ection-using-Transformer-and-CNN-Based-Algorithms
所属分类：内容生成
开发工具：Jupyter Notebook
文件大小：9452KB
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上传日期：2022-11-09 20:02:45
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(Internet access has made it easier for any news article or post to spread, resulting in quick access to information but, sometimes these platforms are exploited to spread fake information. The research contributes by developing three unique multimodal algorithms that classify fake news by processing the news text and its images simultaneously.)
文件列表:
BERT (0, 2022-11-10)
BERT\BERT_Final.ipynb (239775, 2022-11-10)
BERT\bert_final.py (8140, 2022-11-10)
BERT_CNN (0, 2022-11-10)
BERT_CNN\BERT_CNN_Final_Multimodal.ipynb (1520286, 2022-11-10)
BERT_CNN\bert_cnn_final_multimodal.py (12105, 2022-11-10)
BERT_Inception (0, 2022-11-10)
BERT_Inception\Inception_BERT_Final_Multimodal.ipynb (1605761, 2022-11-10)
BERT_Inception\inception_bert_final_multimodal.py (12976, 2022-11-10)
Image Data_Preprocessing.ipynb (5920, 2022-11-10)
InitialDataClean_ImageFolderCreation.ipynb (1322578, 2022-11-10)
XML_RoBERTa (0, 2022-11-10)
XML_RoBERTa\XML_RoBERTa_Final.ipynb (247413, 2022-11-10)
XML_RoBERTa\xml_roberta_final.py (8232, 2022-11-10)
XML_RoBERTa_CNN (0, 2022-11-10)
XML_RoBERTa_CNN\CNN_XML_RoBERTa_MultiModal_Final.ipynb (1509457, 2022-11-10)
XML_RoBERTa_CNN\cnn_xml_roberta_multimodal_final.py (12235, 2022-11-10)
multimodal_data.tsv (15342046, 2022-11-10)
x20207786_ResearchProject_ConfigManual.pdf (426191, 2022-11-10)
x20207786_ResearchProject_Presentation.pptx (2530187, 2022-11-10)
# MULTIMODAL-FAKE-NEWS-AND-TAMPERED-IMAGE-DETECTION-USING-TRANSFORMER-AND-CNN-BASED-ALGORITHMS
In this modern world, the use of computing devices and internet access has made it easier for any news article or post to spread among the masses, resulting in quick access to information but, sometimes these platforms are exploited to spread fake information. A customized multimodal algorithm that uses these news headlines to verify the authenticity and concurrently recognize if its corresponding image information is fabricated or not can reduce the spread of wrong information. The research contributes by developing three unique multimodal algorithms BERT+CNN, BERT+InceptionV3, and XML_RoBERTa+CNN that classify fake news text and related images simultaneously. The study was crucial in understanding the impact of using multimodal text and visual features to classify fake news and the obtained results were analyzed to extract insights from the implemented multimodal technique.
Developed Architecture:
Deep Learning Algorithms and accuracy obtained: BERT (72%), XML_RoBERTa (66%), BERT+CNN (71%), BERT+InceptionV3 (70%), XML_RoBERTa+CNN (63%)
Programming Language: Python
Tools and Methodologies Used: Text Data Pre-processing - NLTK, Transformer-based Algorithms, Anaconda Navigator - Jupyter Notebook, Google Drive - Data Storing
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