Consumers increasingly take online word-of-mouth as an important reference. Negative word-of-mouth spread rapidly on social media. How to effectively deal with the spreading of negative word-of-mouth on the platform is important to the enterprise. In order to predict the spread of negative word-of-mouth on the platform more accurately, we quantify user influence from four dimensions of user activity, user behavior, user authenticity, and user infection ability, and test the non-collinearity of these four dimensions to ensure the comprehensive and non-redundant evaluation. Then, combined with the Hidden Markov Model logic framework, an algorithm using user influence to predict the heat of negative word-of-mouth spreading was proposed. Meanwhile, we integrate both the static and dynamic information of microblog contents, directly quantify the popularity of negative word-of-mouth spreading on the platform as a benchmark, and select ten data sets from negative word-of-mouth transmission events to test the performance of the proposed algorithms.


Paper Number 1764; Track Platforms; Short Paper


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