A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks
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DOI：
10.1145/3332168
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发表时间：
2019-08-01
影响因子：
3.6
通讯作者：
Singh, Ambuj K.
中科院分区：
文献类型：
--
作者：
Amelkin, Victor;Bogdanov, Petko;Singh, Ambuj K.
社交网络中的舆论动态分析在当今的生活中发挥着重要作用。为了预测用户的政治偏好，能够分析相互竞争的两极观点的动态尤为重要，例如支持民主党与支持共和党。在观察社交网络中极性观点随时间的演变时，我们能否判断网络何时演变异常？此外，我们能否预测图中用户的意见将如何变化？要回答这些问题，仅仅研究单个用户的行为是不够的，因为意见可以传播到用户的自我网络之外。相反，我们需要同时考虑所有用户的观点动态，并捕捉个体行为与社交网络全局演化模式之间的联系。在这项工作中，我们引入了社交网络距离（SND）——一种距离度量，它量化了在选定的极性观点动态模型下，社交网络的一个快照演化为另一个快照的可能性。 SND 具有丰富的交通问题语义，而且可以在时间上与用户数量成线性关系，因此适用于大规模在线社交网络。在我们对合成数据和 Twitter 数据进行的实验中，我们展示了距离测量在异常事件检测中的实用性。它的真阳性率达到 0.83，是替代品的两倍。在精确召回空间中提出的相同预测表明，SND 保留了高达 0.2 的召回的完美精确度。然后，其精确度会降低，但与替代方案相比仍保持 2 倍以上的改进，召回率高达 0.95。当用于 Twitter 数据中的观点预测时，SND 的准确率为 75.6%，比次优方法高 7.5%。
Analysis of opinion dynamics in social networks plays an important role in today's life. For predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing polar opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network evolved abnormally? Furthermore, can we predict how the opinions of the users will change in the figure?To answer such questions, it is insufficient to study individual user behavior, since opinions can spread beyond users' ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously and capture the connection between the individuals' behavior and the global evolution pattern of the social network.In this work, we introduce the Social Network Distance (SND)-a distance measure that quantifies the like-lihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users and, as such, is applicable to large-scale online social networks. In our experiments with synthetic and Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. The same predictions presented in precision-recall space show that SND retains perfect precision for recall up to 0.2. Its precision then decreases while maintaining more than 2-fold improvement over alternatives for recall up to 0.95. When used for opinion prediction in Twitter data, SND's accuracy is 75.6%, which is 7.5% higher than that of the next best method.