How to Burst the Bubble in Social Networks?

Filter bubble has considered as a serious risk for democracy and freedom of information on the internet and social media. This phenomenon can restrict users\u27 access to information sources outside their comfort zone and increase the risk of polarisation of opinions on different topics. This in-progress paper explains our plan for conducting a prescriptive research aiming at decreasing the chance of filter bubbles formation on social networks. The paper explains a gap in the literature which is a prescriptive work considering both human and technology perspectives. To focus on this research gap, a design perspective has been selected covering two different bodies of theory as kernel theories. The paper explains the relevance of these theories, some of the primarily formed requirements derived from them and the future steps in this research. The explained future steps includes various phases of developing an Information Systems Design Theory and our strategy to evaluate the effectiveness of the developed theory

Burst the Filter Bubble: Towards an Integrated Tool

Formation of filter bubbles is known as a risk for democracy and can bring negative consequences like polarisation of the society, users’ tendency to extremist viewpoints, and the proliferation of fake news. Previous studies, including prescriptive studies, focused on limited aspects of filter bubbles. The current study aims to propose a model for an integrated tool that assists users in avoiding filter bubbles in social networks. To this end, a systematic literature review has been adopted and 571 papers in six top-ranked scientific databases have been identified. After excluding irrelevant studies and an in-depth study of the remaining papers, a classification of research studies is proposed. This classification is then used to propose an overall architecture for an integrated tool that synthesises all previous studies and proposes new features for avoiding filter bubbles. The study explains the components and features of the proposed architecture and describes their focus on content and agents

A Clustering Based Social Matrix Factorization Technique for Personalized Recommender Systems

Recently, a new paradigm of social network based recommendation approach has emerged wherein structural features from social network turned out to be an effective measure to improve the efficacy of the algorithms. However, these approaches assume a user is impacted by all his social connections and completely ignore their preferential similarity, which is crucial for personalized recommendations. Herein, we address this pivotal issue and propose a two-stage clustering based matrix-factorization algorithm, “Cluster REfinement on Preference Embedded MF (CREPE MF)†using a subgraph of social network that integrates the preferential similarity score. Clustering has been applied first on the user followed by the item based \ on ratings. The proposed algorithm has been systematically evaluated with state-of-the-art algorithms in terms of prediction accuracy and runtime complexity using real-world Yelp dataset. Gratifyingly, our approach outperforms the state-of-the-art algorithms with up to 12.97% and 29.60% improvements in RMSE and runtime, respectively