Abusive language detection in user-generated online content has become a pressing concern due to its negative impact on users and challenges for policy makers. Online platforms are faced with the task of moderating abusive content to mitigate societal harm, adhere to legal requirements, and foster inclusivity. Despite numerous methods developed for automated detection of abusive language, the problem continues to persist. This ongoing challenge necessitates further research and development to enhance the effectiveness of abusive content detection systems and implement proactive measures to create safer and more respectful online spaces. To address the automatic detection of abusive languages in social media platforms, this paper describes the models submitted by our team - MUCS to the shared task “Abusive Comment Detection in Tamil and Telugu” at DravidianLangTech - in Recent Advances in Natural Language Processing (RANLP) 2023. This shared task addresses the abusive comment detection in code-mixed Tamil, Telugu, and romanized Tamil (Tamil-English) texts. Two distinct models: i) AbusiveML - a model implemented utilizing Linear Support Vector Classifier (LinearSVC) algorithm fed with n-grams of words and character sequences within word boundary (char_wb) features and ii) AbusiveTL - a Transfer Learning (TL ) model with three different Bidirectional Encoder Representations from Transformers (BERT) models along with random oversampling to deal with data imbalance, are submitted to the shared task for detecting abusive language in the given code-mixed texts. The AbusiveTL model fared well among these two models, with macro F1 scores of 0.46, 0.74, and 0.49 for code-mixed Tamil, Telugu, and Tamil-English texts respectively.

Sentiment Analysis (SA) examines the subjective content of a statement, such as opinions, assessments, feelings, or attitudes towards a subject, person, or a thing. Though several models are developed for SA in high-resource languages like English, Spanish, German, etc., uder-resourced languages like Dravidian languages are less explored. To address the challenges of SA in low resource Dravidian languages, in this paper, we team MUNLP describe the models submitted to “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)-2023. n-gramsSA, EmbeddingsSA and BERTSA are the models proposed for SA shared task. Among all the models, BERTSA exhibited a maximum macro F1 score of 0.26 for code-mixed Tamil texts securing 2nd place in the shared task. EmbeddingsSA exhibited maximum macro F1 score of 0.53 securing 2nd place for Tulu code-mixed texts.

User-generated social media texts are a blend of resource-rich languages like English and low-resource Dravidian languages like Tamil, Kannada, Tulu, etc. These texts referred to as code-mixing texts are enriching social media since they are written in two or more languages using either a common language script or various language scripts. However, due to the complex nature of the code-mixed text, in this paper, we - team MUCSD, describe a Machine learning (ML) models submitted to “Sentiment Analysis in Tamil and Tulu” shared task at DravidianLangTech@RANLP 2023. The proposed methodology makes use of ML models such as Linear Support Vector Classifier (LinearSVC), LR, and ensemble model (LR, DT, and SVM) to perform SA in Tamil and Tulu languages. The proposed LinearSVC model’s predictions submitted to the shared tasks, obtained 8th and 9th rank for Tamil-English and Tulu-English respectively.

Social media is widely used to spread fake news, which affects a larger population. So it is considered as a very important task to detect fake news spread on social media platforms. To address the challenges in the identification of fake news in the Malayalam language, in this paper, we - team MUCS, describe the Machine Learning (ML) models submitted to “Fake News Detection in Dravidian Languages” at DravidianLangTech@RANLP 2023 shared task. Three different models, namely, Multinomial Naive Bayes (MNB), Logistic Regression (LR), and Ensemble model (MNB, LR, and SVM) are trained using Term Frequency - Inverse Document Frequency (TF-IDF) of word unigrams. Among the three models ensemble model performed better with a macro F1-score of 0.83 and placed 3rd rank in the shared task.

Hope plays a significant role in shaping human thoughts and actions and hope content has received limited attention in the realm of social media data analysis. The exploration of hope content helps to uncover the valuable insights into users’ aspirations, expectations, and emotional states. By delving into the analysis of hope content on social media platforms, researchers and analysts can gain a deeper understanding of how hope influences individuals’ behaviors, decisions, and overall well-being in the digital age. However, this area is rarely explored even for resource-high languages. To address the identification of hope text in social media platforms, this paper describes the models submitted by the team MUCS to “Hope Speech Detection for Equality, Diversity, and Inclusion (LT-EDI)” shared task organized at Recent Advances in Natural Language Processing (RANLP) - 2023. This shared task aims to classify a comment/post in English and code-mixed texts in three languages, namely, Bulgarian, Spanish, and Hindi into one of the two predefined categories, namely, “Hope speech” and “Non Hope speech”. Two models, namely: i) Hope_BERT - Linear Support Vector Classifier (LinearSVC) model trained by combining Bidirectional Encoder Representations from Transformers (BERT) embeddings and Term Frequency-Inverse Document Frequency (TF-IDF) of character n-grams with word boundary (char_wb) for English and ii) Hope_mBERT - LinearSVC model trained by combining Multilingual BERT (mBERT) embeddings and TF-IDF of char_wb for Bulgarian, Spanish, and Hindi code-mixed texts are proposed for the shared task to classify the given text into Hope or Non-Hope categories. The proposed models obtained 1st, 1st, 2nd, and 5th ranks for Spanish, Bulgarian, Hindi, and English texts respectively.

Homophobic/Transphobic (H/T) content includes hate speech, discrimination text, and abusive comments against Gay, Lesbian, Bisexual, Transgender, Queer, and Intersex (LGBTQ) individuals. With the increase in user generated text in social media, there has been an increase in code-mixed H/T content, which poses challenges for efficient analysis and detection of H/T content on social media. The complex nature of code-mixed text necessitates the development of advanced tools and techniques to effectively tackle this issue in social media platforms. To tackle this issue, in this paper, we - team MUCS, describe the transformer based models submitted to “Homophobia/Transphobia Detection in social media comments” shared task in Language Technology for Equality, Diversity and Inclusion (LT-EDI) at Recent Advances in Natural Language Processing (RANLP)-2023. The proposed methodology makes use of resampling the training data to handle the data imbalance and this resampled data is used to fine-tune the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) models. These models obtained 11th, 5th, 3rd, 3rd, and 7th ranks for English, Tamil, Malayalam, Spanish, and Hindi respectively in Task A and 8th, 2nd, and 2nd ranks for English, Tamil, and Malayalam respectively in Task B.

Depression can lead to significant changes in individuals’ posts on social media which is a important task to identify. Automated techniques must be created for the identification task as manually analyzing the growing volume of social media data is time-consuming. To address the signs of depression posts on social media, in this paper, we - team MUCS, describe a Transfer Learning (TL) model and Machine Learning (ML) models submitted to “Detecting Signs of Depression from Social Media Text” shared task organised by DepSign-LT-EDI@RANLP-2023. The TL model is trained using raw text Bidirectional Encoder Representations from Transformers (BERT) and the ML model is trained using Term Frequency-Inverse Document Frequency (TF-IDF) features separately. Among these three models, the TL model performed better with a macro averaged F1-score of 0.361 and placed 20th rank in the shared task.

Emotion Analysis (EA) is the process of automatically analyzing and categorizing the input text into one of the predefined sets of emotions. In recent years, people have turned to social media to express their emotions, opinions or feelings about news, movies, products, services, and so on. These users’ emotions may help the public, governments, business organizations, film producers, and others in devising strategies, making decisions, and so on. The increasing number of social media users and the increasing amount of user generated text containing emotions on social media demands automated tools for the analysis of such data as handling this data manually is labor intensive and error prone. Further, the characteristics of social media data makes the EA challenging. Most of the EA research works have focused on English language leaving several Indian languages including Tamil unexplored for this task. To address the challenges of EA in Tamil texts, in this paper, we - team MUCS, describe the model submitted to the shared task on Emotion Analysis in Tamil at DravidianLangTech@ACL 2022. Out of the two subtasks in this shared task, our team submitted the model only for Task a. The proposed model comprises of an Ensemble of Logistic Regression (LR) classifiers with three penalties, namely: L1, L2, and Elasticnet. This Ensemble model trained with Term Frequency - Inverse Document Frequency (TF-IDF) of character bigrams and trigrams secured 4th rank in Task a with a macro averaged F1-score of 0.04. The code to reproduce the proposed models is available in github1.

Social media has seen enormous growth in its users recently and knowingly or unknowingly the behavior of a person will be reflected in the comments she/he posts on social media. Users having the sign of depression may post negative or disturbing content seeking the attention of other users. Hence, social media data can be analysed to check whether the users’ have the sign of depression and help them to get through the situation if required. However, as analyzing the increasing amount of social media data manually in laborious and error-prone, automated tools have to be developed for the same. To address the issue of detecting the sign of depression content on social media, in this paper, we - team MUCS, describe an Ensemble of Machine Learning (ML) models and a Transfer Learning (TL) model submitted to “Detecting Signs of Depression from Social Media Text-LT-EDI@ACL 2022” (DepSign-LT-EDI@ACL-2022) shared task at Association for Computational Linguistics (ACL) 2022. Both frequency and text based features are used to train an Ensemble model and Bidirectional Encoder Representations from Transformers (BERT) fine-tuned with raw text is used to train the TL model. Among the two models, the TL model performed better with a macro averaged F-score of 0.479 and placed 18th rank in the shared task. The code to reproduce the proposed models is available in github page1.

Sentiment Analysis (SA) employing code-mixed data from social media helps in getting insights to the data and decision making for various applications. One such application is to analyze users’ emotions from comments of videos on YouTube. Social media comments do not adhere to the grammatical norms of any language and they often comprise a mix of languages and scripts. The lack of annotated code-mixed data for SA in a low-resource language like Tulu makes the SA a challenging task. To address the lack of annotated code-mixed Tulu data for SA, a gold standard trlingual code-mixed Tulu annotated corpus of 7,171 YouTube comments is created. Further, Machine Learning (ML) algorithms are employed as baseline models to evaluate the developed dataset and the performance of the ML algorithms are found to be encouraging.

Due to the rapid rise of social networks and micro-blogging websites, communication between people from different religion, caste, creed, cultural and psychological backgrounds has become more direct leading to the increase in cyber conflicts between people. This in turn has given rise to more and more hate speech and usage of abusive words to the point that it has become a serious problem creating negative impacts on the society. As a result, it is imperative to identify and filter such content on social media to prevent its further spread and the damage it is going to cause. Further, filtering such huge data requires automated tools since doing it manually is labor intensive and error prone. Added to this is the complex code-mixed and multi-scripted nature of social media text. To address the challenges of abusive content detection on social media, in this paper, we, team MUM, propose Machine Learning (ML) and Deep Learning (DL) models submitted to Multilingual Gender Biased and Communal Language Identification (ComMA@ICON) shared task at International Conference on Natural Language Processing (ICON) 2021. Word uni-grams, char n-grams, and emoji vectors are combined as features to train a ML Elastic-net regression model and multi-lingual Bidirectional Encoder Representations from Transformers (mBERT) is fine-tuned for a DL model. Out of the two, fine-tuned mBERT model performed better with an instance-F1 score of 0.326, 0.390, 0.343, 0.359 for Meitei, Bangla, Hindi, Multilingual texts respectively.