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Deep Learning for Information Triage on Twitter
https://kitami-it.repo.nii.ac.jp/records/2000449
https://kitami-it.repo.nii.ac.jp/records/20004493e9adac4-7dd9-439b-8b54-6610c8699d69
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
applsci-11-06340-v2.pdf (4.5 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||||||||||||
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| 公開日 | 2023-07-25 | |||||||||||||||||||||
| タイトル | ||||||||||||||||||||||
| タイトル | Deep Learning for Information Triage on Twitter | |||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||
| 資源 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||
| タイプ | journal article | |||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||||||||
| 著者 |
Ptaszynski, Michal
× Ptaszynski, Michal
× Fumito, Masui
× Yuuto, Fukushima
× Yuuto, Oikawa
× Hiroshi, Hayakawa
× Yasunori, Miyamori
× Kiyoshi, Takahashi
× Shunzo, Kawajiri
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| 抄録 | ||||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||||
| 内容記述 | In this paper, we present a Deep Learning-based system for the support of information triaging on Twitter during emergency situations, such as disasters, or other influential events, such as political elections. The system is based on the assumption that a different type of information is required right after the event and some time after the event occurs. In a preliminary study, we analyze the language behavior of Twitter users during two kinds of influential events, namely, natural disasters and political elections. In the study, we analyze the credibility of information included by users in tweets in the above-mentioned situations, by classifying the information into two kinds: Primary Information (first-hand reports) and Secondary Information (second-hand reports, retweets, etc.). We also perform sentiment analysis of the data to check user attitudes toward the occurring events. Next, we present the structure of the system and compare a number of classifiers, including the proposed one based on Convolutional Neural Networks. Finally, we validate the system by performing an in-depth analysis of information obtained after a number of additional events, including an eruption of a Japanese volcano Ontake on 27 September 2014, as well as heavy rains and typhoons that occurred in 2020. We confirm that the methods works sufficiently well even when trained on data from nearly 10 years ago, which strongly suggests that the model is well-generalized and sufficiently grasps important aspects of each type of classified information. |
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| 言語 | en | |||||||||||||||||||||
| 書誌情報 |
en : Applied Sciences 巻 11, 号 14, p. 6340-6340 |
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| DOI | ||||||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||||
| 関連識別子 | https://doi.org/10.3390/app11146340 | |||||||||||||||||||||
| 権利 | ||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
| 権利情報 | c2021 by the authors. Licensee MDPI | |||||||||||||||||||||
| 出版者 | ||||||||||||||||||||||
| 出版者 | MDPI | |||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
| 著者版フラグ | ||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
| 値 | publisher | |||||||||||||||||||||
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| 出版タイプ | VoR | |||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||
