In this paper, we are using different ML algorithms like Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and K-Nearest Neighbours (KNN). Along with these algorithms we have used two different normalization techniques such as Z-Score and Min-Max to improve accuracy...
Conroy NJ, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol 52(1):1–4 Google Scholar Granik M, Mesyura V (2017) Fake news detection using naive bayes classifier. In: 2017 IEEE first Ukraine conference on electrical and co...
Machine LearningFacebookEnsembleFake accountSybilSocial MediaOver the past few years, social networks have seen tremendous growth in users. There are 2.32 billion monthly active users (MAU) on the Facebook social networkdoi:10.2139/ssrn.3462933Singh, Yeshwant...
A Feature Extraction Approach for the Detection of Phishing Websites Using Machine Learning In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) ... SC Gundla,MP Karthik,MJK Reddy,... -...
The majority of the fake news detection models focus on resource-rich languages like English and Spanish. Due to lack of bench marked corpus, fake news detection in languages like Urdu and many Indian languages have garnered very little attention. However, few workshops and shared tasks are ...
Advancements in AI and machine learning mean that scammers are now leveraging generative AI and machine learning algorithms to create more convincing fake websites that can bypass traditional detection methods, making it harder for brands to identify and take down these fake sites. By identifying and...
Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques Fake news is a phenomenon which is having a significant impact on our social life, in particular in the political world. Fake news detection is an emerging... H Ahmed,I Traore,S Saad - International Conference...
to capture rich and contextual representations of news texts. By combining natural language understanding with transfer learning and context-based features, the proposed architectures aimed to enhance the detection of fake news. The experiments were conducted using the FakeNewsNet dataset. The results de...
Most work on misinformation detection uses manually labeled datasets that are hard to scale for building their predictive models. In this research, we overcome this challenge of data scarcity by proposing an automated approach for labeling data using verified fact-checked statements on a Twitter ...
Cross-modal Ambiguity Learning for Multimodal Fake News Detection 2022, WWW 2022 - Proceedings of the ACM Web Conference 2022 Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues 2021, MM 2021 - Proceedings of the 29th ACM International Conference on ...