Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks
The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4788 |
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| author | Manuel López-Vizcaíno Francisco J. Nóvoa Thierry Artieres Fidel Cacheda |
| author_facet | Manuel López-Vizcaíno Francisco J. Nóvoa Thierry Artieres Fidel Cacheda |
| author_sort | Manuel López-Vizcaíno |
| collection | DOAJ |
| description | The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>t</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>a</mi><mi>w</mi><mi>a</mi><mi>r</mi><mi>e</mi><mspace width="4pt"></mspace><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>a</mi><mi>P</mi></mrow></semantics></math></inline-formula>) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset. |
| first_indexed | 2024-03-11T03:20:50Z |
| format | Article |
| id | doaj.art-d426e661a10346099482e6cc2c90d955 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:50Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d426e661a10346099482e6cc2c90d9552023-11-18T03:12:35ZengMDPI AGSensors1424-82202023-05-012310478810.3390/s23104788Site Agnostic Approach to Early Detection of Cyberbullying on Social Media NetworksManuel López-Vizcaíno0Francisco J. Nóvoa1Thierry Artieres2Fidel Cacheda3CITIC Research Center, Computer Science and Information Technologies Department, Campus de Elviña, 15071 A Coruña, SpainCITIC Research Center, Computer Science and Information Technologies Department, Campus de Elviña, 15071 A Coruña, SpainAix Marseille University, Université de Toulon, CNRS, LIS, Ecole Centrale Marseille, 13397 Marseille, FranceCITIC Research Center, Computer Science and Information Technologies Department, Campus de Elviña, 15071 A Coruña, SpainThe rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>t</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>a</mi><mi>w</mi><mi>a</mi><mi>r</mi><mi>e</mi><mspace width="4pt"></mspace><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></semantics></math></inline-formula> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>a</mi><mi>P</mi></mrow></semantics></math></inline-formula>) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset.https://www.mdpi.com/1424-8220/23/10/4788cyberbullyingsocial networksearly detectionmachine learningtext featuresmultiple-instance learning |
| spellingShingle | Manuel López-Vizcaíno Francisco J. Nóvoa Thierry Artieres Fidel Cacheda Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks Sensors cyberbullying social networks early detection machine learning text features multiple-instance learning |
| title | Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks |
| title_full | Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks |
| title_fullStr | Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks |
| title_full_unstemmed | Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks |
| title_short | Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks |
| title_sort | site agnostic approach to early detection of cyberbullying on social media networks |
| topic | cyberbullying social networks early detection machine learning text features multiple-instance learning |
| url | https://www.mdpi.com/1424-8220/23/10/4788 |
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