Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Early detection of depression using BERT and DeBERTa
    (CEUR-WS, 2022) Devaguptam, S.; Kogatam, T.; Kotian, N.; Anand Kumar, A.M.
    In today’s world, social media usage has become one of the most fundamental human activities. On the report of Oberlo, at present, 3.2 billion people are on social media, which comprises 42% of the World’s population. People usually post about their daily life style, special occasions, views about on-going issues and their networks on the social media platforms. People also share things on social media which otherwise would not have shared with other people. Social media helps us to stay connected, keep informed, mobilise on social issues. Due to the surge of suicide attempts, social media can act as a life saver in detecting and tracing users who are on the verge of depression and self-harm. Natural language processing methods with the help of deep learning are aiding in solving language/text related real world problems like sentiment analysis, translation of text into different languages, depression detection. Many transformer based models like BERT (Bidirectional Encoders Representations from Transformers) are put to use to solve NLP problems, which voluntarily learns to attend to different features differently (Weighing). In this paper, a supervised machine learning algorithm with transfer learning approach is used to detect self-harm tendency in the social media users at the earliest. © 2022 Copyright for this paper by its authors.
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    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.