In the virtual world, an echo chamber is formed when people are exposed only to online information and opinions that align with their existing beliefs and values. Otherwise stated, the propensity for information confirming previous opinions and the interactions with other users showing similar positions result in the formation of "a bounded, enclosed media space that has the potential to both magnify the messages delivered within it and insulate them from rebuttal" (Jamieson, Cappella, 2008). So far, an accepted and general method for the detection of echo chambers is not present in the literature. In the present seminar, I will show how entropy-based null-models can provide an unbias framework for the detection of echo chambers on Twitter. In the case study of Twitter Italian debate about COVID-19 vaccination, we found that the number of users in echo chambers is extremely limited (less than 2%), but nevertheless, their flux of tweets is quite relevant.
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