Interpretable Classification of Wiki-Review Streams
Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10356073/ |
| _version_ | 1797376259943038976 |
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| author | Silvia Garcia-Mendez Fatima Leal Benedita Malheiro Juan Carlos Burguillo-Rial |
| author_facet | Silvia Garcia-Mendez Fatima Leal Benedita Malheiro Juan Carlos Burguillo-Rial |
| author_sort | Silvia Garcia-Mendez |
| collection | DOAJ |
| description | Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90% values for all evaluation metrics (accuracy, precision, recall, and <inline-formula> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula>-measure). |
| first_indexed | 2024-03-08T19:35:58Z |
| format | Article |
| id | doaj.art-0400dab2c00144c1a8922d2ad6ff0d18 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:35:58Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0400dab2c00144c1a8922d2ad6ff0d182023-12-26T00:12:04ZengIEEEIEEE Access2169-35362023-01-011114113714115110.1109/ACCESS.2023.334247210356073Interpretable Classification of Wiki-Review StreamsSilvia Garcia-Mendez0https://orcid.org/0000-0003-0533-1303Fatima Leal1https://orcid.org/0000-0003-4418-2590Benedita Malheiro2https://orcid.org/0000-0001-9083-4292Juan Carlos Burguillo-Rial3Information Technologies Group, atlanTTic, University of Vigo, Vigo, SpainResearch on Economics, Management and Information Technologies, Universidade Portucalense, Porto, PortugalISEP, Polytechnic of Porto, Porto, PortugalInformation Technologies Group, atlanTTic, University of Vigo, Vigo, SpainWiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90% values for all evaluation metrics (accuracy, precision, recall, and <inline-formula> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula>-measure).https://ieeexplore.ieee.org/document/10356073/Data reliability and fairnessdata-stream processing and classificationsynthetic datatransparencyvandalismwikis |
| spellingShingle | Silvia Garcia-Mendez Fatima Leal Benedita Malheiro Juan Carlos Burguillo-Rial Interpretable Classification of Wiki-Review Streams IEEE Access Data reliability and fairness data-stream processing and classification synthetic data transparency vandalism wikis |
| title | Interpretable Classification of Wiki-Review Streams |
| title_full | Interpretable Classification of Wiki-Review Streams |
| title_fullStr | Interpretable Classification of Wiki-Review Streams |
| title_full_unstemmed | Interpretable Classification of Wiki-Review Streams |
| title_short | Interpretable Classification of Wiki-Review Streams |
| title_sort | interpretable classification of wiki review streams |
| topic | Data reliability and fairness data-stream processing and classification synthetic data transparency vandalism wikis |
| url | https://ieeexplore.ieee.org/document/10356073/ |
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