An Integrated Model for User Attribute Discovery: A Case Study on Political Affiliation Identification

• Main Authors: Gottipati, Swapna, Qiu, Minghui, Yang, Liu, ZHU, Feida, JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Unsupervised Integrated Model; Social/feedback networks; Probabilistic Matrix Factorization; Collaborative filtering; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2648; https://ink.library.smu.edu.sg/context/sis_research/article/3648/viewcontent/C103___An_Integrated_Model_For_User_Attribute_Discovery_A_Case_Study_on_Political_Affiliation_Identification__PAKDD2014_.pdf
• Institution: Singapore Management University

Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be generalized to include three components — users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an integrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1% for user party detection task.