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Peer influence and social contagion are key denominators in the adoption andparticipation of information spreading, such as news propagation, word-of-mouthor viral marketing. In this study, we argue that it is biased to only focus onthe scale and coverage of information spreading, and propose that the level ofinfluence reinforcement, quantified by the re-exposure rate, i.e., the rate ofindividuals who are repeatedly exposed to the same information, should beconsidered together to measure the effectiveness of spreading. We show thatlocal network structural characteristics significantly affects the probabilityof being exposed or re-exposed to the same information. After analyzingtrending news on the super large-scale online network of Sina Weibo (China'sTwitter) with 430 million connected users, we find a class of users withextremely low exposure rate, even they are following tens of thousands ofothers; and the re-exposure rate is substantially higher for news with moretransmission waves and stronger secondary forwarding. While exposure andre-exposure rate typically grow together with the scale of spreading, we findexceptional cases where it is possible to achieve a high exposure rate whilemaintaining low re-exposure rate, or vice versa.

Beyond the Coverage of Information Spreading: Analytical and Empirical Evidence of Re-exposure in Large-scale Online Social Networks