Immense use of social media platforms results in quicker and wider dissemination of not only legitimate information, but also rumors which may cause mental stress and reduce the productivity of the society. One of the important ways to counteract the rumors in online social networks (OSNs) is to spread counter-rumors. In this work, we propose a Precedence based Competitive Cascade (PCC) model for the propagation of competing rumor and counter-rumor cascades. We have presented a model to compute a belief based precedence value by which a user chooses to believe the rumor or counter-rumor received during information propagation. The influence of the rumor and the counter-rumor cascades in the OSNs is analyzed by considering a neighborhood based propagation approach. Another challenging issue which has been addressed in this work is to select the minimal seed set of users for the initiation of the counter-rumor so as to reduce the message overhead in the application. This has been formulated as a multi-objective optimization problem to select the minimal set of seed users for counter-rumor to minimize the effect of the rumor. We design a Decomposition based Multi-objective Genetic (DMOG) algorithm to solve the problem. Experiments are conducted on real-world data sets to evaluate the efficacy of the proposed PCC model and DMOG algorithm by considering important parameters such as the precedence, budget and time delay.

社交媒体平台的大量使用不仅会导致合法信息更快、更广泛地传播，还会导致可能造成精神压力和降低社会生产力的谣言。消除在线社交网络 (OSN) 中谣言的重要方法之一是传播反谣言。在这项工作中，我们提出了一个基于优先级的竞争级联(PCC) 模型，用于传播竞争性谣言和反谣言级联。我们提出了一个模型来计算基于信念的优先级值，用户通过该优先级值选择相信在信息传播期间收到的谣言或反谣言。通过考虑基于邻域的传播方法来分析 OSN 中谣言和反谣言级联的影响。在这项工作中已经解决的另一个具有挑战性的问题是选择最小的用户种子集来发起反谣言，以减少应用程序中的消息开销。这已被表述为一个多目标优化问题，以选择用于反谣言的最小种子用户集，以最小化谣言的影响。我们设计了一个基于分解的多目标遗传(DMOG) 算法来解决这个问题。通过考虑优先级、预算和时间延迟等重要参数，对真实世界的数据集进行了实验，以评估所提出的 PCC 模型和 DMOG 算法的有效性。