Recommendation algorithms are crucial to social media platforms, determining the order of information users see and aiding in discovering news. However, they can inadvertently cause harm through algorithmic privilege, excessive personalization, filter bubbles, and misinformation recommendation and propagation. Spreading misinformation through online social networks like Facebook, X (formerly Twitter), TikTok, and Instagram is commonplace in today’s milieu. Similar such cases were during the COVID-19 pandemic and the US Presidential election where misleading information spread widely. Such issues highlight the need for transparency and oversight in recommendation algorithms design. While some research has been conducted on mitigating misinformation within recommendation algorithms, the focus has primarily been on intervention strategies, which have been observed to cause a backfire effect. Likewise, the current literature only focuses on addressing misinformation at the time of recommendation, particularly without considering dissemination which is very concerning. Our study focuses on strategies to generate recommendations by modeling trustworthy social network and/or user neighbors aggregation while designing user-item latent factors to mitigate misinformation recommendation and dissemination. We evaluated various trustworthy user modeling designs on two Twitter datasets to assess the misinformation in top-K recommendation lists and simulated the propagation of recommended misinformation within the user network to also analyze misinformation spread. The research findings indicate that trustworthy social network and/or user neighbors modeling helps to mitigate misinformation spread at both at time of recommendation and dissemination.
Ahmed et al. (Sat,) studied this question.