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Active Opinion Maximization in Social Networks

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Published:19 July 2018Publication History

ABSTRACT

Influence maximization (IM) targets at maximizing the number of users being aware of a product by finding a set of seed users to expose in a social network. Previous IM models mainly focus on optimizing the spread of product consumption, which assumes that all users are potential customers and more exposures lead to better profit. However, in the real-world scenario, some people may not like the product and may express negative opinions after consuming, which damage the product reputation and harm the long-term profit. Only a portion of users in the social network, called the target user, is the potential customer that likes the product and will spread positive opinion. In this paper, we consider a problem called AcTive Opinion Maximization (ATOM), where the goal is to find a set of seed users to maximize the overall opinion spread toward a target product in a multi-round campaign. Different from previous works, we do not assume the user opinion is known before consumption, but should be derived from user preference data. The ATOM problem has essential applications in viral marketing, such as reputation building and precision advertising. Given its significance, ATOM problem is profoundly challenging due to the hardness of estimating user opinion in a multi-round campaign. Moreover, the process of opinion estimation and influence propagation intertwine with each other, which requires the model to consider the two components collectively. We propose an active learning framework called CONE (aCtive OpinioN Estimator) to address above challenges. Experimental results on two real-world datasets demonstrate that CONE improves the total opinion spread in a social network.

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    • Published in

      cover image ACM Other conferences
      KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
      July 2018
      2925 pages
      ISBN:9781450355520
      DOI:10.1145/3219819

      Copyright © 2018 ACM

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      Publication History

      • Published: 19 July 2018

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      KDD '18 Paper Acceptance Rate107of983submissions,11%Overall Acceptance Rate1,133of8,635submissions,13%

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