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An Online Multilingual Hate speech Recognition System [article]

Neeraj Vashistha, Arkaitz Zubiaga
2020 arXiv   pre-print
While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased.  ...  We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.  ...  The main contribution of this work are as follows: • Creating a system which is trained on a sufficiently large corpus of hate speech text. • A system which is multilingual, can work on different languages  ... 
arXiv:2011.11523v2 fatcat:smijrlwu3bc6pfqufeiwhz4oei

Challenges and Considerations with Code-Mixed NLP for Multilingual Societies [article]

Vivek Srivastava, Mayank Singh
2021 arXiv   pre-print
speech for multilingual societies.  ...  It often results in language mixing, a.k.a. code-mixing, when a multilingual speaker switches between multiple languages in a single utterance of a text or speech.  ...  An NLP system, depending on the characteristics of hate speech, should be able to process and detect such hateful content effectively (hereafter, hate speech detection (HSD)).  ... 
arXiv:2106.07823v1 fatcat:7jkoetjwtbf3za36pjdwbcnzwq

A Survey on Multilingual Hate Speech Detection and Classification by Machine Learning Techniques

FHA. Shibly*, Uzzal Sharma**, HMM. Naleer***
2021 Zenodo  
Social media plays a significant role in hate speech spreading online.  ...  This paper proposes background detection of hate speech. Furthermore, anti-social behaviour topics and recent contributions of hate speech are reviewed.  ...  According to researchers, Multilingual Hate Speech Detection is an effective form of classifying data and has a bright future in the field of autonomous detection.  ... 
doi:10.5281/zenodo.5599745 fatcat:lncqkhbbybgndka6eovvafcyti

ANDES at SemEval-2020 Task 12: A jointly-trained BERT multilingual model for offensive language detection [article]

Juan Manuel Pérez, Aymé Arango, Franco Luque
2020 arXiv   pre-print
Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages.  ...  This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection.  ...  System Overview Our model is a fine-tuned version of Multilingual BERT (Devlin et al., 2018) .  ... 
arXiv:2008.06408v1 fatcat:agqymhkw6rd77g6ggqphjqiegi

Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media [article]

Ananya Srivastava, Mohammed Hasan, Bhargav Yagnik, Rahee Walambe, Ketan Kotecha
2021 arXiv   pre-print
Thus, the worldwide hate speech detection rate of around 44% drops even more considering the content in Indian colloquial languages and slangs.  ...  Hate speech detection algorithms deployed by most social networking platforms are unable to filter out offensive and abusive content posted in these code-mixed languages.  ...  Hate speech has an adverse effect on the mental health of individuals [11] . Twitter have seen about 900% increase in hate speech during COVID-19 pandemic [15] .  ... 
arXiv:2105.04913v1 fatcat:e6fjcpiinfcznl4rxil3qrrbau

An Improve Framework for hate speech detection using Machine Learning Approach

J. Palimote, F. Gaage
2021 IJARCCE  
The paper proposes an improve framework for hate speech detection using machine learning approach.  ...  We proposed a system to detect hate speech using machine learning approach, we trained our model using support vector machine and random forest classifier and had an accuracy of 95% and 99%.  ...  Bohra [8] break down the issue of hate speech recognition in code-blended messages and present a Hindi-English codeblended dataset comprising of tweets posted online on Twitter.  ... 
doi:10.17148/ijarcce.2021.10332 fatcat:6azn4xim6rgypiqtagjgwypyjm

WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans [article]

Tharindu Ranasinghe, Diptanu Sarkar, Marcos Zampieri, Alexander Ororbia
2021 arXiv   pre-print
In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms.  ...  Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts.  ...  Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models. In Proceedings of SBP-BRiMS.  ... 
arXiv:2104.04630v3 fatcat:2uqhyrjudbbr5jwgnljqnieslu


Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babaoğlu
2019 Proceedings of the 13th International Workshop on Semantic Evaluation  
In this paper, we describe our contribution in SemEval-2019: subtask A of task 5 "Multilingual detection of hate speech against immigrants and women in Twitter (HatEval)".  ...  We developed two hate speech detection model variants through Tw-StAR framework.  ...  Unfortunately, online Hate Speech (HS) is spreading widely, forming a serious problem that can lead to actual hate crimes (Matsuda, 2018) .  ... 
doi:10.18653/v1/s19-2090 dblp:conf/semeval/MulkiAHB19 fatcat:hinn5iy3a5blnmajbevstnuzay

SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection [article]

Fatemah Husain, Jooyeon Lee, Samuel Henry, Ozlem Uzuner
2020 arXiv   pre-print
This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media.  ...  Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations.  ...  the shooter had an online profile full of hate speech language (Keyser, 2018) .  ... 
arXiv:2007.13974v1 fatcat:bjmo3zpjyremvms7fbqs4bul34

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Wassen Aldjanabi, Abdelghani Dahou, Mohammed A. A. Al-qaness, Mohamed Abd Elaziz, Ahmed Mohamed Helmi, Robertas Damaševičius
2021 Informatics  
This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system.  ...  More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model.  ...  Online hate speech is characterized as the use of an offensive language, aimed at a specific group of people who share some common trait [1] , while social networks have been recognized as a very favorable  ... 
doi:10.3390/informatics8040069 fatcat:55a7zksytfgkxkpsvh7jkr4vn4

Tracking Hate in Social Media: Evaluation, Challenges and Approaches

Sandip Modha, Thomas Mandl, Prasenjit Majumder, Daksh Patel
2020 SN Computer Science  
This paper presents online hate speech as a societal and computational challenge.  ...  HASOC intends to stimulate research and development in hate speech recognition across different languages.  ...  The development of such algorithmic detection systems is an active research area at the moment. Systems and datasets for hate speech identification require a working definition of hate speech.  ... 
doi:10.1007/s42979-020-0082-0 fatcat:ami2jyvl4bflxl2jupxqbsgshq

Pegasus@Dravidian-CodeMix-HASOC2021: Analyzing Social Media Content for Detection of Offensive Text [article]

Pawan Kalyan Jada, Konthala Yasaswini, Karthik Puranik, Anbukkarasi Sampath, Sathiyaraj Thangasamy, Kingston Pal Thamburaj
2021 arXiv   pre-print
Offensive comments/posts on the social media platforms, can affect an individual, a group or underage alike.  ...  Online hate speech and offensive content produces challenges to the society.  ...  In Waseem and Hovy, the authors provided a dataset of 16k tweets annotated for hate speech and analysed the features that help detect hate speech in the corpus.  ... 
arXiv:2111.09836v1 fatcat:mtbizuer5jhj7b5ybrsa5q6dia

A systematic review of Hate Speech automatic detection using Natural Language Processing [article]

Md Saroar Jahan, Mourad Oussalah
2021 arXiv   pre-print
With the multiplication of social media platforms, which offer anonymity, easy access and online community formation, and online debate, the issue of hate speech detection and tracking becomes a growing  ...  GAB 33,776 0.43 Hate, Not hate Qian et al. [119], 82 Online Hate Speech (Reddit) 2019 GitHub Reddit 22,324 0.24 Hate, Not hate Qian et al. [119], 82 Multilingual and Multi- Aspect Hate Speech  ...  Many of these studies have also tackled hate speech in several non-English languages and online communities.  ... 
arXiv:2106.00742v1 fatcat:qwxjwgma4zaynemge57cu7xqlm

Pretrained Transformers for Offensive Language Identification in Tanglish [article]

Sean Benhur, Kanchana Sivanraju
2021 arXiv   pre-print
This paper describes the system submitted to Dravidian-Codemix-HASOC2021: Hate Speech and Offensive Language Identification in Dravidian Languages (Tamil-English and Malayalam-English).  ...  Our approach utilizes pooling the last layers of pretrained transformer multilingual BERT for this task which helped us achieve rank nine on the leaderboard with a weighted average score of 0.61 for the  ...  Shared tasks like HASOC-19 [26] dealt with hate speech and offensive language identification in Indo-European languages.  ... 
arXiv:2110.02852v4 fatcat:tsuszyi2grea5lqfflrn5ovadi

Cross-lingual embeddings for hate speech detection in comments

Rok Marinšek, Marko Robnik-Šikonja
2019 Zenodo  
With the recent explosion of social media content, the amount of online hate speech is increasing, making it impossible to filter it manually.  ...  We use cross-lingual embeddings to achieve an acceptable performance in hate speech detection in a target language, using data from another language.  ...  It is important to note that amateur annotators are more likely to label items as hate speech and systems trained on expert annotations outperfrom systems trained on amateur annotations.  ... 
doi:10.5281/zenodo.3894644 fatcat:5aoaia2mifa53lnf22fkalg7oi
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