General: Deep Learning Applications

• {Ma16} Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, Meeyoung Cha. Detecting Rumors from Microblogs with Recurrent Neural Networks. Proc. of IJCAI, 2016.
• {Suhara17} Yoshihiko Suhara, Yinzhan Xu, Alex 'Sandy' Pentland. DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks. Proc. of WWW, 2017. forecasting severely depressed moods based on self-reported histories
• {Feng18} Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. Proc. of WWW, 2018. design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility; propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way
• {Gong18} Qingyuan Gong, Yang Chen, Xinlei He, Zhou Zhuang, Tianyi Wang, Hong Huang, Xin Wang, Xiaoming Fu. DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks. IEEE Communications Magazine, 2018, 56(11):21-27.
• {Hu18} Wenjian Hu, Krishna Kumar Singh, Fanyi Xiao, Jinyoung Han, Chen-Nee Chuah, and Yong Jae Lee. Who Will Share My Image?: Predicting the Content Diffusion Path in Online Social Networks. Proc. of ACM WSDM, 2018. Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network