@inproceedings{2b8ff05d0c4646cab03c08b0dfd052cc,
title = "Tweets can tell: Activity recognition using hybrid long short-term memory model",
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.",
author = "Renhao Cui and Gagan Agrawal and Rajiv Ramnath",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 ; Conference date: 27-08-2019 Through 30-08-2019",
year = "2019",
month = aug,
day = "27",
doi = "10.1145/3341161.3342935",
language = "English (US)",
series = "Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019",
publisher = "Association for Computing Machinery, Inc",
pages = "164--167",
editor = "Francesca Spezzano and Wei Chen and Xiaokui Xiao",
booktitle = "Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019",
}