International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning-Based Detection of Fake Product Reviews and News Articles |
|---|---|
| ABSTRACT | With the unprecedented proliferation of online platforms, social media networks, and e-commerce websites, detecting fake content—specifically fake reviews and fake news—has become a critical and formidable challenge for ensuring the authenticity and reliability of digital information. The digital age has democratized content creation, allowing anyone to publish information; however, this has also paved the way for malicious actors to disseminate deceptive content at scale. This comprehensive paper presents an extensive survey and an enhanced, generalized framework utilizing machine learning (ML) techniques and models applied to the dual problem of fake review and fake news detection. By leveraging advanced Natural Language Processing (NLP) methods and hybrid machine learning approaches, the paper rigorously evaluates various algorithms, including Support Vector Machines (SVM), Random Forests, Long Short-Term Memory (LSTM) networks, and complex ensemble models, assessing their performance in detecting deceptive content. Key performance metrics, such as accuracy, precision, recall, and F1-Score, are systematically analyzed across multiple large-scale datasets to determine the effectiveness and robustness of these computational approaches. Furthermore, this study delves deeply into domain-specific challenges that currently plague the field, including the intricate handling of imbalanced datasets, the parsing of linguistic nuances (such as sarcasm and irony), and the stringent requirements for real-time detection in live data streams. The paper concludes by outlining strategic future directions, emphasizing the pressing need for enhanced models capable of addressing continuously evolving deception techniques and integrating broader contextual factors to yield more accurate and reliable predictions. |
| AUTHOR | AKUMALLA JAHNAVI, DR D. SURESH REDDY PG Student, Dept. of CSE, Siddartha Educational Academy Group of Institutions, Tirupati, India Assoc. Professor, Dept. of CSE(AI&ML), Siddartha Educational Academy Group of Institutions, Tirupati, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406024 |
| pdf/24_Machine Learning-Based Detection of Fake Product Reviews and News Articles.pdf | |
| KEYWORDS | |
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