Talk Laks V.S. Lakshmanan

Abstract

While social networks greatly facilitate information dissemination, they are well known to have contributed to the phenomena of filter bubbles and echo chambers. This in turn can lead to societal polarization and erosion of trust in public institutions. Mitigating filter bubbles is an urgent open problem. 

Recently, approaches based on the influence maximization paradigm have been explored for mitigating filter bubbles by balancing exposure to opposing viewpoints. However, existing works ignore the inherent competition between the adoption of opposing viewpoints by users.

In this talk, I will describe a realistic model for the filter bubble problem, which unlike previous work, captures the  competition between opposing opinions propagating in a network as well as the complementary nature of the reward for exposing users to both those opinions. We formulate an optimization problem for mitigating filter bubbles under this model. We establish several evidences of the intrinsic difficulty in developing constant approximation to the problem and develop a heuristic and two instance-dependent approximation algorithms. Our experiments over 4 real datasets show that our heuristic far outperforms two state-of-the-art baselines as well as other algorithms in both efficiency and mitigating filter bubbles. We also empirically demonstrate that our best heuristic performs close to the optimal objective, which is verified by utilizing the theoretical bounds of our approximation algorithms. 

Biography

Laks V.S. Lakshmanan is a professor of Computer science at UBC, Vancouver, Canada. His research interests span a wide spectrum of topics in data management, integration, cleaning, and warehousing; data mining; semi-structured and unstructured data; big graphs, social networks and social media; ML, NLP; and efficient deep learning. He is an ACM Distinguished Scientist and has won several awards including best paper awards and distinguished reviewer awards. He has served on most top conferences and journals in his areas of research, on program committees, as senior PC member, meta-reviewer, general chair, and as associate editor.