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Deploying NLP techniques in Twitch application to comprehend online user behaviour
Sentiment analysis of emotion entails identifying and analyzing subjective information from language, such as views and attitudes, and helps to improve data visualization by employing a variety of strategies, tactics, and tools. New media channels have significantly changed how people interact, exchange ideas, and share information. Numerous businesses have begun to mine this data, concentrating on social media since it is a popular platform for customers to voice their ideas about various brands or goods and because it gives users an audience, enhancing the visibility and potential effect of this input. So far, as the internet expands and modern technology advances, new avenues have emerged with a higher ability to offer businesses pertinent feedback on their goods. The goal of this study is to investigate the many forms of online behaviour by analyzing chat interactions from the well-known streaming service Twitch. Emotes were occasionally employed in place of letters, to get attention, or to communicate emotions. We propose a system that may take in chat logs from a certain stream, use a sentiment analysis algorithm to classify each message, and then display the data in a way that might permit users to analyze the results according to its polarity (positive message, negative message, or neutral message). This application must be sufficiently versatile to be used with any platform broadcast type and to handle the datasets at very huge level. 2023 IEEE. -
Deploying Fact-Checking Tools to Alleviate Misinformation Promulgation in Twitter Using Machine Learning Techniques
In the present era, the rising portion of our lives is spending interactions online with social media platforms. Thanks to the latest technology adoption as well as smartphones proliferation. Gaining news from the platforms of social media is quicker, easier as well as cheaper in comparison with other traditional media platforms such as T.V and newspapers. Hence, social media is being exploited in order to spread misinformation. The study tends to construct fake corpus that comprises tweets for a product advertisement. The FakeAds corpus objective is to explore the misinformation impact on the advertising and marketing materials for a particular product as well as what kinds of products are targeted mostly on Twitter to draw the consumers attention. Products include cosmetics, fashions, health, electronics, etc. The corpus is varied and novel to the topic (i.e., Twitter role in spreading misinformation in relation to production promotion and advertising) as well as in terms of fine-grained annotations. The guidelines of the annotations were framed through the guidance of domain experts as well as the annotation is done with two domain experts, which results in higher quality annotation, through the agreement rate F-scores as higher as 0.976 using text classification. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
FC-Kit: an intelligent fact-checking system for preventing fake news spread in social media through active plugins
The rapid expansion of social media has intensified the spread of misinformation, threatening public trust, informed decision-making and societal stability. This paper introduces the Fact-Checking Kit (FC-Kit), a plugin-based, real-time misinformation detection framework designed for seamless integration into social media platforms. At its core, the system employs the proposed CanineNet News Sentinel (CnNS) model, which incorporates advanced algorithms for detecting fake news while also assessing bias indicators, identifying clickbait headlines, detecting poor text framing and calculating an article credibility rate. Experimental evaluations on benchmark datasets Twitter and Twitch demonstrate that FC-Kit achieves 99% detection accuracy and reduces computational time by 41.4% compared to state-of-the-art methods. Unlike conventional fact-checking systems, FC-Kit actively tracks the news dissemination chain, enabling early intervention before misinformation gains traction. Its modular plugin architecture supports real-time analysis, ensuring media literacy promotion and fostering critical thinking among users. By combining content credibility scoring with advanced detection features, FC-Kit offers a scalable and practical solution for social media platforms, fact-checking organizations and researchers committed to combating online misinformation. This work advances the state-of-the-art in misinformation detection and emphasizes the necessity of embedding automated fact-checking tools directly into social media platforms. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
FC-Kit: an intelligent fact-checking system for preventing fake news spread in social media through active plugins
The rapid expansion of social media has intensified the spread of misinformation, threatening public trust, informed decision-making and societal stability. This paper introduces the Fact-Checking Kit (FC-Kit), a plugin-based, real-time misinformation detection framework designed for seamless integration into social media platforms. At its core, the system employs the proposed CanineNet News Sentinel (CnNS) model, which incorporates advanced algorithms for detecting fake news while also assessing bias indicators, identifying clickbait headlines, detecting poor text framing and calculating an article credibility rate. Experimental evaluations on benchmark datasets Twitter and Twitch demonstrate that FC-Kit achieves 99% detection accuracy and reduces computational time by 41.4% compared to state-of-the-art methods. Unlike conventional fact-checking systems, FC-Kit actively tracks the news dissemination chain, enabling early intervention before misinformation gains traction. Its modular plugin architecture supports real-time analysis, ensuring media literacy promotion and fostering critical thinking among users. By combining content credibility scoring with advanced detection features, FC-Kit offers a scalable and practical solution for social media platforms, fact-checking organizations and researchers committed to combating online misinformation. This work advances the state-of-the-art in misinformation detection and emphasizes the necessity of embedding automated fact-checking tools directly into social media platforms. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
A web forensic optimization framework for investigating false information on social media using the ForenOptiNet model
Todays technological advancements in the field of digital media have resulted in the unprecedented transmission of information leading to unauthorized exploitation. Businesses use social media as the primary marketing platform. Considering the severity of spreading misinformation and fake news in our society due to false marketing by bogus businesses, there is a great need to demystify this propagation using web forensics-based frameworks. In order to increase consumer equity, the rapid spreading of malicious information makes it hard for users to differentiate between real and false information. This research intends to design an effective and adaptable framework for detecting false information campaign carried out by criminals affecting online social network (ONS). A novel ForenOptiNet model is designed and diverse data gathered from the Reddit and INFD dataset is used to train the suggested model. The Web Forensic-Based Investigation Optimization (WFBIO) algorithm provides a high accuracy classification of malicious content from the web. Moreover, the WFBIO framework enhances the robustness of the ForenOptiNet model and ensures that the proposed approach can effectively identifies misinformation and fake news to validate factual claims. Results of the simulation analysis provides a muti-level mechanism combining anomaly detection and ForenOptiNet model together outperforming other state-of the-art optimization algorithms trained against CNNs with SGD, Adagrad and AdaDelta. While these baselines yielded accuracies between 55 and 92%, our proposed model achieved highest accuracy of 99% accuracy with an effective front-end design integration. The Author(s) 2025. -
FusionBotSentinel: A Framework to Mitigate Probable Social Bots Spreading False Information in Cyber Physical Systems
The escalating dissemination of fake news across social media networks has emerged as a concerning societal issue and a threat to cyber physical systems. Bots, often employed to propagate such misinformation, present a formidable challenge in their detection and elimination. Bot prediction have been pivotal in identifying and curbing these deceptive bot activities within social media networks. Twitchs live streaming content is readily scrapable and totally accessible. But quite understudied. Recent studies scrutinized these frameworks, revealing significant strides in their development while acknowledging the need for further enhancements in both predictions for proactive measures. FusionBotSentinel proposes a novel architecture that underscores the imperative for future research to concentrate on fortifying these frameworks, ensuring they are more resilient and adaptable in mitigating and predicting the spread of fake news by social bots. Another focus is on enhancing the effectiveness of deep learning models through a refined understanding of data quality with a largest dataset available and employing better hybrid techniques that bolster the generalizability and robustness helping in forecasting bot activities in combatting this escalating problem within cyber physical systems. Since bots are seen to be the source of the present problems with cyber physical systems, including privacy, security, safety, and ethical difficulties, it is necessary to recognize these gaps. Our suggested FusionBotSentinelprovides a revolutionary significance by contributing to in combatting fake news in the society by achieving up to 99% in accuracy, 98% in precision, 100% in recall, 99% in sensitivity with F1 score as 99% in social bot prediction offering 20% more efficiency when compared to the most advanced existing models proving its superiority. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
FORCASTING DEPLOYMENT OF WEB FORENSICS TO AVERT MISINFORMATION SPREAD USING WEFA TOOL
As fake news or misinformation is becoming more prevalent, it is distorting people's understanding and decisions by influencing their perceptions and knowledge. Since long before the Internet, misinformation and hoaxes have existed. News outlets and social media websites publish false news to increase readership or as a psychological warfare tactic, especially during times of crisis, such as the COVID-19 pandemic. Social networks gener ated large amounts of multimedia data due to the ubiquitous nature of social media platforms. Misinformation, also called rumors, may cause severe damages due to its unverified nature. Let me start by saying there is a big problem of fake news currently affecting societies, nations, and individuals. For example, a fake news story about alleged medicine in the United States or Brazil was part of the COVID-19 pandemic (e.g., misinformation on fake medicines); misinformation has negatively impacted democratic elections and the statuses of individuals and organizations. Misinformation must be addressed; and we have to find reliable solutions that are agile and reliable. In this sense, it provides a serious assessment of the current knowledge state in misinformation detection, both in order to describe probable solutions as well as to inspire upcoming research. Our study is extended to focus on means to avert this misinformation spread with the help of forensic web based tools along with ML that can extend its application to identifying systems and websites responsible for this malicious attempt. 2026 by Apple Academic Press, Inc. -
Inter-State Migration, Footloose Labour and Accessibility to Health Care: An Exploration among Metro Workers of a Camp in Bengaluru
The neoliberal political economy that India adopted in 1991 has brought in huge Foreign Direct Investments, which has led to a perceptible increase in the number of migrants in the major cities of India due to various structural reasons in their place of origin and rapid developmental activities in the cities. Bengaluru has the second largest migrant population after Mumbai, and as per the labour department of the government of Karnataka; there are more than 65 lakh migrant workers in Karnataka, who are involved in various developmental projects, including the metro railway project in Bangalore. Even though the Karnataka Building and Other Construction Workers Welfare Board (KBOCWWB) offers certain social security, including health care for registered migrants, they must wait more than a year to get these benefits. With privatisation and increased out-of-pocket expenditure for health related issues, the migrants face a major hurdle in surviving at the migrated workplaces. Many of them are unaware of welfare boards, and the number of migrants who are registered with them is very small. This paper aims to understand the accessibility of health facilities for migrant workers working in the Bengaluru Metro Project. This research will understand the legal, economic and psychological aspects related to the health status of migrant workers through qualitative study. The study used in-depth interviews to elicit responses from selected inter-state migrant workers to understand their access towards health facilities. The thematic analysis of the interview transcripts revealed a substantive gap in workers access to health facilities. The unregulated working conditions have added more stress to the workers, and due to poverty and unemployment back home, these hurdles are not forcing them to go back. More awareness creating interventions from the government can transform their lives. (2024), (University of Duisburg). All rights reserved. -
Offline Handwritten Character and Numeral Recognition: A Kernel-Based Approach
Automatic character recognition for the handwritten Indic script is a challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, all the state-of-the-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level fast discrete curvelet transform (FDCT) to get higher dimension feature vector. After that, kernel-principal component analysis (K-PCA) is considered to obtain optimal features from FDCT feature. Finally, the classification is performed by using probabilistic neural network (PNN) on a handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of the proposed scheme performs better compared to existing models with optimized Gaussian kernel-based feature sets. Copyright 2022, IGI Global. -
A New Facile Iodine-Promoted One-Pot Synthesis of Dihydroquinazolinone Compounds
A one-pot iodine catalyzed reaction has been developed for the preparation of dihydroquinazolinones from isatoic anhydride, enaminones, and amines in modest to good yields. The reaction has been screened in various catalysts and solvents and a gram scale experiment has been performed based on the optimum conditions. A possible mechanism has been proposed based on the control experiments. The reaction has been checked with broad range of substrates. 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
A study on causes of job stress in the IT sector of Bangalore
International Journal of Research in Commerce, IT & Management Vol.2, Issue 2, pp. 126-128 ISSN No. 2231-5756 -
Digital Gender Gap, Gender Equality and National Institutional Freedom: A Dynamic Panel Analysis
While digital gender gap is a growing field of research in Information Systems (IS), there remains a dearth of research focusing on it. The objective of this study is to investigate the relationship between the digital gender gap in mobile and internet usage and gender equality. Additionally, this study also examines the impact of national institutional freedoms on the aforementioned relationship. Utilizing the theoretical framework of intersecting inequalities and building upon existing literature on the gender digital divide, this study aims to explore the associations between disparities in mobile and internet usage, gender equality, and the extent of national institutional freedoms encompassing economic, political, and media domains. In pursuit of this objective, we undertake a dynamic panel data analysis using publicly accessible archival data at the country level. The results indicate that national institutions have a significant impact on the relationship between the digital gender gap in internet and mobile phone usage and gender equality. The discussion encompasses the significance of our findings for both study and practice. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Is ChatGPT Enhancing Youths Learning, Engagement and Satisfaction?
Integration of artificial intelligence (AI) in educational practices necessitates the understanding of the influence of tools such as ChatGPT. Self-determination Theory (SDT) has been used to examine the impact of ChatGPT usage by students for the improvement of perceived learning, engagement and satisfaction. The moderating role of students AI literacy between ChatGPT and the antecedents of intrinsic motivation, autonomy, competence and relatedness. The data was collected through questionnaire from 481 students and structural equation modeling was used to analyze the data. The findings of the study shows that ChatGPT usage impacts students perceived autonomy, competence, and relatedness, enhancing intrinsic motivation. Also, there is a moderation of AI literacy between ChatGPT usage and these psychological needs. This study extends SDT to student interactions with ChatGPT and underscores the pivotal role of AI literacy. The findings contribute to the discourse on AI and education, offering valuable perspectives on students use of ChatGPT and its effect on their academic experience. 2024 International Association for Computer Information Systems. -
Continuance Intention of ChatGPT Use by Students
ChatGPT, an AI language model, has gained significant attention for its potential to enhance educational experiences and foster interactive learning environments. The potential of student interaction via ChatGPT has engendered significant debate around educational technology. It is apparent that the current literature has yet to fully explore the role of ChatGPT in management education. Amidst the increasing integration of ChatGPT into educational contexts, the concept of continuance intention takes center stage. This research paper delves into the nuanced landscape of students continuance intention regarding the use of ChatGPT in educational settings. We ground our study in Technology Continuance Theory and Theory of Planned Behavior to examine students continuance intention to use ChatGPT. By investigating the determinants that shape this intention, we aim to provide insights that inform educators and educational technology designers in optimizing the integration of AI-driven tools like ChatGPT. This study contributes to the growing body of research at the intersection of AI and education, offering valuable implications for both theory and practice. 2024, IFIP International Federation for Information Processing. -
Hotel Recommendation System Based on Customer's Reviews Content Based Filtering Approach
Recommendation systems are fantastic tools for remembering people's ideas in order to gain knowledge more efficiently and selectively. Recently, booking and searching for hotels online has become more common. As it takes more time, online hotel research is growing more quickly. In addition, the amount of knowledge accessible online is continuously expanding. User preferences have a big impact on hotel recommendations. The most effective recommendations may be made by recommendation systems by utilising historical user preference data. To solve this problem, recommender systems have suggested content-based filtering methods. Product recommendations, recommendations for websites, news articles, restaurants, and TV series are all examples of applications for content-based recommender systems. The dataset for this project includes client evaluations of the offered Kaggle profile. Word embedding, word2vec, and TF-IDF natural language processing methods were used for feature extraction. The algorithm shows the user the top 10 suggested hotels based on the user's past knowledge of the hotel's location. 2022 IEEE. -
Sex determination using finger print ridge density among the medical students of NIMS medical college, Jaipur
To determine the sex of an individual plays an important role among forensic pathologists and scientists particularly when the fingerprints recovered from the crime scene does not match any of the criminal record so in that case fingerprint ridge density plays an important role in determining the sex of an individual. The present study was done among the 100 medical students of NIMS Medical college (50 males and 50 females) shobha nagar, Jaipur. Finger ridge density was counted on the radial border of each print. Result of the study shows that females have higher number of finger ridge density count as compared to males. Application of Bayes theorem suggests that finger print ridge density count <14ridges/25mm2 is more likely to be male while finger print ridge density count >14ridges/25mm2 is more likely to be female. 2016, World Informations Syndicate. All rights reserved. -
Influence of industrialization on economic growth in the asian tigers and lessons for India
Economic growth over the past two centuries has been driven mainly by the process of industrialization. Mechanized manufacturing, factories, and technological advancements have contributed largely to economic development. A prime example of such countries is the Asian Tigers- Hong Kong, Singapore, Taiwan, and South Korea, and the Tiger Cubs- Indonesia, Thailand, Malaysia, Philippines, and Vietnam that have achieved rapid industrialization through export-led strategies, technological innovation, and strong policies fostering economic development. India gained its independence around the same time as the Tiger, though the pursuit of industrialization hasnt been as pronounced in India as it has been in the Tigers. This study examines the impact of industrialization, proxied with industrial efficiency, on the GDP per capita of the tiger economies and India. Along with other control variables like FDI inflows, inflation, market capitalization, manufacturing exports, ICT imports, and CO2 emissions. Using data from 1991 to 2022, Using data from 1991 to 2022, a 2SLS model is applied to the Tiger economies using the instrument, control of corruption. A time series Autoregressive Distributed Lag model is used for India. The findings of this paper confirm that industrialization was the primary driver of the economic success of the Asian Tigers, while showing weaker progress in India. Building efficient infrastructure facilities, strengthening human capital formation and export-led manufacturing could allow India to emulate the strategy of the Asian Tigers. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
The Role of Psychoactive Drugs in the Onset of Dissociative Identity Disorder: A Comparative Study of Alcohol and Drug Abusers
Psychoactive substances influence mood, cognition, and personality, with emerging synthetic drugs presenting new challenges. This study examines the impact of psychoactive drug use on the development of dissociative identity disorder (DID), comparing alcohol and drug abusers. A total of 120 participants (60 alcohol abusers, 60 drug abusers) from Suman Ellen Trust in Bangalore were assessed using standardized psychological instruments, including the Addiction Severity Index, Dissociative Disorders Interview Schedule, Drug Abuse Screening Test, and Dissociative Experiences Scale. A mixed-methods approach was employed, incorporating both quantitative (ANOVA, Pearson correlation) and qualitative (thematic analysis) methodologies to analyze dissociative symptoms and substance use patterns. Findings revealed a significant difference in DID prevalence between alcohol and drug abusers (F = 55.88, p <.01), with drug abuse showing a strong positive correlation with dissociative symptoms (r = 0.790, p <.01). The study highlights the need for integrated clinical approaches addressing both substance use disorders and dissociative pathology. Future research should focus on longitudinal studies to clarify causal mechanisms and develop targeted intervention strategies. 2026 Taylor & Francis Group, LLC. -
Digital Gender Gap, Gender Equality and National Institutional Freedom: A Dynamic Panel Analysis
While digital gender gap is a growing field of research in Information Systems (IS), there remains a dearth of research focusing on it. The objective of this study is to investigate the relationship between the digital gender gap in mobile and internet usage and gender equality. Additionally, this study also examines the impact of national institutional freedoms on the aforementioned relationship. Utilizing the theoretical framework of intersecting inequalities and building upon existing literature on the gender digital divide, this study aims to explore the associations between disparities in mobile and internet usage, gender equality, and the extent of national institutional freedoms encompassing economic, political, and media domains. In pursuit of this objective, we undertake a dynamic panel data analysis using publicly accessible archival data at the country level. The results indicate that national institutions have a significant impact on the relationship between the digital gender gap in internet and mobile phone usage and gender equality. The discussion encompasses the significance of our findings for both study and practice. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Is ChatGPT Enhancing Youths Learning, Engagement and Satisfaction?
Integration of artificial intelligence (AI) in educational practices necessitates the understanding of the influence of tools such as ChatGPT. Self-determination Theory (SDT) has been used to examine the impact of ChatGPT usage by students for the improvement of perceived learning, engagement and satisfaction. The moderating role of students AI literacy between ChatGPT and the antecedents of intrinsic motivation, autonomy, competence and relatedness. The data was collected through questionnaire from 481 students and structural equation modeling was used to analyze the data. The findings of the study shows that ChatGPT usage impacts students perceived autonomy, competence, and relatedness, enhancing intrinsic motivation. Also, there is a moderation of AI literacy between ChatGPT usage and these psychological needs. This study extends SDT to student interactions with ChatGPT and underscores the pivotal role of AI literacy. The findings contribute to the discourse on AI and education, offering valuable perspectives on students use of ChatGPT and its effect on their academic experience. 2024 International Association for Computer Information Systems.