Limiting the spread of misinformation in social networks

By Ceren Budak, D. Agrawal, A. El Abbadi. The Web Conference, 2011

895 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/82f20fec9f0a2667f5a9d280dcfa655cc510090e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Yi-Cheng Zhang
2. Neil Shah
3. Jure Leskovec
4. Srijan Kumar
5. Marinka Zitnik

In Statistical and Nonlinear Physics

1. Yi-Cheng Zhang
2. Tanmoy Chakraborty
3. Robert West
4. Chuxu Zhang
5. Chuang Liu
6. Zi-ke Zhang
7. Xin Lu
8. Xiuxiu Zhan

Citations in the corpus

• Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model (2011, background): “Recently, there are a number of studies on competitive influence diffusion [1, 17, 21, 24, 2, 3, 4].”
• Influence Maximization on Social Graphs: A Survey (2018, background): “Budak et al. [4] and He et al. [5] propose greedy solutions for this problem in the IC and LT models respectively.”
• Fighting an Infodemic: COVID-19 Fake News Dataset (2020, background): “In a similar way, several approaches have been developed to identify as-well-as limit the spread of (mis-)information (Acemoglu et al., 2010; Budak et al., 2011; Kwon et al., 2013; Ma et al., 2017; Nguyen et al., 2020; Chandra et al., 2020).”
• False Information on Web and Social Media: A Survey (2018, methodology): “Propagation-modeling algorithms create information spread models for true information and use these models to identify false information [3, 18, 41, 64, 103, 110, 112].”
• A Stylometric Inquiry into Hyperpartisan and Fake News (2017, background): “(2013) and Chen et al. (2015). In this regard, Rubin et al.”
• Dynamics of information diffusion and its applications on complex networks (2016, background): “For example, researchers proposed different models to model the spread of rumors and anti-rumors on the networks [272,264,273], in which the anti-rumors are used to combat rumor spreading.”
• Countering Misinformation and Fake News Through Inoculation and Prebunking (2021, background): “…of the internet, we now know that the propagation of misinformation through online social networks closely resembles the spread of a virus: rapidly transmitting highly infectious information from one host to another but without the need for physical contact (Budak et al., 2011; Kucharski, 2016).”
• Combating Fake News (2019, methodology): “[11] showed that a similar greedy algorithm as discussed in Section 6.”
• Latent Space Model for Multi-Modal Social Data (2015, neutral): “ACM 978-1-4503-4143-1/16/04. http://dx.doi.org/10.1145/2872427.2883031.”
• Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes (2016, background): “Other work has developed approximation algorithms for the problem of limiting the spread of misinformation by selecting a small number of nodes to counteract the effect of misinformation [5, 29].”