论文标题
在线社交网络中重新访问信息级联
Revisiting Information Cascades in Online Social Networks
论文作者
论文摘要
现在，民间传说要了解在线社交网络（OSN）平台中用户的活动模式，需要查看他的朋友或他所关注的朋友。普遍的看法是，这些朋友对用户产生影响，并影响他的决定是否重新分享内容。依靠这种直觉，开发了各种模型来预测信息在OSN中的传播方式，类似于感染在人群中的传播方式。在本文中，我们重新审视了这个世界观点并得出新的结论。给定一组用户$ v $，我们研究了预测用户$ u \ in v $中的用户是否会在以下时间窗口中所有用户在$ v $的活动中重新分享$ v \ in v $中的某些$ v \ in v $。我们为此任务设计了几种算法，从仅了解$ U $ u $的条件概率分布的简单贪婪算法，忽略了$ V $的其余算法，到卷积神经网络基于网络的算法，该算法获得了$ v $的活动，但并未明确地获得社交链接结构。我们在Twitter收集的四个数据集上测试了我们的算法，每个数据集都围绕着2020年的另一个流行主题。在四个数据集中，最佳性能，平均F1分数为0.86，是通过卷积神经网络实现的。简单的社交链接无知，算法的平均F1得分为0.78。
It's by now folklore that to understand the activity pattern of a user in an online social network (OSN) platform, one needs to look at his friends or the ones he follows. The common perception is that these friends exert influence on the user, effecting his decision whether to re-share content or not. Hinging upon this intuition, a variety of models were developed to predict how information propagates in OSN, similar to the way infection spreads in the population. In this paper, we revisit this world view and arrive at new conclusions. Given a set of users $V$, we study the task of predicting whether a user $u \in V$ will re-share content by some $v \in V$ at the following time window given the activity of all the users in $V$ in the previous time window. We design several algorithms for this task, ranging from a simple greedy algorithm that only learns $u$'s conditional probability distribution, ignoring the rest of $V$, to a convolutional neural network-based algorithm that receives the activity of all of $V$, but does not receive explicitly the social link structure. We tested our algorithms on four datasets that we collected from Twitter, each revolving around a different popular topic in 2020. The best performance, average F1-score of 0.86 over the four datasets, was achieved by the convolutional neural network. The simple, social-link ignorant, algorithm achieved an average F1-score of 0.78.
