Investigating Hostile Post Detection in Hindi

Bhatnagar, Varad; Kumar, Prince; Bhattacharyya, Pushpak

doi:10.1016/j.neucom.2021.11.096

Cited by 17 publications

(6 citation statements)

References 26 publications

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“…To solve the multi-level multi-class classification problem Kumar et al [5] The task was separated between coarse and fine grained tasks. And investigated methodologies such as transformer and multitask learning before employing classic statistical classifiers such as SVM and polynomial logistic regression.…”

Section: Related Workmentioning

confidence: 99%

Truculent Post Analysis for Hindi Text

Agarwal,

Sahu,

Singh

et al. 2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: With the rise of social media platforms, the prevalence of truculent posts has become a major concern. These posts, which exhibit anger, aggression, or rudeness, not only foster a hostile environment but also have the potential to stir up harm and violence. OBJECTIVES: It is essential to create efficient algorithms for detecting virulent posts so that they can recognise and delete such content from social media sites automatically. In order to improve accuracy and efficiency, this study evaluates the state-of-the-art in truculent post detection techniques and suggests a unique method that combines deep learning and natural language processing. The major goal of the proposed methodology is to successfully regulate hostile social media posts by keeping an eye on them. METHODS: In order to effectively identify the class labels and create a deep-learning method, we concentrated on comprehending the negation words, sarcasm, and irony using the LSTM model. We used multilingual BERT to produce precise word embedding and deliver semantic data. The phrases were also thoroughly tokenized, taking into consideration the Hindi language, thanks to the assistance of the Indic NLP library. RESULTS: The F1 scores for the various classes are given in the "Proposed approach” as follows: 84.22 for non-hostile, 49.26 for hostile, 68.69 for hatred, 49.81 for fake, and 39.92 for offensive CONCLUSION: We focused on understanding the negation words, sarcasm and irony using the LSTM model, to classify the class labels accurately and build a deep-learning strategy.

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Section: Related Workmentioning

confidence: 99%

Truculent Post Analysis for Hindi Text

Agarwal,

Sahu,

Singh

et al. 2024

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

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“…We studied various approaches used for hostile post detection in different languages. Bhatnagar et al [2] has developed an automated system that can automatically detect hostile posts in the Hindi language.…”

Section: Related Workmentioning

confidence: 99%

“…Gujarati is the sixth most spoken language in India, spoken by 56 million people [1]. During covid19 pandemic the use of social media platforms such as Twitter, Facebook, Instagram, linkedin, Reddit and YouTube has increased drastically [2] [3]. Social media platforms are now used for many purposes such as education, politics, entertainment, business, charity work etc.…”

Section: Introductionmentioning

confidence: 99%

“…According to the survey, there is an increase in the content of Indian language hostile posts on social media. The hostile post are identified in Indian languages like Hindi [2,3,[7][8][9][10], Marathi [8] [11][12][13], Bengali [9], Punjabi, Gujarati in addition to the English language [10]. Very less research work has been done in the low-resource Indian languages [6].…”

Section: Introductionmentioning

confidence: 99%

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Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Boda

2023

Preprint

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Hostile post on social media is a crucial issue for individual, government, and organizations. There is a critical need for an automated system that can investigate and identify hostile posts from large-scale data. In India, Gujarati is the sixth most spoken language. In this work, we have constructed a major hostile post dataset in the Gujarati language. The data are collected from Twitter, Instagram, and Facebook. Our dataset consists of 1,51,000 distinct tweets having 10,000 manually annotated posts. These posts are labeled into the Hostile and Non-Hostile categories. We have used the dataset in two ways: (i) Original Gujarati Text Data and (ii) Gujarati Data translated into English. We have also checked the performance of pre-processing and without pre-processing data by removing extra symbols and substituting emoji descriptions in the text. Finally, we have conducted experiments using machine learning models based on supervised learning such as Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive-Bayes, Logistic Regression, K-Nearest Neighbor and unsupervised learning based model such as k-means clustering. We have evaluated performance of these models for Bag-of-Words and TF-IDF feature extraction methods. It is observed that classification using TF-IDF features is efficient. Among these methods Logistic regression outperforms with an Accuracy of 0.68 and F1-score of 0.67.

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“…Gujarati is the sixth most spoken language in India, spoken by 56 million people 1 . 2 During covid19 pandemic the use of social media platforms such as Twitter, their opinion, suggestions and emotions very openly (3,4).…”

Section: Introductionmentioning

confidence: 99%

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Rameshbhai,

Rana

2024

IJE

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Hostile post on social media is a crucial issue for individuals, governments and organizations. There is a critical need for an automated system that can investigate and identify hostile posts from large-scale data. In India, Gujarati is the sixth most spoken language. In this work, we have constructed a major hostile post dataset in the Gujarati language. The data are collected from Twitter, Instagram and Facebook. Our dataset consists of 1,51,000 distinct comments having 10,000 manually annotated posts. These posts are labeled into the Hostile and Non-Hostile categories. We have used the dataset in two ways: (i) Original Gujarati Text Data and (ii) English data translated from Gujarati text. We have also checked the performance of pre-processing and without pre-processing data by removing extra symbols and substituting emoji descriptions in the text. We have conducted experiments using machine learning models based on supervised learning such as Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive-Bayes, Logistic Regression, K-Nearest Neighbor and unsupervised learning based model such as k-means clustering. We have evaluated performance of these models for Bag-of-Words and TF-IDF feature extraction methods. It is observed that classification using TF-IDF features is efficient. Among these methods Logistic regression outperforms with an Accuracy of 0.68 and F1-score of 0.67. The purpose of this research is to create a benchmark dataset and provide baseline results for detecting hostile posts in Gujarati Language.

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Investigating Hostile Post Detection in Hindi

Cited by 17 publications

References 26 publications

Truculent Post Analysis for Hindi Text

Truculent Post Analysis for Hindi Text

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

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