Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2014

Abstract

Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to identify users who are important in the religious user community. We further define features using representative users. We show that SVM classifiers using our proposed features can accurately assign Christian and Muslim labels to a set of Twitter users with known religion labels.

Keywords

Religion prediction, Social networks, User profiling

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

SIGIR '14: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval: July 6-11, 2014, Gold Coast

First Page

1211

Last Page

1214

Identifier

10.1145/2600428.2609547

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors/LARC

Additional URL

https://doi.org/10.1145/2600428.2609547

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