通过比较判别性秩检测事件级联中的社会影响
Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers
摘要：
事件级联的全局动态通常由同伴影响的局部动态控制。然而，从观测数据中检测社会影响是具有挑战性的，因为存在同质性等混淆和缺失数据等实际问题。我们提出了一个简单的判别方法来检测观测数据的影响。该方法的核心是训练一个排名算法来预测级联中下一个事件的来源，并将其样本外的准确性与缺乏与社会影响力相对应的特征的竞争基线进行比较。我们分析综合生成的数据表明，这种方法正确地识别存在混淆的影响，是强大的缺失数据和错误的-不像众所周知的替代品。我们将该方法应用于两个真实世界的数据集：（1）在美国众议院共同发起的立法在一个共享的竞选捐助者的社交网络;（2）关于希格斯玻色子发现的谣言在$10^5$ Twitter帐户的追随者网络。我们的模型确定了社会影响力在这些场景中的作用，并利用它对级联的未来轨迹进行更准确的预测。
英文摘要：
The global dynamics of event cascades are often governed by the local dynamics of peer influence. However, detecting social influence from observational data is challenging due to confounds like homophily and practical issues like missing data. We propose a simple discriminative method to detect influence from observational data. The core of the approach is to train a ranking algorithm to predict the source of the next event in a cascade, and compare its out-of-sample accuracy against a competitive baseline which lacks access to features corresponding to social influence. We analyze synthetically generated data to show that this method correctly identifies influence in the presence of confounds, and is robust to both missing data and misspecification --- unlike well-known alternatives. We apply the method to two real-world datasets: (1) the co-sponsorship of legislation in the U.S. House of Representatives on a social network of shared campaign donors; (2) rumors about the Higgs boson discovery on a follower network of $10^5$ Twitter accounts. Our model identifies the role of social influence in these scenarios and uses it to make more accurate predictions about the future trajectory of cascades.
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