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 present MRecuri (Music RECommender for filter bUbble diveRsIfication), a music recommendation technique to foster the diversity and novelty of recommendations. A preliminary evaluation over Last.fm listening data showed the potential of MRecuri to increase the diversity and novelty of recommendations compared with state-of-the-art techniques.
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.
The Workshop on Online Misinformation- and Harm-Aware Recommender Systems (OHARS 2021) aimed at fostering research in recommender systems that can circumvent the negative effects of online harms by promoting the recommendation of safe content and users.
The study shows how the choice of the different factors and assessment alternatives affects followee recommendation. It also verifies the existence of certain patterns regarding friends and random users' similarities, which can condition the adequacy of the available similarity metrics.
The Workshop on Online Misinformation- and Harm-Aware Recommender Systems (OHARS 2020) aimed at fostering research in recommender systems that can circumvent the negative effects of online harms by promoting the recommendation of safe content and users.
Misinformation in Social Media & Recommender Systems
Misinformation in Social Media & Recommender Systems
This work presents a semi-automated approach to recommend relevant contents of a given SAD to specific stakeholder profiles.
Online Harm-Aware Recommender System