Faculty Publications
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Item 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.Item Assessing mobile health applications with twitter analytics(Elsevier Ireland Ltd, 2018) Pai, R.R.; Alathur, S.Introduction: Advancement in the field of information technology and rise in the use of Internet has changed the lives of people by enabling various services online. In recent times, healthcare sector which faces its service delivery challenges started promoting and using mobile health applications with the intention of cutting down the cost making it accessible and affordable to the people. Objectives: The objective of the study is to perform sentiment analysis using the Twitter data which measures the perception and use of various mobile health applications among the citizens. Methods: The methodology followed in this research is qualitative with the data extracted from a social networking site “Twitter” through a tool RStudio. This tool with the help of Twitter Application Programming Interface requested one thousand tweets each for four different phrases of mobile health applications (apps) such as “fitness app” “diabetes app” “meditation app” and “cancer app”. Depending on the tweets, sentiment analysis was carried out, and its polarity and emotions were measured. Results: Except for cancer app there exists a positive polarity towards the fitness, diabetes, and meditation apps among the users. Following a system thinking approach for our results, this paper also explains the causal relationships between the accessibility and acceptability of mobile health applications which helps the healthcare facility and the application developers in understanding and analyzing the dynamics involved the adopting a new system or modifying an existing one. © 2018 Elsevier B.V.Item Sentiment analysis of an epidemic: A case of Nipah virus in India(Inderscience Publishers, 2022) Jayan, V.; Alathur, S.; Pai, R.R.Data in social media and other news media can have an impact on the decision-making process of the government and the citizen if properly examined. The mode and pace of dissemination in both media leads to an increase in the delivery of misinformation. This affects the economy of the country and people's mental health. The government must formulate the required measures to counter the proliferation of fake messages and disinformation in the media, which would otherwise lead to an unnecessary burden. Regulation of health communication during the period of epidemic is important, as it has an effect on the mental health of users of the media. The study assesses the emotions of health communication in social media and online news media in the context of the Nipah epidemic in India during 2018. © © 2022 Inderscience Enterprises Ltd.Item Impact of COVID-19 on individuals’ mental health and preventive health behaviours: a conceptual framework(Inderscience Publishers, 2022) Pai, R.R.; Chetty, N.; Alathur, S.The corona virus disease (COVID-19) is a pandemic that facilitate a confrontation space for scientific and social existence of human frontiers. The rapid spread and mortality rate of COVID-19 and the preventive measures including social distancing and its impact on economy, developed an unprecedented consciousness around the globe. It has created an effect on the mental health of individuals employed across various sectors and is outlined in this study. There is currently an inadequate theoretical model that focuses on the comprehensive understanding of the psychology of preventive behaviour during the outbreak of pandemics. In this study, a transnational model is delineated for assessing the adoption of preventive behavioural practices associated with COVID-19 pandemic. It uses the components derived from the theories of situational awareness and health belief model and literatures related to impact of containment strategies on various sectors. The contribution includes policy recommendations that can be helpful for the healthcare professionals and government to control the disease spread. © © 2022 Inderscience Enterprises Ltd.Item An empirical study on mobile-assisted civic and e-learning service through sentiment analysis(Inderscience Publishers, 2023) Vanitha, P.S.; Alathur, S.This paper aims to analyse the use of mobile phone assisted services in civic and academic learning. General and education-related learning applications useful to educate the users are considered as the input. This paper explores the literature into two different aspects: general and education-related mobile applications. The sentiment analysis is carried out to study users' emotions towards the mobile learning (mLearning) service. More than 30,000 tweets were collected. Through sentiment analysis, the users' awareness about mLearning application is analysed and compared. Fewer studies have reported the usefulness of civic learning apps introduced by government agencies. Moreover, the users' perceptions towards the mLearning apps in higher education are also less reported in the Indian context. The findings show the importance of improving mLearning services initiated by government agencies for civic and education-related learning. The suggestions are also provided for the improvement of mLearning services in India. © 2023 Inderscience Enterprises Ltd.Item Social media integrated mobile government adoption model: investigating adoption behaviour in Karnataka's smart cities(Inderscience Publishers, 2024) Hebbar, S.; Kiran, K.B.Digital transformation like m-government and social media (SM) plays an ornamental role in boosting government services and supports smart city mission. However, the public's dissatisfaction with government's use of SM and lower adoption of m-government, necessitates more research into citizens' perspective on these. Hence, the study integrates diffusion of innovation and uncertainty reduction theories with SM-influence and few external variables to analyse people's usage-intention in Karnataka's smart cities. The questionnaire survey yielded 1,444 citizen responses, which were statistically tested using PLS-SEM. The factors relative advantage, compatibility, facilitating condition, and trust were proved significant. Also, the importance of being aware of specific aspects like relative advantage and compatibility (mediators) proved vital. Information quality and transparency were found critical in enhancing trust and thereby impacting usage-intention. Finally, SM is proven significant in strengthening trust, transparency, social influence, image and awareness. The discussions and research implications are expanded upon in this paper. © 2024 Inderscience Enterprises Ltd.Item An empirical investigation to understand mobile phone users’ behavioural intention to give their end-of-life mobile phones for formal recycling(Elsevier Ltd, 2024) Prabhu N, S.; Majhi, R.Mobile phones have turned into a highly essential device for numerous individuals. Swift innovation and decrease in in-use lifespan have increased the generation of end-of-life mobile phones (EOL-MPs). Lesser formal recycling of EOL-MPs has detrimental outcomes on the environment, human health, and circular economy. Therefore, this research was undertaken to investigate factors impacting mobile phone users’ behavioural intention to give their EOL-MPs for formal recycling. The conceptual model was developed by integrating the theory of planned behaviour, norm activation model, and value-belief-norm theory. Responses were collected from mobile phone users aged 18 and above residing in Bengaluru, Mangaluru, and Huballi-Dharvad cities of Karnataka state, India. 1135 responses were analysed by applying partial least squares structural equation modelling. Incentives was figured out to be the most positively impacting construct on behavioural intention. Followed by awareness of consequences, social media, past recycling experience, and recycling attitude. Whereas risk perception regarding information security and convenience of recycling negatively impacts behavioural intention. Personal norms get activated by awareness of consequences and ascription of responsibility. As a result, personal norms positively impact behavioural intention. In addition, biospheric values also positively impact personal norms. The outcomes of PLSpredict signify that the conceptual model has high out-of-sample predictive power. The outcomes of this research can be utilized by various stakeholders like e-waste collection organisations, e-waste recycling organisations, mobile phone manufacturing companies, city corporations, educational institutions, etc for improving sustainable end-of-life management of EOL-MPs. © 2024 Elsevier Ltd