Assessment of fake news detection from machine learning and deep learning techniques
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Megha Shah
Abstract
The overwhelming prevalence of news data across numerous online platforms is far less taxing, but it has become an unnecessary burden that has transformed people’s lives. The dominance of mass media on the internet has had a substantial impact, with many individuals depending on it as a regular source of information. However, this widespread influence has also given rise to the creation of partially evident or entirely fabricated news stories. Intentionally spreading these fake stories through online social networking sites has become a common practice. Websites now primarily aim to mold public opinion using false information. The core aim of this study is to develop a reliable model that can identify a given news report as true or false. For this, the authors have modeled a trust-based architecture for online shared news incorporating natural language processing in machine learning (ML) and deep learning (DL) techniques. To develop the architecture, six ML models, two long-short-term memory (LSTM) models, and two distinct feature extraction techniques have been utilized. The findings reveal that, out of all six ML models, random forest with TF-IDF and logistic regression with CountVectorizer yield the optimal results. In the case of DL models, the outcomes for the LSTM model and bi-directional LSTM have yielded the same results.
Abstract
The overwhelming prevalence of news data across numerous online platforms is far less taxing, but it has become an unnecessary burden that has transformed people’s lives. The dominance of mass media on the internet has had a substantial impact, with many individuals depending on it as a regular source of information. However, this widespread influence has also given rise to the creation of partially evident or entirely fabricated news stories. Intentionally spreading these fake stories through online social networking sites has become a common practice. Websites now primarily aim to mold public opinion using false information. The core aim of this study is to develop a reliable model that can identify a given news report as true or false. For this, the authors have modeled a trust-based architecture for online shared news incorporating natural language processing in machine learning (ML) and deep learning (DL) techniques. To develop the architecture, six ML models, two long-short-term memory (LSTM) models, and two distinct feature extraction techniques have been utilized. The findings reveal that, out of all six ML models, random forest with TF-IDF and logistic regression with CountVectorizer yield the optimal results. In the case of DL models, the outcomes for the LSTM model and bi-directional LSTM have yielded the same results.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Machine learning-enabled techniques for speech categorization 1
- Comprehensive study of cybersecurity issues and challenges 21
- An energy-efficient FPGA-based implementation of AES algorithm using HSTL IO standards for new digital age technologies 41
- A comparative study on security issues and clustering of wireless sensor networks 55
- Heuristic approach and its application to solve NP-complete traveling salesman problem 69
- Assessment of fake news detection from machine learning and deep learning techniques 87
- Spam mail detection various machine learning methods and their comparisons 119
- Cybersecurity threats in modern digital world 137
- Mechanism to protect the physical boundary of organization where the private and public networks encounter 149
- By combining binary search and insertion sort, a sorting method for small input size 167
- Index 179
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Machine learning-enabled techniques for speech categorization 1
- Comprehensive study of cybersecurity issues and challenges 21
- An energy-efficient FPGA-based implementation of AES algorithm using HSTL IO standards for new digital age technologies 41
- A comparative study on security issues and clustering of wireless sensor networks 55
- Heuristic approach and its application to solve NP-complete traveling salesman problem 69
- Assessment of fake news detection from machine learning and deep learning techniques 87
- Spam mail detection various machine learning methods and their comparisons 119
- Cybersecurity threats in modern digital world 137
- Mechanism to protect the physical boundary of organization where the private and public networks encounter 149
- By combining binary search and insertion sort, a sorting method for small input size 167
- Index 179
