{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240850","sets":["6164:6165:6462:11854"]},"path":["11854"],"owner":"11","recid":"240850","title":["機械学習を用いた X 上の炎上予測モデルの提案"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-10-15"},"_buckets":{"deposit":"fbaec6ec-5e32-4ebf-8591-bf1ff743e2e5"},"_deposit":{"id":"240850","pid":{"type":"depid","value":"240850","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"機械学習を用いた X 上の炎上予測モデルの提案","author_link":["661735","661736","661737","661738","661739","661740"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いた X 上の炎上予測モデルの提案","subitem_title_language":"ja"},{"subitem_title":"Proposal for a Machine Learning-Based Flare-Up Prediction Model on Platform X","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ソーシャルメディア，X，Twitter，炎上","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2024-10-15","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"明治大学大学院"},{"subitem_text_value":"明治大学"},{"subitem_text_value":"明治大学／レンジフォース株式会社"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Meiji University /Rangeforce, Inc.","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/240850/files/IPSJ-CSS2024104.pdf","label":"IPSJ-CSS2024104.pdf"},"date":[{"dateType":"Available","dateValue":"2026-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2024104.pdf","filesize":[{"value":"322.2 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"ce855486-3771-47bb-b6a8-a4c3539bb16c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"家村, 芽弥"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小玉, 直樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"齋藤, 孝道"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Meiya, Iemura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Naoki, Kodama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takamichi, Saito","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"インターネット技術の普及と発展に伴い，ソーシャルネットワーキングサービス（SNS）が広く利用され，情報発信や共有において中心的な役割を果たすようになった．この進展は情報の発信や共有を容易にする一方で，炎上や偽情報の拡散といった新たな課題をもたらしている．本論文では，炎上問題に対応するため炎上予測モデルの開発を目的とし，機械学習技術を用いてSNS上の単一ポストが炎上するか否かを早期に予測する二値分類モデルを二段階アプローチで構築した．Xで話題となったポストとアカウント情報をデータセットとして採用し，各ポストに対する炎上予測及びその予測結果に基づいて分析を行った．その結果，単一ポストが炎上する前に，炎上すると予測することができた．また，政治的な話題では，比較用の話題を使用した比較データセットと比べ，炎上すると予測したポストの割合が大きかった．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"With the widespread adoption and development of internet technology, social networking services (SNS) have become widely used, playing a central role in information dissemination and sharing. While this progress has facilitated the transmission and sharing of information, it has also introduced new challenges such as the spread of online firestorms and misinformation. This paper aims to address the problem of online firestorms by developing a firestorm prediction model. We constructed a binary classification model using machine learning techniques to predict whether a single post on SNS will ignite a firestorm early on. We adopted posts and account information that became topics of interest on platform X as our dataset and conducted predictions and analyses based on those predictions for each post. As a result, it was possible to predict a post would become a firestorm before it actually did. Additionally, political topics showed a higher proportion of predicted firestorms compared to a comparative dataset using different topics. ","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"773","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2024論文集"}],"bibliographicPageStart":"767","bibliographicIssueDates":{"bibliographicIssueDate":"2024-10-15","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":240850,"updated":"2025-03-06T05:34:16.921267+00:00","links":{},"created":"2025-01-19T01:45:16.585052+00:00"}