Rumor detection on Twitter with tree-structured recursive neural networks

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed th...

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Main Authors: MA, Jing, GAO, Wei, WONG, Kam-Fai
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4561
https://ink.library.smu.edu.sg/context/sis_research/article/5564/viewcontent/P18_1184.pdf
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spelling sg-smu-ink.sis_research-55642019-12-26T08:27:04Z Rumor detection on Twitter with tree-structured recursive neural networks MA, Jing GAO, Wei WONG, Kam-Fai Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins. 2018-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4561 https://ink.library.smu.edu.sg/context/sis_research/article/5564/viewcontent/P18_1184.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
MA, Jing
GAO, Wei
WONG, Kam-Fai
Rumor detection on Twitter with tree-structured recursive neural networks
description Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.
format text
author MA, Jing
GAO, Wei
WONG, Kam-Fai
author_facet MA, Jing
GAO, Wei
WONG, Kam-Fai
author_sort MA, Jing
title Rumor detection on Twitter with tree-structured recursive neural networks
title_short Rumor detection on Twitter with tree-structured recursive neural networks
title_full Rumor detection on Twitter with tree-structured recursive neural networks
title_fullStr Rumor detection on Twitter with tree-structured recursive neural networks
title_full_unstemmed Rumor detection on Twitter with tree-structured recursive neural networks
title_sort rumor detection on twitter with tree-structured recursive neural networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4561
https://ink.library.smu.edu.sg/context/sis_research/article/5564/viewcontent/P18_1184.pdf
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