finally, the results of the Turkish digital censorship campaign showed marginal effectiveness as some Turkish citizens circumvented the censorship policies, thus highlighting an individual decision cycle to risk punishment and engage in online activities. The recognition of this citizen decision cycle served as the basis for the adaptation to digital censorship model, which used empirical evidence to stylize and template a simulation censorship environment. In all, this dissertation presents a unique CSS methodology to observe, measure and simulate social adaptation that exists in complex online social systems.