Malta

Meme is a very popular term prevailing among almost all social media platforms in recent days. A meme can be a combination of text and image whose sole purpose is meant to be funny and entertain people. Memes can sometimes promote misogynistic content expressing hatred, contempt or prejudice against women. The Shared Task LT–EDI 2024: Multitask Meme classification – Unraveling Misogynistic and Trolls in Online Memes Task 1 was created with the purpose to classify social media memes as &quot;Misogynistic&quot; and &quot;Non - Misogynistic&quot;. The task encompassed Tamil and Malayalam datasets. We separately classified the textual data using Multinomial Naive Bayes and pictorial data using ResNet50 model. The results of from both data were combined to yield an overall result. We were ranked 2nd for both languages in this task.

EACL 2024

Quartet@LT-EDI 2024: A SVM-ResNet50 Approach For Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes

misogynistic memes

tf-idf vectorizer

naive bayes

resnet50

multimodal

transfer learning

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Meme is a very popular term prevailing among almost all social media platforms in recent days. A meme can be a combination of text and image whose sole purpose is meant to be funny and entertain people. Memes can sometimes promote misogynistic content expressing hatred, contempt or prejudice against women. The Shared Task LT–EDI 2024: Multitask Meme classification – Unraveling Misogynistic and Trolls in Online Memes Task 1 was created with the purpose to classify social media memes as "Misogynistic" and "Non - Misogynistic". The task encompassed Tamil and Malayalam datasets. We separately classified the textual data using Multinomial Naive Bayes and pictorial data using ResNet50 model. The results of from both data were combined to yield an overall result. We were ranked 2nd for both languages in this task.

workshop paper

Homophobia and transphobia are terms which are used to describe the fear or hatred towards people who are attracted to the same sex or people whose psychological gender differs from his biological sex. People use social media to exert this behaviour. The increased amount of abusive content negatively affects people in a lot of ways. It makes the environment toxic and unpleasant to LGBTQ+ people. The paper talks about the classification model for classifying the contents into 3 categories which are homophobic, transphobic and non-homophobic/transphobic. We used many traditional models like Support Vector Machine, Random Classifier, Logistic Regression and K- Nearest Neighbour to achieve this. The macro average F1 scores for Malayalam, Telugu, English, Marathi, Kannada, Tamil, Gujarati, Hindi are 0.88, 0.94, 0.96, 0.78, 0.93, 0.77, 0.94, 0.47 and the rank for these languages are 5, 6, 9, 6, 8, 6, 6, 4.

Quartet@LT-EDI 2024: Support Vector Machine based approach for Homophobia/Transphobia Detection in social media comments

This paper presents our submission for the shared task on Caste/Immigration Hate Speech Detection: LT-EDI@EACL 2024. This text classification task aims to foster the creation of models capable of identifying hate speech related to caste and immigration. The dataset comprises of social media comments, and the goal is to categorise them into negative and positive sentiments. Our approach explores back-translation for data augmentation to address sparse datasets in low-resource Dravidian languages. While Part-of-Speech (POS) tagging is valuable in natural language processing, our work highlights its ineffectiveness in Dravidian languages, with model performance drastically reducing from 0.73 to 0.67 on application. In analysing boosting and ensemble methods, the voting classifier with traditional models outperforms others and the boosting techniques, underscoring the efficacy of simpler models on low-resource data despite augmentation.

SSN-Nova@LT-EDI 2024: POS Tagging, Boosting Techniques and Voting Classifiers for Caste/Immigration Hate Speech Detection

The widespread use of online communication has caused a significant increase in the spread of hate speech on social media. However, there are also hate crimes based on caste or immigration status. Despite India's efforts to bring equality among its citizens, numerous crimes occur just based on caste. Immigration-based hostility happens both in India and in developed countries. A shared task was arranged to address this issue in a low-resource language such as Tamil. This paper aims to improve the detection of hate speech and hostility based on caste and immigration status on social media. To achieve this, we investigated several Machine Learning (ML), Deep Learning (DL), and transformer-based models, including M-BERT, XLM-R, and Tamil BERT. We obtained a macro -score of 0.80 using the M-BERT model, which enabled us to rank on the shared task.

CUET_NLP_Manning_LT-EDI@EACL2024: Transformer Based Approach on Caste or Immigration Hate Speech Detection

In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team Transformers' submission to the Caste/Immigration Hate Speech Detection in Tamil shared task by LT-EDI 2024 workshop at EACL 2024. We used an ensemble approach in the shared task, combining various transformer-based pre-trained models using majority voting. The best macro average F1-score achieved was 0.82. We secured the 1st rank in the Caste/Immigration Hate Speech in Tamil shared task.

Transformers@LT-EDI-EACL2024: Caste/Immigration Hate Speech Detection in Tamil Using Ensembling on Transformers

In this paper, we are discussing the submission we have made for the shared task on “Speech recognition for vulnerable individuals in Tamil” in LT-EDI in the year 2024. In this , we have tried to develop an automatic speech recognition system in the Tamil language. The dataset used was the audio collected from vulnerable individuals such as elderly men and women and transgenders in Tamil. The automatic speech recognition system is designed with pre- trained model Rajaram1996/wav2vec2-large-xlsr-53-tamil. The pretrained model is finetuned with common tamil voice dataset. Now the test dataset is fed into this system and the transcriptions obtained are submitted. The evaluation metric is Word Error Rate(WER). Our system has a WER of 37.733.

DRAVIDIAN LANGUAGE@ LT-EDI 2024:Pretrained Transformer based Automatic Speech Recognition system for Elderly People

Caste and Immigration speech refers to the use of language that distinguishes the offense, violence, and distresses on their social, caste, and immigration status. Here, caste hate speech targets the imbalance of an individual’s social status and focuses mainly on the degradation of their caste group. While the immigration hate speech imposes the differences in nationality, culture, and individual status. These speeches are meant to affront the social status of these people. To detect this hate in the speech, our task on Caste/Immigration Hate Speech Detection has been created which classifies human speech into genuine or stimulate categories. For this task, we used multiple classification models such as the train test split model to split the dataset into train and test data, Logistic regression, Support Vector Machine, MLP (Multi-layer Perceptron) classifier, Random Forest classifier, KNN classifier, and Decision tree classification. Among these models, The SVM gave the highest macro average F1 score of 0.77 and the average accuracy for these models is around 0.75

Algorithm Alliance LT-EDI-2024: Caste/Immigration Hate Speech Detection

Detection of Homophobia and Transphobia in social media comments serves as an important step in the overall development of Equality, Diversity and Inclusion (EDI). In this research, we describe the system we formulated while participating in the shared task of Homophobia/Transphobia detection as a part of the Fourth Workshop On Language Technology For Equality, Diversity, Inclusion (LT-EDI-2024) at EACL 2024. We used an ensemble of three state-of-the-art multilingual transformer models, namely Multilingual BERT (mBERT), Multilingual Representations for Indic Languages (MURIL) and XLM-RoBERTa to detect the presence of Homophobia or Transphobia in YouTube comments. The task comprised of datasets in ten languages - Hindi, English, Telugu, Tamil, Malayalam, Kannada, Gujarati, Marathi, Spanish and Tulu. Our system achieved rank 1 for the Spanish and Tulu tasks, 2 for Telugu, 3 for Marathi and Gujarati, 4 for Tamil, 5 for Hindi and Kannada, 6 for English and 8 for Malayalam. These results speak for the efficacy of our ensemble model as well as the data augmentation strategy we adopted for the detection of anti-LGBT+ language in social media data.

MEnTr@LT-EDI-2024: Multilingual Ensemble of Transformer Models for Homophobia/Transphobia Detection

Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-f1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.

dkit@LT-EDI-2024: Detecting Homophobia and Transphobia in English Social Media Comments

The pervasive impact of stress on individuals necessitates proactive identification and intervention measures, especially in social media interaction. This research paper addresses the imperative need for proactive identification and intervention concerning the widespread influence of stress on individuals. This study focuses on the shared task, "Stress Identification in Dravidian Languages," specifically emphasizing Tamil and Telugu code-mixed languages. The primary objective of the task is to classify social media messages into two categories: stressed and non stressed. We employed various methodologies, from traditional machine-learning techniques to state-of-the-art transformer-based models. Notably, the Tamil-BERT and Telugu-BERT models exhibited exceptional performance, achieving a noteworthy macro F1-score of \textbf{0.71} and \textbf{0.72}, respectively, and securing the position in Tamil code-mixed language and the position in the Telugu code-mixed language. These findings underscore the effectiveness of these models in recognizing stress signals within social media content composed in Tamil and Telugu.

CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT

Identifying an individual where he/she is stressed or not stressed is our shared task topic. we have used several machine learning models for identifying the stress. This paper presents our system submission for the task 1 and 2 for both Tamil and Telugu dataset, focusing on us- ing supervised approaches. For Tamil dataset, we got highest accuracy for the Support Vector Machine model with f1-score of 0.98 and for Telugu dataset, we got highest accuracy for Random Forest algorithm with f1-score of 0.99. By using this model, Stress Identification System will be helpful for an individual to improve their mental health in optimistic manner.

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Next from EACL 2024

Quartet@LT-EDI 2024: Support Vector Machine based approach for Homophobia/Transphobia Detection in social media comments

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MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification