Violence Detection in Social Media-Review
| dc.contributor.author | Dikwatta, U. | |
| dc.contributor.author | Fernando, T.G.I. | |
| dc.date.accessioned | 2022-03-18T06:44:15Z | |
| dc.date.available | 2022-03-18T06:44:15Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Social media has become a vital part of humans’ day to day life. Different users engage with social media differently. With the increased usage of social media, many researchers have investigated different aspects of social media. Many examples in the recent past show, content in the social media can generate violence in the user community. Violence in social media can be categorised into aggregation in comments, cyber-bullying and incidents like protests, murders. Identifying violent content in social media is a challenging task: social media posts contain both the visual and text as well as these posts may contain hidden meaning according to the users’ context and other background information. This paper summarizes the different social media violent categories and existing methods to detect the violent content. | en_US |
| dc.identifier.citation | Dikwatta, U., Fernando, T.G.I. (2019). Violence Detection in Social Media-Review, Vidyodaya Journal of Science Vol. 22 No. 02 (2019) 7-16 | en_US |
| dc.identifier.doi | https://doi.org/10.31357/vjs.v22i2.4385 | en_US |
| dc.identifier.uri | http://dr.lib.sjp.ac.lk/handle/123456789/10644 | |
| dc.language.iso | en | en_US |
| dc.publisher | Faculty of Applied Sciences, University of Sri Jayewardenepura | en_US |
| dc.subject | Machine learning, natural language processing, violence, social media, convolution neural network | en_US |
| dc.title | Violence Detection in Social Media-Review | en_US |
| dc.type | Article | en_US |