Malta

In this modern era, many people have been using Facebook and Twitter, leading to a rise in information sharing and communication. However, a considerable amount of information on these platforms is misleading or intentionally crafted to deceive users, which is often termed &quot;Fake News&quot;. This shared task on Fake News Detection in Dravidian Languages (Task-1) organized by DravidianLangTech@EACL 2024 allows for addressing the challenge of distinguishing between original and fake news content in the Malayalam language. Our approach involves creating an intelligent model to categorize text as either fake or original. We experimented with various models, including Logistic Regression, Decision Tree, Random Forest, SVM, and SGD for machine learning; CNN, BiLSTM, and BiLSTM with attention to deep learning; and XLM-R and m-BERT for transformer-based approaches. Notably, our most successful model, m-BERT, achieved a top-ranking position with a macro F1 score of 0.85, ranked 4th among the participants. This research contributes to combating misinformation on social media news, offering an effective solution to classify content accurately.

EACL 2024

CUET_NLP_GoodFellows@DravidianLangTech 2024: A transformer-based approach for detecting Fake News in Dravidian Languages

malayalam

low-resourced

fake news

transformer

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.

In this modern era, many people have been using Facebook and Twitter, leading to a rise in information sharing and communication. However, a considerable amount of information on these platforms is misleading or intentionally crafted to deceive users, which is often termed "Fake News". This shared task on Fake News Detection in Dravidian Languages (Task-1) organized by DravidianLangTech@EACL 2024 allows for addressing the challenge of distinguishing between original and fake news content in the Malayalam language. Our approach involves creating an intelligent model to categorize text as either fake or original. We experimented with various models, including Logistic Regression, Decision Tree, Random Forest, SVM, and SGD for machine learning; CNN, BiLSTM, and BiLSTM with attention to deep learning; and XLM-R and m-BERT for transformer-based approaches. Notably, our most successful model, m-BERT, achieved a top-ranking position with a macro F1 score of 0.85, ranked 4th among the participants. This research contributes to combating misinformation on social media news, offering an effective solution to classify content accurately.

workshop paper

With the continuous evolution of technology and widespread internet access, various social media platforms have gained immense popularity, attracting a vast number of active users globally. However, this surge in online activity has also led to a concerning trend by driving many individuals to resort to posting hateful and offensive comments or posts, publicly targeting groups or individuals. In response to these challenges, we participated in this shared task. Our approach involved proposing a fine-tuning-based pre-trained transformer model to effectively discern whether a given text contains offensive content that propagates hatred. We conducted comprehensive experiments, exploring various machine learning (LR, SVM, and Ensemble), deep learning (CNN, BiLSTM, CNN+BiLSTM), and transformer models (indic-SBERT, mBERT, MuRIL, Distil-mBERT, XLMR), adhering to a meticulous fine-tuning methodology. Among the models evaluated, our fine-tuned L3Cube-Indic-Sentence-Similarity-BERT or Indic-SBERT model demonstrated superior performance, achieving a macro-average F1-score of 0.7013. This notable result positioned us at the 6th place in the task. The implementation details of the task will be found in the GitHub repository.

CUET_Binary_Hackers@DravidianLangTech@EACL 2024: Hate and Offensive Language Detection in Telugu Code-Mixed Text Using Sentence Similarity BERT

The ever-evolving landscape of online social media has initiated a transformative phase in communication, presenting unprecedented opportunities alongside inherent challenges. The pervasive issue of false information, commonly termed fake news, has emerged as a significant concern within these dynamic platforms. This study delves into the domain of Fake News Detection, with a specific focus on Malayalam. Utilizing advanced transformer models like mBERT, ALBERT, and XMLRoBERTa, our research proficiently classifies social media text into original or fake categories. Notably, our proposed model achieved commendable results, securing a rank of 3 in Task 1 with macro F1 scores of 0.84 using mBERT, 0.56 using ALBERT, and 0.84 using XMLRoBERTa. In Task 2, the XMLRoBERTa model excelled with a rank of 12, attaining a macro F1 score of 0.21, while mBERT and BERT achieved scores of 0.16 and 0.11, respectively. This research aims to develop robust systems capable of discerning authentic from deceptive content, a crucial endeavor in maintaining information reliability on social media platforms amid the rampant spread of misinformation.

TechWhiz@DravidianLangTech 2024: Fake News Detection Using Deep Learning Models

Sentiment analysis delves into people's opinions, intuitions, and emotions regarding any entity. Within natural language processing (NLP), it serves as a technique to extract subjective knowledge, determining whether an opinion or comment leans positive, negative, neutral, or a mix thereof towards an entity. In recent years, it has garnered substantial attention from NLP researchers due to the vast availability of comments and opinions on the internet. Despite extensive studies in this domain, sentiment analysis in low-resource languages such as Tamil and Tulu faces challenges in handling code-mixed and transliterated content. To address these challenges, this work focuses on sentiment analysis of code-mixed and transliterated Tamil and Tulu social media comments. It explores four machine learning approaches (LR, SVM, XGBoost, Ensemble), two deep learning methods (BiLSTM with fastText and Word2Vec), and four transformer-based models (m-BERT, MuRIL, L3Cube-IndicSBERT, and DistilmBERT) for both languages. For Tamil, L3Cube-IndicSBERT and ensemble approaches outperform others, while m-BERT demonstrates superior performance among the models for Tulu. The presented models achieved the third and first ranks by attaining macro F1-scores of 0.227 and 0.584 in Tamil and Tulu, respectively.

CUET_Binary_Hackers@DravidianLangTech-EACL 2024: Sentiment Analysis using Transformer-Based Models in Code-Mixed and Transliterated Tamil and Tulu

Abusive language is a major problem on social media, which highlights the need for efficient detection methods to be developed. This research addresses the formidable challenge of detecting abusive language in Tamil through a comprehensive multimodal approach, incorporating textual, acoustic, and visual inputs. In the domain of video data, our study utilizes ConvLSTM, 3DCNN, and a hybrid 3DCNN with BiLSTM. For audio data, models such as LSTM, BiLSTM, RNN, LR, GNB, and RF are explored. Regarding textual content, we investigate MNB, LR, RF, RNN, LSTM, and BiLSTM. To further enhance overall performance, we introduce a weighted late fusion model amalgamating predictions from all modalities. The fusion model is then applied to make predictions on our test dataset, yielding macro f1 score of 71.43%. Within the shared task "Multimodal Abusive Language Detection and Sentiment Analysis in Dravidian Languages: DravidianLangTech@EACL 2024," this methodology has achieved the highest ranking for detecting abusive language detection in the Tamil.

Binary_Beasts@DravidianLangTech@EACL 2024: Multimodal Abusive Language Detection in Tamil: An Integrated Approach using ML and DL Techniques

Sentiment Analysis of Dravidian Languages has begun to garner attention recently as there is more need to analyze emotional responses and subjective opinions present in social media text. As this data is code-mixed and there are not many solutions to code-mixed text out there, we present to you a stellar solution to DravidianLangTech@EACL 2024: Sentiment Analysis in Tamil and Tulu task. To understand the sentiment of social media text, we used pre trained transformer models and feature extraction vectorizers to classify the data with results that placed us 11th in the rankings for the Tamil task and 8th for the Tulu task with a macro F1 score of 0.074 and 0.251 which shows the efficiency of our approach.

WordWizardsDravidianLangTech@EACL 2024: Sentiment Analysis in Tamil and Tulu

The importance of being able to identify between fake and original news in the arena of social media demands surveillance procedures. This paper simply establishes the shared task on 'Fake News Detection in Dravidian Languages - DravidianLangTech@EACL 2024.' Focused on the Malayalam language, the task entails identifying social media posts as either fake or original news. The participating teams presented varied strategies, ranging from conventional machine-learning techniques to cutting-edge transformer-based models. Notably, our findings underscore the usefulness of the Malayalam-BERT model, demonstrating a noteworthy accuracy of 88% in detecting between fake and original news among Malayalam social media material.

CUET_DUO@DravidianLangTech 2024: Fake News Classification Using Malayalam-BERT

The main objective of the task is categorised into three subtasks. Subtask-1 Build models to determine the sentiment expressed in multimodal posts (or videos) in Tamil and Malayalam languages, leveraging textual, audio, and visual components. The videos are labelled into five categories: highly positive, positive, neutral, negative and highly negative. Subtask-2 Design machine models that effectively identify and classify abusive language within the multimodal context of social media posts in Tamil. The data are categorized into abusive and non-abusive categories. Subtask-3 Develop advanced models that accurately detect and categorize hate speech and offensive language in multimodal social media posts in Dravidian languages. The data points are categorized into Caste, Offensive, Racist and Sexist classes. In this session, the focus is primarily on Tamil language text data analysis. Various combination of machine learning models have been used to perform each tasks and do oversampling techniques to train models on biased dataset.

Wit Hub@DravidianLangTech-2024:Multimodal Social Media Data Analysis in Dravidian Languages using machine learning models

Social media reviews are utilized in sentiment analysis (SA), a computing area that analyses people's thoughts, and attitudes which has increased the need for efficient tools to analyze and determine feelings in such content. To tackle this challenge, our study introduces a system developed for the shared task of analyzing sentiment in Tamil and Tulu code-mixed languages. We utilized a combination of ML models (DT, RF, MNB, SVM), DL models (CNN, BiLSTM, CNN+BiLSTM with FastText), and transformer-based models (Indic-BERT, XLM-RoBERTa, m-BERT) for both Tamil and Tulu code-mixed language. Experimental outcomes reveal that the transformer-based models XLM-RoBERTa and m-BERT surpassed others in performance for Tamil and Tulu, respectively. The proposed XLM-RoBERTa and m-BERT models attained macro F1-scores of 0.258 and 0.468 for the Tamil and Tulu datasets, securing the 2nd and 5th positions, respectively, in the shared task competition.

CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based approach for Sentiment Analysis in Tamil and Tulu Code-Mixed Text

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.

Social Media Hate and Offensive Speech Detection Using Machine Learning method

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.

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Next from EACL 2024

CUET_Binary_Hackers@DravidianLangTech@EACL 2024: Hate and Offensive Language Detection in Telugu Code-Mixed Text Using Sentence Similarity BERT

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CUET_DUO@DravidianLangTech 2024: Fake News Classification Using Malayalam-BERT