Please use this identifier to cite or link to this item:
https://research.matf.bg.ac.rs/handle/123456789/1371
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Vujičić Stanković, Staša | en_US |
dc.contributor.author | Mladenović, Miljana | en_US |
dc.date.accessioned | 2024-10-23T12:40:02Z | - |
dc.date.available | 2024-10-23T12:40:02Z | - |
dc.date.issued | 2023-12-01 | - |
dc.identifier.uri | https://research.matf.bg.ac.rs/handle/123456789/1371 | - |
dc.description | Article is published in Journal of Big Data vol. 10, no. 1 Article no. 109, DOI <a href="10.1186/s40537-023-00766-9">10.1186/s40537-023-00766-9</a> | en_US |
dc.description.abstract | Hate Speech encompasses different forms of trolling, bullying, harassment, and threats directed against specific individuals or groups. This phenomena is mainly expressed on Social Networks. For sports players, Social Media is a means of communication with the widest part of their fans and a way to face different cyber-aggression forms. These virtual attacks can harm players, distress them, cause them to feel bad for a long time, or even escalate into physical violence. To date, athletes were not observed as a vulnerable group, so they were not a subject of automatic Hate Speech detection and recognition from content published on Social Media. This paper explores whether a model trained on the dataset from one Social Media and not related to any specific domain can be efficient for the Hate Speech binary classification of test sets regarding the sports domain. The experiments deal with Hate Speech detection in Serbian. BiLSTM deep neural network was learned with different parameters, and the results showed high Precision of detecting Hate Speech in sports domain (96% and 97%) and pretty low Recall. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Journal of Big Data | en_US |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.subject | Automatic hate speech recognition | en_US |
dc.subject | Hate speech | en_US |
dc.subject | Social media | en_US |
dc.subject | Social networks | en_US |
dc.subject | Sport | en_US |
dc.title | An approach to automatic classification of hate speech in sports domain on social media | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1186/s40537-023-00766-9 | - |
dc.identifier.scopus | 2-s2.0-85162931559 | - |
dc.identifier.isi | 001015429000001 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85162931559 | - |
dc.contributor.affiliation | Informatics and Computer Science | en_US |
dc.relation.issn | 2196-1115 | en_US |
dc.description.rank | M21a | en_US |
dc.relation.firstpage | Article no. 109 | en_US |
dc.relation.volume | 10 | en_US |
dc.relation.issue | 1 | en_US |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.openairetype | Article | - |
crisitem.author.dept | Informatics and Computer Science | - |
crisitem.author.orcid | 0000-0002-7200-3724 | - |
Appears in Collections: | Research outputs |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
s40537-023-00766-9.pdf | 1.32 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
8
checked on Dec 18, 2024
Page view(s)
14
checked on Dec 24, 2024
Google ScholarTM
Check
Altmetric
Altmetric
This item is licensed under a Creative Commons License