Tweets can tell: activity recognition using hybrid gated recurrent neural networks

Renhao Cui, Gagan Agrawal, Rajiv Ramnath

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper presents techniques to detect the “offline” activity (such as dining, shopping, or entertainment) a person is engaged in when she is tweeting , in order to create a dynamic profile of the user, for uses such as better targeting of advertisements. To this end, we present a hybrid gated recurrent neural network (GRNN)-based model for rich contextual learning. Specifically, the study and construction of the hybrid model are applied to two types of GRNNs, namely LSTM and GRU networks. In the process, we study the effects of applying and combining multiple contextual modeling methods with different contextual features. Our hybrid model outperforms a set of baselines and state-of-the-art methods. Finally, this paper presents an orthogonal validation using a real-world application. Our model generates offline activity analysis for the followers of several well-known accounts, and the result is quite representative of the expected characteristics of these accounts.

Original languageEnglish (US)
Article number16
JournalSocial Network Analysis and Mining
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

Keywords

  • Activity recognition
  • Classification
  • Neural network
  • Social network
  • User profiling

ASJC Scopus subject areas

  • Information Systems
  • Communication
  • Media Technology
  • Human-Computer Interaction
  • Computer Science Applications

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