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

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ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 164 - 167

https://doi.org/10.1145/3341161.3342935

Published: 15 January 2020 Publication History

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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.

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Najjar AMede K(2023)Where You Are Is What You Do: On Inferring Offline Activities From Location Data2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00113(836-843)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00113
Cui RAgrawal GRamnath R(2020)Tweets can tell: activity recognition using hybrid gated recurrent neural networksSocial Network Analysis and Mining10.1007/s13278-020-0628-010:1Online publication date: 2-Mar-2020
https://doi.org/10.1007/s13278-020-0628-0

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ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 January 2020

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August 27 - 30, 2019

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Najjar AMede K(2023)Where You Are Is What You Do: On Inferring Offline Activities From Location Data2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00113(836-843)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00113
Cui RAgrawal GRamnath R(2020)Tweets can tell: activity recognition using hybrid gated recurrent neural networksSocial Network Analysis and Mining10.1007/s13278-020-0628-010:1Online publication date: 2-Mar-2020
https://doi.org/10.1007/s13278-020-0628-0

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Who Tweets About Politics?: Political Participation of Twitter Users During the 2011Gubernatorial Elections

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