Faculty Publications
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Item Health Fear Mongering Make People More Sicker: Twitter Analysis in the Context of Corona Virus Infection(Springer Science and Business Media Deutschland GmbH, 2020) Jayan, J.; Alathur, S.The purpose of this study is to assess the fear factor in Social media data in the context of Coronavirus Disease - 2019(COVID-19) across the globe. The fear generated from social media content will adversely affect the mental health of the public. Design/methodology/approach: The study is followed by a literature survey during the emergence of social media and Internet technologies since the year 2006 where the people commonly started to use the internet across the world. The Twitter data collected on COVID-19 during the infection period and the analysis. Findings: The social media contents adversely affect the mental health of the common public and also the healthcare programs run by the government organizations to some extent. The findings show that the social media are the major source of fear-mongering information and the people behind the fear-mongering are making use of the disaster situation to set their agenda. The strict enactment of law and the efforts by the social media platforms can reduce the fake news and misinformation. Research limitations/implications: The research focuses only on the Twitter data for the analysis during the COVID-19 distress. The detailed study needs to be done in similar distress situations across the globe. The data retrieval became limited from different social media platforms because of privacy issues. © 2020, IFIP International Federation for Information Processing.Item NITK_NLP at CheckThat! 2021: Ensemble transformer model for fake news classification(CEUR-WS, 2021) LekshmiAmmal, R.L.; Anand Kumar, M.Social media has become an inevitable part of our life as we are primarily dependent on them to get most of the news around us. However, the amount of false information propagated through it is much higher than the genuine ones, thus becoming a peril to society. In this paper, we have proposed a model for Fake News Classification as a part of CLEF2021 Checkthat! Lab1 shared task, which had Multi-class Fake News Detection and Topical Domain Classification of News Articles. We have used an ensemble model consisting of pre-trained transformer-based models that helped us achieve 4tâ„Ž and 1st positions on the leaderboard of the two tasks. We achieved an F1-score of 0.4483 against a top score of 0.8376 in one task and a score of 0.8813 in another. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Item NITK-IT NLP at CheckThat! 2022: Window based approach for Fake News Detection using transformers(CEUR-WS, 2022) LekshmiAmmal, H.R.; Anand Kumar, A.M.Misinformation is a severe threat to society which mainly spreads through online social media. The amount of misinformation generated and propagated is much more than authentic news. In this paper, we have proposed a model for the shared task on Fake News Classification by CLEF2022 CheckThat! Lab1, which had mono-lingual Multi-class Fake News Detection in English and cross-lingual task for English and German. We employed a transformer-based model with overlapping window strides, which helped us to achieve 7th and 2nd positions out of 25 and 8 participants on the final leaderboard of the two tasks respectively. We got an F1 score of 0.2980 and 0.2245 against the top score of 0.3391 and 0.2898 for the two tasks. © 2022 Copyright for this paper by its authors.Item Fake News Detection in Hindi Using Embedding Techniques(Institute of Electrical and Electronics Engineers Inc., 2022) Shailendra, P.; Rashmi, M.; Ramu, S.; Guddeti, R.M.R.Internet users have been rapidly increasing in recent years, especially in India. That is why nearly everything operates in an online mode. Sharing information has also become simple and easy due to the internet and social media. Almost everyone now shares news in the community without even considering the source of information. As a result, there is the issue of disseminating false, misleading, or fabricated data. Detecting fake news is a challenging task because it is presented in such a form that it looks like authentic information. This problem becomes more challenging when it comes to local languages. This paper discusses several deep learning models that utilize LSTM, BiLSTM, CNN+LSTM, and CNN+BiLSTM. On the Hostility detection dataset in Hindi, these models use Word2Vec, IndicNLP fastText, and Facebook's fastText embeddings for fake news detection. The proposed CNN+BiLSTM model with Facebook's fastText embedding achieved an F1-score of 75%, outperforming the baseline model. Additionally, the BiLSTM using Facebook's fastText outperforms CNN+BiLSTM using Facebook's fastText on the F1-score. © 2022 IEEE.Item Ontology for Contextual Fake News Assessment Based on Text and Images(Institute of Electrical and Electronics Engineers Inc., 2024) Chandrasekaran, K.; Kandasamy, A.; Venkatesan, M.; Prabhavathy, P.; Gokuldhev, M.; Aishwarya, C.The spread of false news on social networks is a major challenge in the digital age across various sectors, encompassing technology, politics, public health, and finance. This paper introduces an ontology-based method that combines text and image analysis to evaluate the accuracy of news stories in the context of social media. We investigate the role of social engineering tactics in crafting and dispersing fake news and advocate for a comprehensive multi-contextual perspective that covers content, source, social media, psychological, and impact aspects. Using OWL (Web Ontology Language), we present an ontology framework for assessing fake news, providing a structured approach to analyze text, visuals, audio, audience behavior, source credibility, and news propagation patterns. This framework serves as a foundation for advanced detection systems, contributing to the fight against digital misinformation. © 2024 IEEE.Item Grapevine SDLC Model for Real-Time Fake News Classification(Springer Science and Business Media Deutschland GmbH, 2025) Aishwarya, C.; Shekokar, T.P.; Naga Mukesh, K.; Venkatesan, M.; Prabhavathy, P.In an era of rapid information distribution, the presence of fake news presents enormous difficulties to society, influencing public opinion and decision-making on a global scale. To address this issue, a reliable and efficient system capable of detecting and classifying fake news in real time must be developed. This project proposes the design and implementation of a specialized Software Development Life Cycle (SDLC) model, called the Grapevine SDLC, specifically designed for developing a real-time fake news classifier using Large Language Models (LLMs) and Apache Kafka. The Grapevine SDLC takes a methodical, iterative approach, starting with a thorough requirements analysis that identifies both system capabilities and limitations. During the design and development phase, the system architecture is crafted with a focus on scalability and real-time processing, integrating LLMs for highly accurate content analysis and categorization. Kafka’s distributed messaging platform ensures seamless and efficient data streaming, enabling the system to handle large volumes of data in real time. Further, the model includes continuous monitoring and feedback loops to improve detection accuracy and adapt to evolving fake news patterns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Item Text-mining-based Fake News Detection Using Ensemble Methods(Chinese Academy of Sciences, 2020) Reddy, H.; Raj, N.; Gala, M.; Annappa, A.Social media is a platform to express one’s views and opinions freely and has made communication easier than it was before. This also opens up an opportunity for people to spread fake news intentionally. The ease of access to a variety of news sources on the web also brings the problem of people being exposed to fake news and possibly believing such news. This makes it important for us to detect and flag such content on social media. With the current rate of news generated on social media, it is difficult to differentiate between genuine news and hoaxes without knowing the source of the news. This paper discusses approaches to detection of fake news using only the features of the text of the news, without using any other related metadata. We observe that a combination of stylometric features and text-based word vector representations through ensemble methods can predict fake news with an accuracy of up to 95.49%. © 2020, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature.Item Hindi fake news detection using transformer ensembles(Elsevier Ltd, 2023) Praseed, A.; Rodrigues, J.; Santhi Thilagam, P.S.In the past few decades, due to the growth of social networking sites such as Whatsapp and Facebook, information distribution has been at a level never seen before. Knowing the integrity of information has been a long-standing problem, even more so for the regional languages. Regional languages, such as Hindi, raise challenging problems for fake news detection as they tend to be resource constrained. This limits the amount of data available to efficiently train models for these languages. Most of the existing techniques to detect fake news is targeted towards the English language or involves the manual translation of the language to the English language and then proceeding with Deep Learning methods. Pre-trained transformer based models such as BERT are fine-tuned for the task of fake news detection and are commonly employed for detecting fake news. Other pre-trained transformer models, such as ELECTRA and RoBERTa have also been shown to be able to detect fake news in multiple languages after suitable fine-tuning. In this work, we propose a method for detecting fake news in resource constrained languages such as Hindi more efficiently by using an ensemble of pre-trained transformer models, all of which are individually fine-tuned for the task of fake news detection. We demonstrate that the use of such a transformer ensemble consisting of XLM-RoBERTa, mBERT and ELECTRA is able to improve the efficiency of fake news detection in Hindi by overcoming the drawbacks of individual transformer models. © 2022 Elsevier LtdItem LeDoFAN: enhancing lengthy document fake news identification leveraging large language models and explainable window-based transformers with n-gram expulsion(Springer Science and Business Media Deutschland GmbH, 2025) LekshmiAmmal, H.R.; Anand Kumar, M.Nowadays, people use social media to gather everything around them and consider it their primary source of information. Moreover, people rely more on information disseminated through social media and news channels. The alarming concern is that as the amount of information increases, the amount of fake news or misinformation spread also increases through social media. Generally, fake news has few lines of data; when it comes to a document or an article, the amount of information or the size of the documents is high, and it needs to be appropriately trained to build a model. In this work, we have developed a model that identifies and classifies fake news, consisting of articles collected from social media websites and news pages trained using transformer-based architecture. We have introduced a novel window method for handling lengthy documents and an N-gram expulsion method for managing similar words for classifying the article as fake or real news. We achieved the state-of-the-art F1-score of 0.3492 on test data for the window-based N-gram expulsion method and got an F1-score improvement of 2.1% for long documents alone with this method. We also explored the large language models (LLMs), specifically TinyLlama, which could only achieve an F1-score of 0.2098, and with LLama for summarization of the document that achieved an F1-score of 0.3402 with N-gram expulsion. We have further explored the results using Explainable Artificial Intelligence (XAI) to know the reason behind the proposed model’s intuition. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.