Algorithmic personalization is difficult to approach because it entails studying many different user experiences, with a lot of variables outside of our control. Two common biases are frequent in experiments: relying on corporate service API and using synthetic profiles with small regards of regional and individualized profiling and personalization. In this work, we present the result of the first crowdsourced data collections of YouTube's recommended videos via YouTube Tracking Exposed (YTTREX). Our tool collects evidence of algorithmic personalization via an HTML parser, anonymizing the users. In our experiment we used a BBC video about COVID-19, taking into account 5 regional BBC channels in 5 different languages and we saved the recommended videos that were shown during each session. Each user watched the first five second of the videos, while the extension captured the recommended videos. We took into account the top-20 recommended videos for each completed session, looking for evidence of algorithmic personalization. Our results showed that the vast majority of videos were recommended only once in our experiment. Moreover, we collected evidence that there is a significant difference between the videos we could retrieve using the official API and what we collected with our extension. These findings show that filter bubbles exist and that they need to be investigated with a crowdsourced approach.

Echo chambers have often been analyzed in social media studies as dysfunctions of communication fostering the polarization of debates and the spreading of conspiracy theories. On the other hand, from a linguistic perspective, very little research has been conducted on these themes. Our work aims to investigate the linguistic dimension of echo chambers, exploring them as ideological structures that are observable when ideological conflict occurs. Using word embedding and corpus-based discourse analysis, we investigate mediatic discourse on COVID-19 in the Coronavirus Corpus and in the Public Coronavirus Twitter Data Set. The analysis focuses on the semantic and pragmatic status of the word hoax, which emerges as a keyword characterizing the Twitter dataset. Our study shows how linguistic markers of ideological conflict can act as markers of position and affective/social identity.

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