The diffusion of fake news has become a crucial problem in recent years. One way to battle it is to propagate the corresponding real news. To achieve this goal, we find a set of individuals who are likely to receive the fake news so that they can test its credibility, and when they propagate the corresponding real news, it is likely to reach a large number of individuals. For this problem, we propose a polynomial time greedy algorithm (AFC) which provides (1-1/e-e)-approximation. We further optimize the runtime of AFC by developing a fast graph-pruning heuristic (RAFC) that performs as well as AFC in checking the spread of fake news. Our experiments on real-world networks demonstrate that our approach outperforms popular methods in social network analysis literature.
[1]
Nam P. Nguyen,et al.
Containment of misinformation spread in online social networks
,
2012,
WebSci '12.
[2]
Xiaokui Xiao,et al.
Influence Maximization in Near-Linear Time: A Martingale Approach
,
2015,
SIGMOD Conference.
[3]
Éva Tardos,et al.
Maximizing the Spread of Influence through a Social Network
,
2015,
Theory Comput..
[4]
Wei Chen,et al.
Efficient influence maximization in social networks
,
2009,
KDD.
[5]
Wei Chen,et al.
Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model
,
2011,
SDM.
[6]
Rajeev Motwani,et al.
The PageRank Citation Ranking : Bringing Order to the Web
,
1999,
WWW 1999.