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Journal of Intelligent Information Systems

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15-08-2018

Predicting future personal life events on twitter via recurrent neural networks

Authors: Maryam Khodabakhsh, Mohsen Kahani, Ebrahim Bagheri

Published in: Journal of Intelligent Information Systems | Issue 1/2020

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Abstract

Social network users publicly share a wide variety of information with their followers and the general public ranging from their opinions, sentiments and personal life activities. There has already been significant advance in analyzing the shared information from both micro (individual user) and macro (community level) perspectives, giving access to actionable insight about user and community behaviors. The identification of personal life events from user’s profiles is a challenging yet important task, which if done appropriately, would facilitate more accurate identification of users’ preferences, interests and attitudes. For instance, a user who has just broken his phone, is likely to be upset and also be looking to purchase a new phone. While there is work that identifies tweets that include mentions of personal life events, our work in this paper goes beyond the state of the art by predicting a future personal life event that a user will be posting about on Twitter solely based on the past tweets. We propose two architectures based on recurrent neural networks, namely the classification and generation architectures, that determine the future personal life event of a user. We evaluate our work based on a gold standard Twitter life event dataset and compare our work with the state of the art baseline technique for life event detection. While presenting performance measures, we also discuss the limitations of our work in this paper.

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Title: Predicting future personal life events on twitter via recurrent neural networks
Authors: Maryam Khodabakhsh
Mohsen Kahani
Ebrahim Bagheri
Publication date: 15-08-2018
Publisher: Springer US
Published in: Journal of Intelligent Information Systems / Issue 1/2020
Print ISSN: 0925-9902
Electronic ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-018-0519-2

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