Enhance Rumor Controlling Algorithms Based on Boosting and Blocking Users in Social Networks

It is undeniable that rumors abound on online social networks and rumors can cause many disastrous consequences. Effective controlling of rumors is of great significance in social networks. However, the existing research only selects boosting users who are more likely to adopt the truth or select blocking users to terminate the spread of rumors. The former tends to correct the rumor after the spread is over but with high controlling cost, while the latter blocks the rumor without considering the truth transmission. In this article, we focus on how to select boosting–blocking users to control rumors when the rumor and truth are spreading together. We propose a boosting-truth blocking-rumor cascade (BTBRC) model. Under this model, given the rumor seed set and truth seed set, the boosting rumor controlling (BRC) problem aims to find a boosting–blocking seed set with $k$ users such that the number of users influenced by the truth can be maximized. In order to solve it, we design a multihop neighbor boosting (MHNB) algorithm, which can get effective results with a data-parameter-dependent approximation ratio. Based on the above model, we also propose a positive boosting-truth blocking-rumor cascade (PBTBRC) model and design a connected multihop neighbor boosting (CMHNB) algorithm to solve the connected positive boosting rumor controlling (CPBRC) problem that requires a seed set to be connected under this model. Finally, extensive theoretical analysis and experimental results show the superiority of our algorithms over other comparison methods

Algorithm · Computer network · Gradient boosting · Machine learning · Random forest · Rumor · Complex Network Analysis Techniques · Computer Science · Law · Peer-to-Peer Network Technologies · Spam and Phishing Detection · Artificial Intelligence · Theoretical Computer Science

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