论文标题
竞争性影响力的传播和在强大用户偏见的情况下进行的伪造新闻缓解
Competitive Influence Propagation and Fake News Mitigation in the Presence of Strong User Bias
论文作者
论文摘要
由于社交网络在社交媒体中的广泛作用，人们很容易分享新闻，并且它的传播速度比以往任何时候都更快。这些平台也已被利用以共享谣言或虚假信息，这对社会构成威胁。减少虚假信息影响的一种方法是使人们根据硬证明了解正确的信息。在这项工作中，首先，我们提出了一个名为“竞争性独立级联模型”模型的传播模型，并具有用户的偏见（CICMB），该模型考虑了对不同意见，信仰或政党的强烈用户偏见的存在。我们进一步提出了一种称为$ k-truthscore $的方法，以从给定的一组潜在的真理运动者中确定一组最佳的真相运动者，以最大程度地减少谣言宣传者在网络上的影响。我们将$ k-truthscore $与最先进的方法进行了比较，我们将它们的性能视为保存节点的百分比（在没有真相运动的人的情况下会相信假新闻的节点）。我们在一些现实世界网络上介绍了这些结果，结果表明$ k-truthscore $方法优于基线方法。
Due to the extensive role of social networks in social media, it is easy for people to share the news, and it spreads faster than ever before. These platforms also have been exploited to share the rumor or fake information, which is a threat to society. One method to reduce the impact of fake information is making people aware of the correct information based on hard proof. In this work, first, we propose a propagation model called Competitive Independent Cascade Model with users' Bias (CICMB) that considers the presence of strong user bias towards different opinions, believes, or political parties. We further propose a method, called $k-TruthScore$, to identify an optimal set of truth campaigners from a given set of prospective truth campaigners to minimize the influence of rumor spreaders on the network. We compare $k-TruthScore$ with state of the art methods, and we measure their performances as the percentage of the saved nodes (nodes that would have believed in the fake news in the absence of the truth campaigners). We present these results on a few real-world networks, and the results show that $k-TruthScore$ method outperforms baseline methods.
