论文标题
CASCADE-LSTM：使用深神经网络预测信息级联
Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks
论文作者
论文摘要
从传播医疗保健信息到模因跟踪，预测动态社会环境中的信息流与当代社会的许多领域有关。尽管预测信息级联的增长在各种社交平台上已成功解决，但预测信息级联的时间和拓扑结构的探索有限。但是，准确地预测有多少用户将传递特定用户的消息，以及在设计实用干预技术的最高时刻。 本文利用长期任期内存（LSTM）神经网络技术来预测信息级联的两个时空特性，即个人级信息传输的大小和速度。我们将这些预测算法与概率生成的级联树相结合到一个生成的测试模型中，该模型能够在两个不同平台Reddit和github中准确生成级联树。我们的方法导致信息发射器的分类精度超过73％，在各种社交平台中的早期发射器中的分类准确性为83％。
Predicting the flow of information in dynamic social environments is relevant to many areas of the contemporary society, from disseminating health care messages to meme tracking. While predicting the growth of information cascades has been successfully addressed in diverse social platforms, predicting the temporal and topological structure of information cascades has seen limited exploration. However, accurately predicting how many users will transmit the message of a particular user and at what time is paramount for designing practical intervention techniques. This paper leverages Long-Short Term Memory (LSTM) neural network techniques to predict two spatio-temporal properties of information cascades, namely the size and speed of individual-level information transmissions. We combine these prediction algorithms with probabilistic generation of cascade trees into a generative test model that is able to accurately generate cascade trees in two different platforms, Reddit and Github. Our approach leads to a classification accuracy of over 73% for information transmitters and 83% for early transmitters in a variety of social platforms.
