Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/10814
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Eyuboglu, A.B. | - |
dc.contributor.author | Altun, B. | - |
dc.contributor.author | Arslan, M.B. | - |
dc.contributor.author | Sonmezer, E. | - |
dc.contributor.author | Kutlu, M. | - |
dc.date.accessioned | 2023-10-24T07:04:28Z | - |
dc.date.available | 2023-10-24T07:04:28Z | - |
dc.date.issued | 2023 | - |
dc.identifier.isbn | 9783031424472 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://doi.org/10.1007/978-3-031-42448-9_14 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/10814 | - |
dc.description | Proceedings of the 14th International Conference of the Cross-Language Evaluation Forum for European Languages, CLEF 2023 -- 18 September 2023 through 21 September 2023 -- 300519 | en_US |
dc.description.abstract | In this paper, we present our participation in CLEF 2022 CheckThat! Lab’s Task 1 on detecting check-worthy and verifiable claims and attention-worthy and harmful tweets. We participated in all subtasks of Task 1 for Arabic, Bulgarian, Dutch, English, and Turkish datasets. We investigate the impact of fine-tuning various transformer models and how to increase training data size using machine translation. We also use feed-forward networks with the Manifold Mixup regularization for the respective tasks. We are ranked first in detecting factual claims in Arabic and harmful tweets in Dutch. In addition, we are ranked second in detecting check-worthy claims in Arabic and Bulgarian. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Attention-worthy tweets | en_US |
dc.subject | Check-worthiness | en_US |
dc.subject | Fact-Checking | en_US |
dc.subject | Factual Claims | en_US |
dc.subject | Harmful tweets | en_US |
dc.subject | Computational linguistics | en_US |
dc.subject | Attention-worthy tweet | en_US |
dc.subject | Check-worthiness | en_US |
dc.subject | Fact-checking | en_US |
dc.subject | Factual claim | en_US |
dc.subject | Fine tuning | en_US |
dc.subject | Harmful tweet | en_US |
dc.subject | Social media | en_US |
dc.subject | Subtask | en_US |
dc.subject | Transformer modeling | en_US |
dc.subject | Turkishs | en_US |
dc.subject | Social networking (online) | en_US |
dc.title | Fight Against Misinformation on Social Media: Detecting Attention-Worthy and Harmful Tweets and Verifiable and Check-Worthy Claims | en_US |
dc.type | Conference Object | en_US |
dc.department | TOBB ETÜ | en_US |
dc.identifier.volume | 14163 LNCS | en_US |
dc.identifier.startpage | 161 | en_US |
dc.identifier.endpage | 173 | en_US |
dc.identifier.scopus | 2-s2.0-85172383456 | en_US |
dc.institutionauthor | … | - |
dc.identifier.doi | 10.1007/978-3-031-42448-9_14 | - |
dc.authorscopusid | 57866065800 | - |
dc.authorscopusid | 57210372548 | - |
dc.authorscopusid | 57226399387 | - |
dc.authorscopusid | 57867078600 | - |
dc.authorscopusid | 35299304300 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q3 | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.openairetype | Conference Object | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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