LSTMCNN: A hybrid machine learning model to unmask fake news
The widespread dissemination of false information across various online platforms has emerged as a matter of paramount concern due to the potential harm it poses to individuals, communities, and entire nations. Substantial efforts are currently underway in the research community to combat this issue...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024012751 |
| _version_ | 1797304498841976832 |
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| author | Deepali Goyal Dev Vishal Bhatnagar Bhoopesh Singh Bhati Manoj Gupta Aziz Nanthaamornphong |
| author_facet | Deepali Goyal Dev Vishal Bhatnagar Bhoopesh Singh Bhati Manoj Gupta Aziz Nanthaamornphong |
| author_sort | Deepali Goyal Dev |
| collection | DOAJ |
| description | The widespread dissemination of false information across various online platforms has emerged as a matter of paramount concern due to the potential harm it poses to individuals, communities, and entire nations. Substantial efforts are currently underway in the research community to combat this issue. A burgeoning area of study gaining significant traction is the development of fake news identification techniques. However, this field faces formidable challenges primarily stemming from limited resources, including access to comprehensive datasets, computational resources, and evaluation tools. To overcome these challenges, researchers are exploring various methodologies. One promising approach involves the use of feature abstraction and vectorization techniques. In this context, we highly recommend utilizing the Python sci-kit-learn module, which offers many invaluable tools such as the Count Vectorizer and Tiff Vectorizer. These tools enable the efficient handling of text data by converting it into numerical representations, thereby facilitating subsequent analysis. Once the text data is appropriately transformed, the next crucial step involves feature selection. To achieve optimal results, researchers often employ feature selection methods based on misperception matrices. These methods allow for the exploration and selection of the most suitable features, which are essential for achieving the highest accuracy in fake news identification. |
| first_indexed | 2024-03-08T00:11:24Z |
| format | Article |
| id | doaj.art-b042d2940c284333bcf8fadee1b748bc |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T00:11:24Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-b042d2940c284333bcf8fadee1b748bc2024-02-17T06:40:14ZengElsevierHeliyon2405-84402024-02-01103e25244LSTMCNN: A hybrid machine learning model to unmask fake newsDeepali Goyal Dev0Vishal Bhatnagar1Bhoopesh Singh Bhati2Manoj Gupta3Aziz Nanthaamornphong4GGSIPU, AIACTR, Delhi and Assistant Professor, ABES Engineering College, Ghaziabad, UP, IndiaNSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies and Research), New Delhi, IndiaIndian Institute of Information Technology, Sonepat, Haryana, IndiaDepartment of Electrical Engineering, SOS-Engineering & Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur (Chhattisgarh), India; Corresponding author.College of Computing, Prince of Songkla University, Phuket Campus, Phuket, Thailand; Corresponding author.The widespread dissemination of false information across various online platforms has emerged as a matter of paramount concern due to the potential harm it poses to individuals, communities, and entire nations. Substantial efforts are currently underway in the research community to combat this issue. A burgeoning area of study gaining significant traction is the development of fake news identification techniques. However, this field faces formidable challenges primarily stemming from limited resources, including access to comprehensive datasets, computational resources, and evaluation tools. To overcome these challenges, researchers are exploring various methodologies. One promising approach involves the use of feature abstraction and vectorization techniques. In this context, we highly recommend utilizing the Python sci-kit-learn module, which offers many invaluable tools such as the Count Vectorizer and Tiff Vectorizer. These tools enable the efficient handling of text data by converting it into numerical representations, thereby facilitating subsequent analysis. Once the text data is appropriately transformed, the next crucial step involves feature selection. To achieve optimal results, researchers often employ feature selection methods based on misperception matrices. These methods allow for the exploration and selection of the most suitable features, which are essential for achieving the highest accuracy in fake news identification.http://www.sciencedirect.com/science/article/pii/S2405844024012751Fake-newsCNNLSTMNLP |
| spellingShingle | Deepali Goyal Dev Vishal Bhatnagar Bhoopesh Singh Bhati Manoj Gupta Aziz Nanthaamornphong LSTMCNN: A hybrid machine learning model to unmask fake news Heliyon Fake-news CNN LSTM NLP |
| title | LSTMCNN: A hybrid machine learning model to unmask fake news |
| title_full | LSTMCNN: A hybrid machine learning model to unmask fake news |
| title_fullStr | LSTMCNN: A hybrid machine learning model to unmask fake news |
| title_full_unstemmed | LSTMCNN: A hybrid machine learning model to unmask fake news |
| title_short | LSTMCNN: A hybrid machine learning model to unmask fake news |
| title_sort | lstmcnn a hybrid machine learning model to unmask fake news |
| topic | Fake-news CNN LSTM NLP |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024012751 |
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