论文标题
确定多个阶段假新闻运动的成本效益销售者
Identifying Cost-effective Debunkers for Multi-stage Fake News Mitigation Campaigns
论文作者
论文摘要
在线社交网络已成为传播虚假新闻的肥沃基础。已经提出了自动减轻假新闻传播的方法。一些研究着重于选择社交网络上的顶级有影响力的用户作为Debunkers，但是揭穿者的社会影响可能不会像预期的那样转化为广泛的缓解信息传播。其他研究假设一组给定的摘要者，并专注于优化驾驶员发布真实新闻的强度，但是由于驾驶员是固定的，即使具有很高的社会影响力和/或高强度以发布真实新闻，真实新闻也可能不会吸引暴露于假新闻的用户，因此缓解效果可能受到限制。在本文中，我们提出了多个阶段的假新闻缓解运动，在每个阶段，在预算内动态选择了揭穿者。我们将其作为加强学习问题提出，并通过预测未来状态提出了一种优化的贪婪算法，以便可以以最大化整体缓解效果的方式选择启动器。我们对合成和现实世界的社交网络进行了广泛的实验，并表明我们的解决方案在缓解效果方面优于最先进的基线。
Online social networks have become a fertile ground for spreading fake news. Methods to automatically mitigate fake news propagation have been proposed. Some studies focus on selecting top k influential users on social networks as debunkers, but the social influence of debunkers may not translate to wide mitigation information propagation as expected. Other studies assume a given set of debunkers and focus on optimizing intensity for debunkers to publish true news, but as debunkers are fixed, even if with high social influence and/or high intensity to post true news, the true news may not reach users exposed to fake news and therefore mitigation effect may be limited. In this paper, we propose the multi-stage fake news mitigation campaign where debunkers are dynamically selected within budget at each stage. We formulate it as a reinforcement learning problem and propose a greedy algorithm optimized by predicting future states so that the debunkers can be selected in a way that maximizes the overall mitigation effect. We conducted extensive experiments on synthetic and real-world social networks and show that our solution outperforms state-of-the-art baselines in terms of mitigation effect.
