# On Misinformation Containment in Online Social Networks

arXiv: https://arxiv.org/abs/1809.06486 · HTML: https://mlsanity.com/paper/1809.06486/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Graph Representation Learning for Popularity Prediction Problem: A Survey](https://mlsanity.com/paper/2203.07632/) (2022)
- [Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management](https://mlsanity.com/paper/2111.06440/) (2021)
- [Jointly Complementary&Competitive Influence Maximization with Concurrent Ally-Boosting and Rival-Preventing](https://mlsanity.com/paper/2302.09620/) (2023)

## Abstract (arXiv)

The widespread online misinformation could cause public panic and serious economic damages. The misinformation containment problem aims at limiting the spread of misinformation in online social networks by launching competing campaigns. Motivated by realistic scenarios, we present the first analysis of the misinformation containment problem for the case when an arbitrary number of cascades are allowed. This paper makes four contributions. First, we provide a formal model for multi-cascade diffusion and introduce an important concept called as cascade priority. Second, we show that the misinformation containment problem cannot be approximated within a factor of Omega(2^log1-εn4) in polynomial time unless NP subseteq DTIME(n^polylogn). Third, we introduce several types of cascade priority that are frequently seen in real social networks. Finally, we design novel algorithms for solving the misinformation containment problem. The effectiveness of the proposed algorithm is supported by encouraging experimental results.