Detecting video spammers in YouTube social media
Social media is any site that provides a network of people with a place to make connections.An example of the media is YouTube that connects people through video sharing.Unfortunately, due to the explosive number of users and various content sharing, there exist malicious users who aim to self-promo...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2017
|
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/22833/1/ICOCI%202017%20228-234.pdf |
| _version_ | 1825804823365156864 |
|---|---|
| author | Yusof, Yuhanis Sadoon, Omar Hadeb |
| author_facet | Yusof, Yuhanis Sadoon, Omar Hadeb |
| author_sort | Yusof, Yuhanis |
| collection | UUM |
| description | Social media is any site that provides a network of people with a place to make connections.An example of the media is YouTube that connects people through video sharing.Unfortunately, due to the explosive number of users and various content sharing, there exist malicious users who aim to self-promote their videos or broadcast unrelated content. Even though the detection of malicious users is based on various features such as content details, social activity, social network analyzing, or hybrid, the detection rate is still considered low (i.e. 46%).This study proposes a new set of features by constructing features based on the Edge Rank algorithm.Experiments were performed using nine classifiers of different learning; decision tree, function-based and Bayesian. The results showed that the proposed
video spammers detection feature set is beneficial as the highest accuracy (i.e average) is as high as 98% and the lowest was 74%.The proposed work
would benefit YouTube users as malicious users who are sharing non relevant content can be automatically detected.This is because system resources can be optimized as YouTube users are presented with the required
content only. |
| first_indexed | 2024-07-04T06:22:00Z |
| format | Conference or Workshop Item |
| id | uum-22833 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:22:00Z |
| publishDate | 2017 |
| record_format | eprints |
| spelling | uum-228332017-07-26T08:31:48Z https://repo.uum.edu.my/id/eprint/22833/ Detecting video spammers in YouTube social media Yusof, Yuhanis Sadoon, Omar Hadeb QA75 Electronic computers. Computer science Social media is any site that provides a network of people with a place to make connections.An example of the media is YouTube that connects people through video sharing.Unfortunately, due to the explosive number of users and various content sharing, there exist malicious users who aim to self-promote their videos or broadcast unrelated content. Even though the detection of malicious users is based on various features such as content details, social activity, social network analyzing, or hybrid, the detection rate is still considered low (i.e. 46%).This study proposes a new set of features by constructing features based on the Edge Rank algorithm.Experiments were performed using nine classifiers of different learning; decision tree, function-based and Bayesian. The results showed that the proposed video spammers detection feature set is beneficial as the highest accuracy (i.e average) is as high as 98% and the lowest was 74%.The proposed work would benefit YouTube users as malicious users who are sharing non relevant content can be automatically detected.This is because system resources can be optimized as YouTube users are presented with the required content only. 2017-04-25 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/22833/1/ICOCI%202017%20228-234.pdf Yusof, Yuhanis and Sadoon, Omar Hadeb (2017) Detecting video spammers in YouTube social media. In: 6th International Conference on Computing & Informatics (ICOCI2017), 25 - 27 April 2017, Kuala Lumpur. http://icoci.cms.net.my/PROCEEDINGS/2017/Pdf_Version_Chap04e/PID82-228-234e.pdf |
| spellingShingle | QA75 Electronic computers. Computer science Yusof, Yuhanis Sadoon, Omar Hadeb Detecting video spammers in YouTube social media |
| title | Detecting video spammers in YouTube social media |
| title_full | Detecting video spammers in YouTube social media |
| title_fullStr | Detecting video spammers in YouTube social media |
| title_full_unstemmed | Detecting video spammers in YouTube social media |
| title_short | Detecting video spammers in YouTube social media |
| title_sort | detecting video spammers in youtube social media |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/22833/1/ICOCI%202017%20228-234.pdf |
| work_keys_str_mv | AT yusofyuhanis detectingvideospammersinyoutubesocialmedia AT sadoonomarhadeb detectingvideospammersinyoutubesocialmedia |