Tweets can tell-Reference-Cited by-同舟云学术

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Tweets can tell

Published:2019-08-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Author:

Cui Renhao¹,Agrawal Gagan¹,Ramnath Rajiv¹

Affiliation:

1. The Ohio State University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3341161.3342935

Reference13 articles.

1. Long Short-Term Memory

2. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns;Yang D.;IEEE SMC: Systems,2015

3. W. Weerkamp M. De Rijke et al. "Activity prediction: A twitter-based exploration " in SIGIR Workshop on Time-aware Information Access 2012. W. Weerkamp M. De Rijke et al. "Activity prediction: A twitter-based exploration " in SIGIR Workshop on Time-aware Information Access 2012.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Where You Are Is What You Do: On Inferring Offline Activities From Location Data;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04

2. Tweets can tell: activity recognition using hybrid gated recurrent neural networks;Social Network Analysis and Mining;2020-03-02

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