Malta

Even though the improper use of social me- dia is increasing nowadays, there is also tech- nology that brings solutions. Here, improper- ness is posting hate and offensive speech that might harm an individual or group. Hate speech refers to an insult toward an individual or group based on their identities. Spreading it on so- cial media platforms is a serious problem for society. The solution, on the other hand, is the availability of natural language process- ing(NLP) technology that is capable to detect and handle such problems. This paper presents the detection of social media’s hate and offen- sive speech in the code-mixed Telugu language. For this, the task and golden standard dataset were provided for us by the shared task orga- nizer(DravidianLangTech@EACL 2024). To this end, we have employed the TF-IDF tech- nique for numeric feature extraction and used a random forest algorithm for modeling hate speech detection. Finally, the developed model was evaluated on the test dataset and achieved 0.492 macro-F1.

EACL 2024

Social Media Hate and Offensive Speech Detection Using Machine Learning method

dravidean language

random forest

hate speech

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False news misleads people and may lead to real-world miscommunication and injury. Identifying fake news teaches society. Removing misinformation encourages critical thinking, democracy, and the prevention of hatred, fear, and misunderstanding. Identifying and removing fake news and developing a detection system is essential for reliable, accurate, and clear information. Therefore a shared task is organized to detect fake news in Malayalam. This paper presents a system developed for the shared task of detecting and classifying fake news in Malayalam. The approach involves a combination of machine learning models(DT, RF, MNB, SVM), deep learning models (CNN, BiLSTM, CNN+BiLSTM with FastText), and transformer-based models (Indic-BERT, indic-BERT, m-BERT) for both subtasks. The experimental results demonstrate that transformer-based models, specifically m-BERT and Malayalam-BERT, outperformed others. They achieved superior performance with noteworthy macro F1-scores of 0.84 and 0.496 for subtask 1 and subtask 2, respectively, securing the 5th and 2nd positions in the shared task competition.

CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based approach for Detecting and Categorizing Fake News in Malayalam Language

TThe rise of social media has facilitated easier communication, information sharing, and current affairs updates. However, the prevalence of misleading and deceptive content, commonly referred to as fake news, poses a significant challenge. This paper focuses on the classification of fake news in Malayalam, a Dravidian language, utilizing natural language processing (NLP) techniques. To develop a model, we employed a random forest machine learning method on a dataset provided by a shared task(DravidianLangTech@EACL 2024). When evaluated by the separate test dataset, our developed model achieved a 0.71 macro F1 measure.

Social Media Fake News Classification Using Machine Learning Algorithm

The use of deep learning algorithms has resulted in significant progress in automatic speech recognition (ASR). Robust high-accuracy ASR models typically require thousands or tens of thousands of hours of speech data, but even the strongest models tend fail under noisy conditions. Unsurprisingly, the impact of noise on accuracy is more drastic in low-resource settings. In this paper, we investigate the impact of noise on ASR in a low-resource setting. We explore novel methods for developing noise-robust ASR models using a a small dataset for Tamil, a widely-spoken but under-resourced Dravidian languages. We add various noises to the audio data to determine the impact of different kinds of noise (e.g., punctuated vs. constant, man-made vs natural) We also explore the relationship between different data augmentation methods are better suited to handling different types of noise. Our results show that all noises, regardless of the type, had an impact on ASR performance, and that upgrading the architecture alone could not mitigate the impact of noise. SpecAugment, the most common data augmentation method, was not as helpful as raw data augmentation, in which noise is explicitly added to the training data. Raw data augmentation enhances ASR performance on both clean data and noise-mixed data.

Exploring the impact of noise in low-resource ASR for Tam

Hate and offensive language detection is the task of detecting hate and/or offensive content targetting a person or a group of people. Despite many efforts to detect hate and offensive content on social media platforms, the problem remains unsolved till date due to the ever growing social media users and their creativity to create and spread hate and offensive content. To address the automatic detection of hate and offensive content on social media platforms, this paper describes the learning models submitted by our team - MUCS to "Hate and Offensive Language Detection in Telugu Codemixed Text (HOLD-Telugu): DravidianLangTech@EACL" - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024 invites the research community to address the challenges of detecting hate and offensive language in Telugu language. In this paper, we - team MUCS, describe the learning models submitted to the above mentioned shared task. Three models: Three models: i) LR model - a Machine Learning (ML) algorithm fed with TF-IDF of n-grams of subword, word and char_wb are in the range (1, 3), (1, 3), and (1, 5), ii) TL- a pretrained BERT models which makes use of Hate-speech-CNERG/bert-base-uncased-hatexplain model and iii) Ensemble model which is the combination of ML classifieres( MNB, LR, GNB) trained CountVectorizer with word and char ngrams of range (1, 3) and (1, 5) respectively. Proposed LR model trained with TF-IDF of subword, word and char n-grams outperformed the other models with macro F1 scores of 0.6501 securing 15th rank in the shared task for Telugu text.

MUCS@DravidianLangTech-2024: Role of Learning Approaches in Strengthening Hate-Alert Systems for Code-mixed Languages

Sentiment Analysis (SA) is a field of computational study that analyzes and understands people’s opinions, attitudes, and emotions toward any entity. A review of an entity can be written about an individual, an event, a topic, a product, etc., and such reviews are abundant on social media platforms. The increasing number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. In spite of this, SA of social media text is challenging because the code-mixed text is complex. To address SA in code-mixed Tamil and Tulu text, this paper describes the Machine Learning (ML) models submitted by our team - MUCS to "Sentiment Analysis in Tamil and Tulu - Dravidian- LangTech" - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024. Linear Support Vector classifier (LinearSVC) and ensemble of 5 ML classifiers (k Nearest Neighbour (kNN), Stochastic Gradient Descent (SGD), Logistic Regression (LR), LinearSVC, and Random Forest Classifier (RFC)) with hard voting trained using concatenated features obtained from word and character n-ngrams vectoized from Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and CountVectorizer. Further, Gridsearch algorithm is employed to obtain optimal hyperparameter values.The proposed ensemble model obtained macro F1 scores of 0.260 and 0.550 for Tamil and Tulu languages respectively.

MUCS@DravidianLangTech-2024: A Grid Search Approach to explore Sentiment Analysis in Code-mixed Tamil and Tulu

There is opportunity for machine learning and natural language processing research because of the growing volume of textual data. Although there has been little research done on trend extraction from YouTube comments, sentiment analysis is an intriguing issue because of the poor consistency and quality of the material found there. The purpose of this work is to use machine learning techniques and algorithms to do sentiment analysis on YouTube comments pertaining to popular themes. The findings demonstrate that sentiment analysis is capable of giving a clear picture of how actual events affect public opinion. This study aims to make it easier for academics to find high-quality sentiment analysis research publications. Data normalisation methods are used to clean an annotated corpus of 1500 citation sentences for the study. .For classification, a system utilising one machine learning algorithm—K-Nearest Neighbour (KNN), Na¨ıve Bayes, SVC (Support Vector Machine), and RandomForest—is built. Metrics like the F-score and correctness score are used to assess the correctness of the system.

InnovationEngineers@DravidianLangTech-EACL 2024: sentimental analysis of YouTube comments in Tamil by using machine learning

The proliferation of fake news in the Malayalam language across digital platforms has emerged as a pressing issue. By employing Recurrent Neural Networks (RNNs), a type of machine learning model, we aim to distinguish between Original and Fake News in Malayalam and achieved 9th rank in Task 1.RNNs are chosen for their ability to understand the sequence of words in a sentence, which is important in languages like Malayalam. Our main goal is to develop better models that can spot fake news effectively. We analyze various features to understand what contributes most to this accuracy. By doing so, we hope to provide a reliable method for identifying and combating fake news in the Malayalam language.

KEC HAWKS@DravidianLangTech-2024 : Detecting Malayalam Fake News using Machine Learning Models

Code-mixed languages are increasingly prevalent on social media and online platforms, presenting significant challenges in offensive content detection for natural language processing (NLP) systems. Our study explores how effectively the Sentence Transfer Fine-tuning (Set-Fit) method, combined with logistic regression, detects offensive content in a Tamil-English code-mixed dataset. We compare our model’s performance with five other NLP models: Multilingual BERT (mBERT), LSTM, BERT, IndicBERT, and Language-agnostic BERT Sentence Embeddings (LaBSE). Our model, SetFit, outperforms these models in accuracy, achieving an impressive 89.72%, significantly higher than other models. These results suggest the sentence transformer model’s substantial potential for detecting offensive content in codemixed languages. Our study provides valuable insights into the sentence transformer model’s ability to identify various types of offensive material in Tamil-English online conversations, paving the way for more advanced NLP systems tailored to code-mixed languages.

SetFit: A Robust Approach for Offensive Content Detection in Tamil-English Code-Mixed Conversations Using Sentence Transfer Fine-tuning

Sentiment Analysis (SA) in Dravidian codemixed text is a hot research area right now. In this regard, "Second Shared Task on SA in Code-mixed Tamil and Tulu" at European Chapter of the Association for Computational Linguistics (EACL-2024) is organized. Two tasks namely SA in Tamil-English and Tulu-English code-mixed data make up this shared assignment. In total 64 teams registered for the shared task out of which 19 and 17 systems were received for Tamil and Tulu languages respectively. The performance of the systems submitted by the participants were evaluated based on F1-score.The best method obtained macro average F1-Score of 0.260 and 0.584 for code-mixed Tamil and Tulu texts, respectively.

Overview of Second Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu

The rise of online social media has revolutionized communication, offering users a convenient way to share information and stay updated on current events. However, this surge in connectivity has also led to the proliferation of misinformation, commonly known as fake news. This misleading content, often disguised as legitimate news, poses a significant challenge as it can distort public perception and erode trust in reliable sources. This shared task consists of two subtasks such as task 1 and task 2. Task 1 aims to classify a given social media text into original or fake. The goal of the FakeDetect-Malayalam task2 is to encourage participants to develop effective models capable of accurately detecting and classifying fake news articles in the Malayalam language into different categories like False, Half True, Mostly False, Partly False, and Mostly True. For this shared task, 33 participants submitted their results.

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Next from EACL 2024

CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based approach for Detecting and Categorizing Fake News in Malayalam Language

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Robust Hate Speech Detection: A Cross-Dataset Empirical Comparison