NAYEL @LT-EDI-ACL2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM

Ashraf, Nsrin; Taha, Mohamed; Elfattah, Ahmed Abd; Nayel, Hamada A.

doi:10.18653/v1/2022.ltedi-1.42

Cited by 6 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study implemented neural network using sentence embedding and ensemble model in Tamil, English and Tamil-English [4]. Some of the studies implemented different classification algorithms using TF-IDF to classify the YouTube comments as homophobic and transphobic in English, Tamil and Tamil-English [5]. Similarly, the combination of word embeddings and Support Vector Machine (SVM) are implemented along with BERT-based models in English, Tamil and English-Tamil [7].…”

Section: Related Workmentioning

confidence: 99%

Detection of Homophobia & Transphobia in Malayalam and Tamil: Exploring Deep Learning Methods

Sharma

Gupta

Singh

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

The increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making social media toxic. Homophobia and transphobia constitute offensive comments against LGBT+ community. It becomes imperative to detect and handle these comments, to timely flag or issue a warning to users indulging in such behaviour. However, automated detection of such content is a challenging task, more so in Dravidian languages which are identified as low resource languages. Motivated by this, the paper attempts to explore applicability of different deep learning models for classification of the social media comments in Malayalam and Tamil languages as homophobic, transphobic and non-anti-LGBT+content. The popularly used deep learning models-Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) using GloVe embedding and transformer-based learning models (Multilingual BERT and Indic-BERT) are applied to the classification problem. Results obtained show that In-dicBERT outperforms the other implemented models, with obtained weighted average F1-score of 0.86 and 0.77 for Malayalam and Tamil, respectively. Therefore, the present work confirms higher performance of IndicBERT on the given task in selected Dravidian languages.

show abstract

Section: Related Workmentioning

confidence: 99%

Detection of Homophobia & Transphobia in Malayalam and Tamil: Exploring Deep Learning Methods

Sharma

Gupta

Singh

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…Different machine-learning classification models were applied to train the data. The set of classification algorithms that have been used are listed below 4 .…”

Section: Modelmentioning

confidence: 99%

BFCI at #SMM4H 2023: Integration of Machine Learning and TF-IDF for Covid-19 Tweets Analysis

Nayel,

Ashraf,

Aldawsari

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Extracting information from texts generated by users of social media platforms becomes a crucial task. In this paper, we describe the systems submitted to the SMM4H shared tasks 1 and 2. The aims of these two tasks are binary and multi-class classification of English tweets. We developed a machine learning-based model integrated with TF-IDF as a feature extraction approach. Four classification algorithms have been implemented namely, support vector machines, passive-aggressive classifier, multi-layer perceptron and random forest. For task 1, the passive-aggressive classifier reported f1-score of 63.7%. For task 2, multi-layer perceptron reported f1-score of 71.4%.

show abstract

“…The F1-scores for the Tamil datasets were 0.40, 0.85, and 0.89. Asraf et al [9] made models using algorithms for machine learning like Svm Algorithm, Random Forest, Passive Aggressive.. Just on data -sets provided as a part of the shared task LT-EDI-ACL2022 [10], they found that SVM was more accurate.…”

Section: Literature Surveymentioning

confidence: 99%

Detecting homophobic and transphobic texts from youtube comments using machine learning models

Subramanian,

Sathishkumar,

Shanmugavadivel

et al. 2023

ACE

View full text Add to dashboard Cite

Today's world has witnessed an exponential rise in disseminating degrading and offensive content via social media. A global increase in violence against minorities, such as gun violence, murders, and forced displacement, has been connected to using harsh and derogatory language online. The policies enacted to prevent abusive or derogatory language risk stifling free speech and are applied differently. These languages can affect the mental state of social media users. Homophobic and transphobic expressions are a insulting people's sexuality or character among harsh and disrespectful remarks. To study social media information and discriminate between homophobic and transphobic comments, it is necessary to construct language-based automatic categorization methods. This dataset consists of Tamil YouTube comments collected as part of the Shared Task on Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages. This study detects and categorises abusive comments using machine learning models such as SVM, Naive Bayes, Logistic regression, and KNN. In comparison to other classifiers, the accuracy of logistic regression is the highest, at 55%.

show abstract

NAYEL @LT-EDI-ACL2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM

Cited by 6 publications

References 2 publications

Detection of Homophobia & Transphobia in Malayalam and Tamil: Exploring Deep Learning Methods

Detection of Homophobia & Transphobia in Malayalam and Tamil: Exploring Deep Learning Methods

BFCI at #SMM4H 2023: Integration of Machine Learning and TF-IDF for Covid-19 Tweets Analysis

Detecting homophobic and transphobic texts from youtube comments using machine learning models

Contact Info

Product

Resources

About