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Social Network Analysis and Mining

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01.12.2020 | Original Paper

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

verfasst von: Renhao Cui, Gagan Agrawal, Rajiv Ramnath

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

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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.

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Source code is available at https://goo.gl/o9dsBh.

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Titel: Tweets can tell: activity recognition using hybrid gated recurrent neural networks
verfasst von: Renhao Cui
Gagan Agrawal
Rajiv Ramnath
Publikationsdatum: 01.12.2020
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-020-0628-0

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