In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users’ ideology on the controversy; (iii) the construction of users’ debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump’s presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time.
Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate “what if” epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.
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