Combat Information Overload Problem in Social Networks With Intelligent Information-Sharing and Response Mechanisms

Abstract

Increasingly popular social networks have become fast-growing platforms for information sharing, job searching, and product marketing. Information propagates rapidly in social network and may reach a very large population within a very short period of time. An excessive amount of information shared in social networks not only costs computational and communicational resources but also causes the information overload problem, which results in the delay and difficulty of making decisions and may lead to physical and psychological strain. We used computer technologies to attack this information overload problem. First, we developed automatic decision-making mechanisms to help each individual effectively share information. Second, we built a simulation test bed and proposed an evaluation matrix and then conducted an experimental evaluation of six different information-sharing strategies in terms of interest degrees, reachability, appreciation degrees, and communication cost. We also implemented two intelligent response mechanisms. The first one allows users to order information pieces according to the learned ratings of the information sources. The second mechanism dynamically adjusts the network structure based on machine-learning results. The simulation results show that such mechanisms would be very useful to motivate social-network users to adopt more selective information-sharing strategies.