Automatically identifying the function and intent of posts in underground forums
Abstract The automatic classification of posts from hacking-related online forums is of potential value for the understanding of user behaviour in social networks relating to cybercrime. We designed annotation schema to label forum posts for three properties: post type, author intent, and addressee....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-11-01
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| Series: | Crime Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40163-018-0094-4 |
| _version_ | 1811212622446460928 |
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| author | Andrew Caines Sergio Pastrana Alice Hutchings Paula J. Buttery |
| author_facet | Andrew Caines Sergio Pastrana Alice Hutchings Paula J. Buttery |
| author_sort | Andrew Caines |
| collection | DOAJ |
| description | Abstract The automatic classification of posts from hacking-related online forums is of potential value for the understanding of user behaviour in social networks relating to cybercrime. We designed annotation schema to label forum posts for three properties: post type, author intent, and addressee. The post type indicates whether the text is a question, a comment, and so on. The author’s intent in writing the post could be positive, negative, moderating discussion, showing gratitude to another user, etc. The addressee of a post tends to be a general audience (e.g. other forum users) or individual users who have already contributed to a threaded discussion. We manually annotated a sample of posts and returned substantial agreement for post type and addressee, and fair agreement for author intent. We trained rule-based (logical) and machine learning (statistical) classification models to predict these labels automatically, and found that a hybrid logical–statistical model performs best for post type and author intent, whereas a purely statistical model is best for addressee. We discuss potential applications for this data, including the analysis of thread conversations in forum data and the identification of key actors within social networks. |
| first_indexed | 2024-04-12T05:32:15Z |
| format | Article |
| id | doaj.art-365f9d4aea90451ca4b94aff5dfe8309 |
| institution | Directory Open Access Journal |
| issn | 2193-7680 |
| language | English |
| last_indexed | 2024-04-12T05:32:15Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Crime Science |
| spelling | doaj.art-365f9d4aea90451ca4b94aff5dfe83092022-12-22T03:46:00ZengBMCCrime Science2193-76802018-11-017111410.1186/s40163-018-0094-4Automatically identifying the function and intent of posts in underground forumsAndrew Caines0Sergio Pastrana1Alice Hutchings2Paula J. Buttery3Natural Language & Information Processing, Department of Computer Science & Technology, University of CambridgeCambridge Cybercrime Centre, Department of Computer Science & Technology, University of CambridgeCambridge Cybercrime Centre, Department of Computer Science & Technology, University of CambridgeNatural Language & Information Processing, Department of Computer Science & Technology, University of CambridgeAbstract The automatic classification of posts from hacking-related online forums is of potential value for the understanding of user behaviour in social networks relating to cybercrime. We designed annotation schema to label forum posts for three properties: post type, author intent, and addressee. The post type indicates whether the text is a question, a comment, and so on. The author’s intent in writing the post could be positive, negative, moderating discussion, showing gratitude to another user, etc. The addressee of a post tends to be a general audience (e.g. other forum users) or individual users who have already contributed to a threaded discussion. We manually annotated a sample of posts and returned substantial agreement for post type and addressee, and fair agreement for author intent. We trained rule-based (logical) and machine learning (statistical) classification models to predict these labels automatically, and found that a hybrid logical–statistical model performs best for post type and author intent, whereas a purely statistical model is best for addressee. We discuss potential applications for this data, including the analysis of thread conversations in forum data and the identification of key actors within social networks.http://link.springer.com/article/10.1186/s40163-018-0094-4Underground forumsCybercrimeDeviant behaviourMachine learningNatural language processing |
| spellingShingle | Andrew Caines Sergio Pastrana Alice Hutchings Paula J. Buttery Automatically identifying the function and intent of posts in underground forums Crime Science Underground forums Cybercrime Deviant behaviour Machine learning Natural language processing |
| title | Automatically identifying the function and intent of posts in underground forums |
| title_full | Automatically identifying the function and intent of posts in underground forums |
| title_fullStr | Automatically identifying the function and intent of posts in underground forums |
| title_full_unstemmed | Automatically identifying the function and intent of posts in underground forums |
| title_short | Automatically identifying the function and intent of posts in underground forums |
| title_sort | automatically identifying the function and intent of posts in underground forums |
| topic | Underground forums Cybercrime Deviant behaviour Machine learning Natural language processing |
| url | http://link.springer.com/article/10.1186/s40163-018-0094-4 |
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