We study the impact of friend recommender systems on the social influence of misinformation spreaders on Twitter. We applied several user recommenders to a COVID-19 misinformation data collection. Then, we explore what-if scenarios to simulate changes in user misinformation spreading behaviour as an effect of the interactions in the recommended network.
We devise FRediECH (a Friend RecommenDer for breakIng Echo CHambers), an echo chamber-aware friend recommendation approach that learns users and echo chamber representations from the shared content and past users’ and communities’ interactions.
We explore a forward-looking approach that is able to infer groups of likely module dependencies that can anticipate architectural smells in a future system version.
In this work, we investigate whether module dependencies can be predicted (before they actually appear).