Predicting various types of user attributes in Twitter by using personalized pagerank

Kazuya Uesato, Hiroki Asai, Hayato Yamana

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Predicting various types of user-attributes in social networks has become indispensable for personalizing applications since there are many non-disclosed attributes in social networks. However, extracted attributes in existing works are limited to pre-defined types of attributes, which results in no extraction of unexpected-types of attributes. In this paper, we therefore propose a novel method that extracts various, i.e., unlimited, types of attributes by adopting personalized PageRank to a large social network. The experimental results using over 7.9 million of Japanese Twitter-users show that our proposed method successfully extracts four types of attributes per-user in average with 0.841 of MAP@20.

    Original languageEnglish
    Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2825-2827
    Number of pages3
    ISBN (Print)9781479999255
    DOIs
    Publication statusPublished - 2015 Dec 22
    Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
    Duration: 2015 Oct 292015 Nov 1

    Other

    Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
    CountryUnited States
    CitySanta Clara
    Period15/10/2915/11/1

    Keywords

    • personalized PageRank
    • social networking service
    • Twitter
    • user attribute prediction
    • user profiling

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Science Applications
    • Information Systems
    • Software

    Cite this

    Uesato, K., Asai, H., & Yamana, H. (2015). Predicting various types of user attributes in Twitter by using personalized pagerank. In Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 2825-2827). [7364090] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2015.7364090