Criminal Behavior Identification Using Social Media Forensics
<p dir="ltr">Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environme...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513531461763072 |
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| author | Noorulain Ashraf (17541996) |
| author2 | Danish Mahmood (9590684) Muath A. Obaidat (17541513) Ghufran Ahmed (6298196) Adnan Akhunzada (3134064) |
| author2_role | author author author author |
| author_facet | Noorulain Ashraf (17541996) Danish Mahmood (9590684) Muath A. Obaidat (17541513) Ghufran Ahmed (6298196) Adnan Akhunzada (3134064) |
| author_role | author |
| dc.creator.none.fl_str_mv | Noorulain Ashraf (17541996) Danish Mahmood (9590684) Muath A. Obaidat (17541513) Ghufran Ahmed (6298196) Adnan Akhunzada (3134064) |
| dc.date.none.fl_str_mv | 2022-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/electronics11193162 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Criminal_Behavior_Identification_Using_Social_Media_Forensics/24717495 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Human society Criminology Information and computing sciences Artificial intelligence Human-centred computing Machine learning behavior social media machine learning natural language processing aggressive behavior abusive behavior cyber hate antisocial depressive behavior |
| dc.title.none.fl_str_mv | Criminal Behavior Identification Using Social Media Forensics |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.</p><h2>Other Information</h2><p dir="ltr">Published in: Electronics<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/10.3390/electronics11193162" target="_blank">https://dx.doi.org/10.3390/electronics11193162</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3d401e15181a116e35c469543b298529 |
| identifier_str_mv | 10.3390/electronics11193162 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717495 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Criminal Behavior Identification Using Social Media ForensicsNoorulain Ashraf (17541996)Danish Mahmood (9590684)Muath A. Obaidat (17541513)Ghufran Ahmed (6298196)Adnan Akhunzada (3134064)Human societyCriminologyInformation and computing sciencesArtificial intelligenceHuman-centred computingMachine learningbehaviorsocial mediatwittermachine learningnatural language processingaggressive behaviorabusive behaviorcyber hateantisocialdepressive behavior<p dir="ltr">Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.</p><h2>Other Information</h2><p dir="ltr">Published in: Electronics<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/10.3390/electronics11193162" target="_blank">https://dx.doi.org/10.3390/electronics11193162</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p>2022-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/electronics11193162https://figshare.com/articles/journal_contribution/Criminal_Behavior_Identification_Using_Social_Media_Forensics/24717495CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247174952022-10-01T00:00:00Z |
| spellingShingle | Criminal Behavior Identification Using Social Media Forensics Noorulain Ashraf (17541996) Human society Criminology Information and computing sciences Artificial intelligence Human-centred computing Machine learning behavior social media machine learning natural language processing aggressive behavior abusive behavior cyber hate antisocial depressive behavior |
| status_str | publishedVersion |
| title | Criminal Behavior Identification Using Social Media Forensics |
| title_full | Criminal Behavior Identification Using Social Media Forensics |
| title_fullStr | Criminal Behavior Identification Using Social Media Forensics |
| title_full_unstemmed | Criminal Behavior Identification Using Social Media Forensics |
| title_short | Criminal Behavior Identification Using Social Media Forensics |
| title_sort | Criminal Behavior Identification Using Social Media Forensics |
| topic | Human society Criminology Information and computing sciences Artificial intelligence Human-centred computing Machine learning behavior social media machine learning natural language processing aggressive behavior abusive behavior cyber hate antisocial depressive behavior |