Malta

In the digital age, identifying fake news is essential when fake information travels quickly via social media platforms. This project employs machine learning techniques, including Random Forest, Logistic Regression, and Decision Tree, to distinguish between real and fake news. With the rise of news consumption on social media, it becomes essential to authenticate information shared on platforms like YouTube comments. The research emphasizes the need to stop spreading harmful rumors and focuses on authenticating news articles. The proposed model utilizes machine learning and natural language processing, specifically Support Vector Machines, to aggregate and determine the authenticity of news. To address the challenges of detecting fake news in this paper, describe the Machine Learning (ML) models submitted to ‘Fake News Detection in Dravidian Languages” at DravidianLangTech@EACL 2024 shared task. Four different models, namely: Naive Bayes, Support Vector Machine (SVM), Random forest, and Decision tree.

EACL 2024

Beyond Tech@DravidianLangTech : Fake News Detection in Dravidian Languages Using Machine Learning

random forest.

navie bayers

meachine learning

data pre-processing

decision tree

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.

workshop paper

The importance of sentiment analysis in online community research is growing. These technologies only looked at single utterances and were created for high-resource languages until recently. Nonetheless, multilingual communities' social media platforms have seen an increase in code-mixed writing. Our research focuses on sentiment analysis in texts with mixed Tamil and English codes. supervised learning techniques using annotated data outperform features based on lexical attributes, such as word dictionaries and parts of speech tagging. Our project's decision tree technique is essential since it makes it possible to classify sentiments in the code-mixed Tamil-English.

Code_Makers@DravidianLangTech-EACL 2024 : Sentiment Analysis in Code-Mixed Tamil using Machine Learning Techniques

Hateful online content is a growing concern, especially for young people. While social media platforms aim to connect us, they can also become breeding grounds for negativity and harmful language. This study tackles this issue by proposing a novel framework called HOLD-Z, specifically designed to detect hate and offensive comments in Telugu-English code-mixed social media content. HOLD-Z leverages a combination of approaches, including three powerful models: LSTM architecture, Zypher, and openchat_3.5. The study highlights the effectiveness of prompt engineering and Quantized Low-Rank Adaptation (QLoRA) in boosting performance. Notably, HOLD-Z secured the 9th place in the prestigious HOLD-Telugu DravidianLangTech@EACL-2024 shared task, showcasing its potential for tackling the complexities of hate and offensive comment classification.

IIITDWD-zk@DravidianLangTech-2024: Leveraging the Power of Language Models for Hate Speech Detection in Telugu-English Code-Mixed Text

This study presents a strong methodology for detecting offensive content in multilingual text, with a focus on Kannada and Kannada-English mixed comments. The first step in data pre- processing is to work with a dataset containing Kannada comments, which is backed by Google Translate for Kannada-English translation. Following tokenization and sequence labeling, BIO tags are assigned to indicate the existence and bounds of objectionable spans within the text. On annotated data, a Bidirectional LSTM neural network model is trained and achieves an F1 score of 61.0 in recognizing objectionable content. Data preparation, model architecture definition, and iterative training with Kannada and Kannada-English text are all part of the training process. In a fresh dataset, the trained model accurately predicts offensive spans, emphasizing comments in the aforementioned languages. Predictions that have been recorded and include offensive span indices are organized into a database.

Offensive Content Detection in Kannada and Kannada-English Mixed Text Using Deep Learning Techniques

Sentiment analysis has been an active field of research for over 20 years and has gained immense popularity due to its applications in both academia and industry. Sentiment Analysis of code-mixed posts and comments on social media, especially in Dravidian languages, is gaining more and more traction. This paper describes the team Transformers' submission to the Sentiment Analysis in Tamil shared task organized by DravidianLangTech 2024 workshop at EACL 2024. A BERT-based architecture, RoBERTa was used for the shared task. The best macro average F1-score achieved was 0.212. We secured the 5th rank in the Sentiment Analysis shared task in Tamil.

Transformers@DravidianLangTech-EACL2024: Sentiment Analysis of Code-Mixed Tamil Using RoBERTa

This research tackles the issue of fake news by utilizing the RNN-LSTM deep learning method with optimized hyperparameters identified through grid search. The model's performance in multi-label classification is hindered by unbalanced data, despite its success in binary classification. We achieved a score of 0.82 in the binary classification task, whereas in the multi-class task, the score was 0.32. We suggest incorporating data balancing techniques for researchers who aim to further this task, aiming to improve results in managing a variety of information.

Detecting Fake News Detection in Dravidian Languages using Deep Learning

The proliferation of fake news in digital media has become a significant societal concern, impacting public opinion, trust, and decision-making. This project focuses on the development of machine learning models for the detection of fake news. Leveraging a dataset containing both genuine and deceptive news articles, the proposed models employ natural language processing techniques, feature extraction and classification algorithms. This paper provides a solution to Fake News Detection in Dravidian Languages - DravidianLangTech@EACL 2024. There are two sub tasks: Task 1 -The goal of this task is to classify a given social media text into original or fake. We propose an approach for this with the help of a supervised machine learning model – SVM (Support Vector Machine). The SVM classifier achieved a macro F1 score of 0.78 in test data and a rank 11. The Task 2 is classifying fake news articles in Malayalam language into different categories namely False, Half True, Mostly False, Partly False and Mostly True.We have used Naive Bayes which achieved macro f1-score 0.3517 in test data and a rank 6.

Wordwizard Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024

Hate and offensive language in online platforms pose significant challenges, necessitating automatic detection methods. Particularly in the case of codemixed text, which is very common in social media, the complexity of this problem increases due to the cultural nuances of different languages. A shared task on the detection of hate and offensive language for Telugu was organized by DravidianLangTech. To complete this task, our study investigates the effectiveness of transliteration-augmented datasets for Telugu codemixed text. We compare the performance of various machine learning, deep learning, and transformer-based models on both original and augmented datasets. Our findings demonstrate the superiority of transformer models, particularly Telugu-BERT, which achieved an $f_1$ score of 0.77 on the augmented dataset, ranking the $1^{st}$ position in the leaderboard. The study highlights the potential of transliteration-augmented datasets in improving model performance and suggests further exploration of diverse transliteration options to address real-world scenarios.

Sandalphon@DravidianLangTech-EACL2024: Hate and Offensive Language Detection in Telugu Codemixed Text using Transliteration-Augmentation

Due to technological advancements, various methods have emerged for disseminating news to the masses. The pervasive reach of news, however, has given rise to a significant concern - the proliferation of fake news. In response to this challenge, a shared task in DravidianLangTech@EACL 2024 was initiated to detect fake news and classify its types in the Malayalam language. The shared task consisted of two sub-tasks. Task-1 focused on a binary classification problem, determining whether a piece of news is fake or not. Whereas task-2 delved into a multi-class classification problem, categorizing news into five distinct levels. Our approach involved the exploration of various machine learning, deep learning, and transformer models, with an emphasis on parameter tuning to enhance overall model performance. As a result, we introduce a MuRIL model that leverages parameter tuning, achieving notable success with an F1-score of 0.86 in task-1 and 0.5191 in task-2. This successful implementation led to our system securing the 3rd position in task-1 and the 1st position in task-2. The source code will be found in the GitHub repository provided in this link: https://github.com/Salman1804102/DravidianLangTech-EACL-2024-FakeNews.

CUET_Binary_Hackers@DravidianLangTech@EACL 2024: Malayalam Fake News Detection by Fine-Tuned MuRIL BERT

Fake news on social media is increasingly used to target and manipulate public opinion, influencing the decisions of a large number of people beyond mere misinformation. Researchers are increasingly interested in verifying news legitimacy due to the difficulty and cost of manual verification, especially with the increasing use of social media and internet access. Unfortunately, research on low-resource languages like Malayalam remains negligible due to a lack of resources and language processing tools. To address the issue the paper describes the submission of the team Punny Punctuators to the shared task on Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024. The shared task consists of two sub-tasks: (i) the first subtask classifies a given social media text as original or fake, and (ii) the second subtask classifies fake news into different categories. We used many transformer-based, deep-learning, and machine-learning models. The Malayalam-BERT model performs better than the other methods in subtask-1 and subtask-2, placing it 2nd with a macro F1-score of 0.87 and 11th with a macro F1-score of 0.17, respectively.

Punny_Punctuators@DravidianLangTech-EACL2024: Detection and Classification of Fake News for Malayalam Social-Media Text

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.

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Next from EACL 2024

Code_Makers@DravidianLangTech-EACL 2024 : Sentiment Analysis in Code-Mixed Tamil using Machine Learning Techniques

Similar lecture

Wordwizard Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024