Investigating toxicity changes of cross-community redditors from 2 billion posts and comments
<p dir="ltr">This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and...
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| مؤلفون آخرون: | , |
| منشور في: |
2022
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| _version_ | 1864513517540868096 |
|---|---|
| author | Hind Almerekhi (7434776) |
| author2 | Haewoon Kwak (5747558) Bernard J. Jansen (7434779) |
| author2_role | author author |
| author_facet | Hind Almerekhi (7434776) Haewoon Kwak (5747558) Bernard J. Jansen (7434779) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hind Almerekhi (7434776) Haewoon Kwak (5747558) Bernard J. Jansen (7434779) |
| dc.date.none.fl_str_mv | 2022-08-18T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.7717/peerj-cs.1059 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Investigating_toxicity_changes_of_cross-community_redditors_from_2_billion_posts_and_comments/25516162 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Human-centred computing Machine learning Toxicity Posting behavior Online communities Machine learning Online hate |
| dc.title.none.fl_str_mv | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the same community, across multiple communities, and over time. As for the toxicity detection performance, the BERT model achieved a 91.27% classification accuracy and an area under the receiver operating characteristic curve (AUC) score of 0.963 and outperformed several baseline machine learning and neural network models. The user behavior toxicity analysis showed that 16.11% of users publish toxic posts, and 13.28% of users publish toxic comments. However, results showed that 30.68% of users publishing posts and 81.67% of users publishing comments exhibit changes in their toxicity across different communities, indicating that users adapt their behavior to the communities’ norms. Furthermore, time series analysis with the Granger causality test of the volume of links and toxicity in user content showed that toxic comments are Granger caused by links in comments.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.1059" target="_blank">https://dx.doi.org/10.7717/peerj-cs.1059</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_64ff550ac7e477bcc1b7807936bdcf0e |
| identifier_str_mv | 10.7717/peerj-cs.1059 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25516162 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Investigating toxicity changes of cross-community redditors from 2 billion posts and commentsHind Almerekhi (7434776)Haewoon Kwak (5747558)Bernard J. Jansen (7434779)Information and computing sciencesData management and data scienceHuman-centred computingMachine learningRedditToxicityPosting behaviorOnline communitiesMachine learningOnline hate<p dir="ltr">This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the same community, across multiple communities, and over time. As for the toxicity detection performance, the BERT model achieved a 91.27% classification accuracy and an area under the receiver operating characteristic curve (AUC) score of 0.963 and outperformed several baseline machine learning and neural network models. The user behavior toxicity analysis showed that 16.11% of users publish toxic posts, and 13.28% of users publish toxic comments. However, results showed that 30.68% of users publishing posts and 81.67% of users publishing comments exhibit changes in their toxicity across different communities, indicating that users adapt their behavior to the communities’ norms. Furthermore, time series analysis with the Granger causality test of the volume of links and toxicity in user content showed that toxic comments are Granger caused by links in comments.</p><h2>Other Information</h2><p dir="ltr">Published in: PeerJ Computer Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.7717/peerj-cs.1059" target="_blank">https://dx.doi.org/10.7717/peerj-cs.1059</a></p>2022-08-18T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.7717/peerj-cs.1059https://figshare.com/articles/journal_contribution/Investigating_toxicity_changes_of_cross-community_redditors_from_2_billion_posts_and_comments/25516162CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/255161622022-08-18T03:00:00Z |
| spellingShingle | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments Hind Almerekhi (7434776) Information and computing sciences Data management and data science Human-centred computing Machine learning Toxicity Posting behavior Online communities Machine learning Online hate |
| status_str | publishedVersion |
| title | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments |
| title_full | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments |
| title_fullStr | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments |
| title_full_unstemmed | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments |
| title_short | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments |
| title_sort | Investigating toxicity changes of cross-community redditors from 2 billion posts and comments |
| topic | Information and computing sciences Data management and data science Human-centred computing Machine learning Toxicity Posting behavior Online communities Machine learning Online hate |