Bot Detection in Social Networks Based on Multilayered Deep Learning Approach
With the swift rise of social networking sites, they have now come to hold tremendous influence in the daily lives of millions around the globe. The value of one’s social media profile and its reach has soared highly. This has invited the use of fake accounts, spammers and bots to spread content fav...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IFSA Publishing, S.L.
2020-09-01
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| Series: | Sensors & Transducers |
| Subjects: | |
| Online Access: | https://sensorsportal.com/HTML/DIGEST/september_2020/Vol_244/P_3164.pdf |
| _version_ | 1797756071097401344 |
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| author | Sandeep Singh Sengar Sanjay Kumar Pradyot Raina Mukul Mahaliyan |
| author_facet | Sandeep Singh Sengar Sanjay Kumar Pradyot Raina Mukul Mahaliyan |
| author_sort | Sandeep Singh Sengar |
| collection | DOAJ |
| description | With the swift rise of social networking sites, they have now come to hold tremendous influence in the daily lives of millions around the globe. The value of one’s social media profile and its reach has soared highly. This has invited the use of fake accounts, spammers and bots to spread content favourable to those who control them. Thus, in this project we propose using a machine learning approach to identify bots and distinguish them from genuine users. This is achieved by compiling activity and profile information of users on Twitter and subsequently using natural language processing and supervised machine learning to achieve the objective classification. Finally, we compare and analyse the efficiency and accuracy of different learning models in order to ascertain the best performing bot detection system. |
| first_indexed | 2024-03-12T17:55:17Z |
| format | Article |
| id | doaj.art-2e43ad283c124eb2afe09e09c5932720 |
| institution | Directory Open Access Journal |
| issn | 2306-8515 1726-5479 |
| language | English |
| last_indexed | 2024-03-12T17:55:17Z |
| publishDate | 2020-09-01 |
| publisher | IFSA Publishing, S.L. |
| record_format | Article |
| series | Sensors & Transducers |
| spelling | doaj.art-2e43ad283c124eb2afe09e09c59327202023-08-02T16:07:28ZengIFSA Publishing, S.L.Sensors & Transducers2306-85151726-54792020-09-0124453743Bot Detection in Social Networks Based on Multilayered Deep Learning ApproachSandeep Singh Sengar0Sanjay Kumar1Pradyot Raina2Mukul Mahaliyan3Department of Computer Science and Engineering, SRM University-APDepartment of Computer Science and Engineering, Delhi Technological UniversityDepartment of Computer Science and Engineering, Delhi Technological UniversityDepartment of Computer Science and Engineering, Delhi Technological UniversityWith the swift rise of social networking sites, they have now come to hold tremendous influence in the daily lives of millions around the globe. The value of one’s social media profile and its reach has soared highly. This has invited the use of fake accounts, spammers and bots to spread content favourable to those who control them. Thus, in this project we propose using a machine learning approach to identify bots and distinguish them from genuine users. This is achieved by compiling activity and profile information of users on Twitter and subsequently using natural language processing and supervised machine learning to achieve the objective classification. Finally, we compare and analyse the efficiency and accuracy of different learning models in order to ascertain the best performing bot detection system.https://sensorsportal.com/HTML/DIGEST/september_2020/Vol_244/P_3164.pdfbot detectionmachine learningnatural language processingsocial networktext classification |
| spellingShingle | Sandeep Singh Sengar Sanjay Kumar Pradyot Raina Mukul Mahaliyan Bot Detection in Social Networks Based on Multilayered Deep Learning Approach Sensors & Transducers bot detection machine learning natural language processing social network text classification |
| title | Bot Detection in Social Networks Based on Multilayered Deep Learning Approach |
| title_full | Bot Detection in Social Networks Based on Multilayered Deep Learning Approach |
| title_fullStr | Bot Detection in Social Networks Based on Multilayered Deep Learning Approach |
| title_full_unstemmed | Bot Detection in Social Networks Based on Multilayered Deep Learning Approach |
| title_short | Bot Detection in Social Networks Based on Multilayered Deep Learning Approach |
| title_sort | bot detection in social networks based on multilayered deep learning approach |
| topic | bot detection machine learning natural language processing social network text classification |
| url | https://sensorsportal.com/HTML/DIGEST/september_2020/Vol_244/P_3164.pdf |
| work_keys_str_mv | AT sandeepsinghsengar botdetectioninsocialnetworksbasedonmultilayereddeeplearningapproach AT sanjaykumar botdetectioninsocialnetworksbasedonmultilayereddeeplearningapproach AT pradyotraina botdetectioninsocialnetworksbasedonmultilayereddeeplearningapproach AT mukulmahaliyan botdetectioninsocialnetworksbasedonmultilayereddeeplearningapproach |