Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets...

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Autores principales:	Reishi Amitani, Kazuyuki Matsumoto, Minoru Yoshida, Kenji Kita
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-task learning buzz classification social media trend analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a4e48647ded1406aadcc5f41e1531b2f
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spelling	oai:doaj.org-article:a4e48647ded1406aadcc5f41e1531b2f2021-11-25T16:31:46ZBuzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning10.3390/app1122105672076-3417https://doaj.org/article/a4e48647ded1406aadcc5f41e1531b2f2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10567https://doaj.org/toc/2076-3417This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of “likes (favorites)” and “retweets (RTs)” as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate.Reishi AmitaniKazuyuki MatsumotoMinoru YoshidaKenji KitaMDPI AGarticlemulti-task learningbuzz classificationsocial mediatrend analysisTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10567, p 10567 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-task learning buzz classification social media trend analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	multi-task learning buzz classification social media trend analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Reishi Amitani Kazuyuki Matsumoto Minoru Yoshida Kenji Kita Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
description	This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of “likes (favorites)” and “retweets (RTs)” as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate.
format	article
author	Reishi Amitani Kazuyuki Matsumoto Minoru Yoshida Kenji Kita
author_facet	Reishi Amitani Kazuyuki Matsumoto Minoru Yoshida Kenji Kita
author_sort	Reishi Amitani
title	Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
title_short	Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
title_full	Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
title_fullStr	Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
title_full_unstemmed	Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
title_sort	buzz tweet classification based on text and image features of tweets using multi-task learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a4e48647ded1406aadcc5f41e1531b2f
work_keys_str_mv	AT reishiamitani buzztweetclassificationbasedontextandimagefeaturesoftweetsusingmultitasklearning AT kazuyukimatsumoto buzztweetclassificationbasedontextandimagefeaturesoftweetsusingmultitasklearning AT minoruyoshida buzztweetclassificationbasedontextandimagefeaturesoftweetsusingmultitasklearning AT kenjikita buzztweetclassificationbasedontextandimagefeaturesoftweetsusingmultitasklearning
_version_	1718413156680728576

Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

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