Tweets can tell: Activity recognition using hybrid long short-term memory model

Renhao Cui, Gagan Agrawal, Rajiv Ramnath

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

1 Scopus citations

Abstract

This paper presents techniques to detect offline activities of a person 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 propose a hybrid LSTM model for rich contextual learning, along with studies on the effects of applying and combining multiple LSTM based methods with different contextual features. The hybrid model outperforms a set of baselines as well as state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
EditorsFrancesca Spezzano, Wei Chen, Xiaokui Xiao
PublisherAssociation for Computing Machinery, Inc
Pages164-167
Number of pages4
ISBN (Electronic)9781450368681
DOIs
StatePublished - Aug 27 2019
Externally publishedYes
Event11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada
Duration: Aug 27 2019Aug 30 2019

Publication series

NameProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Conference

Conference11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Country/TerritoryCanada
CityVancouver
Period8/27/198/30/19

ASJC Scopus subject areas

  • Communication
  • Computer Networks and Communications
  • Information Systems and Management
  • Sociology and Political Science

Fingerprint

Dive into the research topics of 'Tweets can tell: Activity recognition using hybrid long short-term memory model'. Together they form a unique fingerprint.

Cite this