Detection of Fake News using different Machine Learning and Natural Language Processing Algorithms

Simple item page
creativework.keywordsMachine learning, Natural language, Algorithms, bidirectional encoder representation from transformers, fake news detection, lemmatization, long short-term memory, naive Bayes, support vector machine, tokenization
dc.contributor.advisorDr. Riasat Khan
dc.contributor.authorMd. Emanul Haque Rafi
dc.contributor.authorNoshin Nirvana Prachi
dc.contributor.authorEvan Alam
dc.contributor.authorMd. Habibullah
dc.contributor.id1611149042
dc.contributor.id1610394042
dc.contributor.id1632230642
dc.contributor.id1712220642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-11-27
dc.date.accessioned2025-11-27T06:04:27Z
dc.date.available2025-11-27T06:04:27Z
dc.date.issued2021-08-30
dc.description.abstractThe amount of information shared on the internet, primarily via web-based networking media, grows day by day. Because of the simple availability and exponential expansion of data through social media networks, distinguishing between fake and real information. Most smartphone users tend to read news on social media rather than on the internet. The information published on news websites often needs to authenticate. The simple spread of reports by way of sharing has included the exponential development of its misrepresentation. So, fake news has been a major issue ever since the web developed and expanded it to the general mass. This paper demonstrates several models and techniques for detecting false news by using different machine learning and natural language processing (NLP) models such as Logistic Regression, Decision Tree, Naïve Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bidirectional Encoder Representation from Transformers (BERT). We tried to combine the news, then find out if the information was authentic or fake. Various feature engineering methods such as Regex, Tokenization, stop words, Lemmatization, Term Frequency- Inverse Document Frequency (TF-IDF) generate feature vectors in this paper. Every Machine Learning and NLP model was evaluated with test data. For the machine learning model Logistic Regression, Decision Tree, Naïve Bayes, and SVM, we got 73.75%, 89.66%, 74.19%, and 76.65%, respectively. But the highest accuracy we git is for the NLP method, which is 95% for LSTM and 98% for the BERT language model.
dc.description.degreeUndergraduate
dc.identifier.print-thesis600000263
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1504
dc.language.isoen_US
dc.publisherNorth South University
dc.rights@ NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleDetection of Fake News using different Machine Learning and Natural Language Processing Algorithms
dc.typeProject
oaire.citation.endPage47
oaire.citation.startPage1
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
600000263-abstract.pdf
Size:
5.48 KB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
600000263.pdf
Size:
815.63 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.93 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Theses - Undergraduate