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Inferring user profile using microblog content and friendship network

  • Zhishan Zhao
  • , Jiachen Du
  • , Qinghong Gao
  • , Lin Gui
  • , Ruifeng Xu*
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen
  • Fuzhou University
  • Guangdong Provincial Engineering Technology Research Center for Data Science

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the rapid development of microblogs in recent years, accurate prediction of microblog user profiles is valuable for marketing, personalized recommendation, and legal investigation. Microblog users post rich contents everyday and build a complex friendship network with “following” behaviors. Both of user-generated content and friendship network are crucial for user profiling. In this work, we propose a neural-network based model for user profiling. It takes advantages of both user-generated content and friendship network with attentional multi-scale convolutional neural networks and graph embeddings. We evaluate our model on SMP CUP 2016 dataset whose task is to infer age, gender and region of microblog users. The experiment results show that utilizing information from user generated content and friend network, our method obtains the state-of-the-art performance on all of three sub-tasks.

Original languageEnglish
Title of host publicationSocial Media Processing - 6th National Conference, SMP 2017, Proceedings
EditorsHuan Liu, Xing Xie, Xueqi Cheng, Huawei Shen, Weiying Ma, Shizheng Feng
PublisherSpringer Verlag
Pages29-39
Number of pages11
ISBN (Print)9789811068041
DOIs
StatePublished - 2017
Externally publishedYes
Event6th National Conference on Social Media Processing, SMP 2017 - Beijing, China
Duration: 14 Sep 201717 Sep 2017

Publication series

NameCommunications in Computer and Information Science
Volume774
ISSN (Print)1865-0929

Conference

Conference6th National Conference on Social Media Processing, SMP 2017
Country/TerritoryChina
CityBeijing
Period14/09/1717/09/17

Keywords

  • Neural networks
  • Social network analysis
  • User profiling

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