Name
Abstract | ||
|---|---|---|
Social networks have many advantages and they are very popular. The number of people having at least one account on a certain social network has grown considerably. Social networks allow people to connect and interact more easily with one another, leading to a much easier way to obtain information. However one major disadvantage of social networks is that some information may be untrue. In this paper we propose a protocol in which the network becomes more immune to the diffusion of false information. Our approach is based on evidence theory with Dempster-Shafer and Yager's rule which plays an important role in an individual's decision whether to send further the received information or not. We also took into consideration the confidence degree of the neighbours regarding the information which is spread by a specific source node. Furthermore, we propose a simulation algorithm that allows us to observe the diffusion of two contradictory information spread by two different source nodes. The experimental results show that the true information spreads more easily if the ground truth is sometimes revealed, even rarely. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01424-7_80 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III |
Keywords | Field | DocType |
Information credibility, Information diffusion, Social networks, Confidence degree | Social network,Credibility,Computer science,Ground truth,Artificial intelligence,Simulation algorithm,Machine learning,Disadvantage | Conference |
Volume | ISSN | Citations |
11141 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sabina-Adriana Floria | 1 | 1 | 1.04 |
| Florin Leon | 2 | 71 | 15.03 |
| Doina Logofatu | 3 | 17 | 16.74 |