Results 21 to 30 of about 37,947 (280)

A Review on Fake News Detection

International Journal for Research in Applied Science and Engineering Technology, 2023
Abstract: The widespread increase of fake news, generated by both humans and machines, has negative impacts on both society and individuals, politically and socially. The fast-paced nature of social networks makes it difficult to promptly evaluate the reliability of news. Hence, there is a growing need for automated tools to detect fake news.
Adwait Bandal, Tushar Rane
openaire   +1 more source

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

IEEE Access, 2020
Fake reviews may mislead consumers. A large number of fake reviews will even cause huge property losses and public opinion crises. Therefore, it is necessary to detect and filter fake reviews.
Jingdong Wang, Haitao Kan, Fanqi Meng, Qizi Mu, Genhua Shi, Xixi Xiao   +5 more
doaj   +1 more source

A review of fake news detection approaches: A critical analysis of relevant studies and highlighting key challenges associated with the dataset, feature representation, and data fusion

Heliyon, 2023
Currently, social networks have become the main source to acquire news about current global affairs. However, fake news appears and spreads on social media daily.
Suhaib Kh Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub   +2 more
doaj   +1 more source

Fake Reviewer Groups’ Detection System

IOSR Journal of Computer Engineering, 2014
We have the cyber space occupied with most of the opinions, comments and reviews. We also see the use of opinions in decision making process of many organizations. Not only organizations use these reviews but also users use them to a great extent. So using this opportunity, many groups try to game this system by providing fake reviews.
Kolhe N.M., Joshi M.M., Jadhav A.B., Abhang P.D   +3 more
openaire   +1 more source

Fake Review Detection Using Machine Learning

Journal of Student Research, 2022
E-commerce and online shopping is a booming industry, especially during the COVID-19 pandemic. Online shopping allows customers to browse and purchase products from home using just a phone or laptop. Customers often never end up seeing their product in real life before the purchase, and are instead dependent on photos and descriptions uploaded by the ...
Aaryan Rustagi, Vajraang Padisetti, Suresh Subramaniam   +2 more
openaire   +1 more source

Research and Progress of Fake Product Review Identification [PDF]

Jisuanji gongcheng, 2019
Driven by economic interests,a large number of malicious users publish false comments containing untrue content to influence users' purchasing decisions,thereby promoting the sale of their own products or suppressing competitors,seriously disrupting the ...
ZHANG Lu
doaj   +1 more source

Fake Reviewer Group Detection in Online Review Systems [PDF]

2021 International Conference on Data Mining Workshops (ICDMW), 2021
Online review systems are important components in influencing customers' purchase decisions. To manipulate a product's reputation, many stores hire large numbers of people to produce fake reviews to mislead customers. Previous methods tackle this problem by detecting malicious individuals, ignoring the fact that the spam activities are often formed in ...
Cao, Chen, Li, Shihao, Yu, Shuo, Chen, Zhikui   +3 more
openaire   +2 more sources

Detecting Singleton Review Spammers Using Semantic Similarity

, 2016
Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews.
Blei D. M., Fei G., Feng S., Mihalcea R., Moghaddam S. A., Mukherjee A., Ott M., Sandulescu V., Zengin M.   +8 more
core   +1 more source

TeV Observations of Markarian 501 with the Milagrito Water Cherenkov Detector [PDF]

, 1999
The Milagrito water Cherenkov detector near Los Alamos, New Mexico, operated as a sky monitor at energies of a few TeV between February 1997 and May 1998 including the period of the strong, long-lasting 1997 flare of Markarian 501.
(The Milagro Collaboration), , A. Falcone, A. I. Mincer, A. J. Smith, A. Shoup, Aharonian F., B. L. Dingus, B. Shen, C. Espinoza, C. M. Hoffman, C. Sinnis, D. A. Williams, D. Berley, D. E. Dorfan, D. Evans, D. G. Coyne, G. B. Yodh, G. Gisler, G. W. Sullivan, I. Leonor, J. A. Goodman, J. E. McEnery, J. F. McCullough, J. M. Ryan, K. Wang, L. A. Kelley, L. Fleysher, M. F. Morales, M. L. Chen, M. M. Murray, M. McConnell, M. O. Wascko, M. Schneider, McCullough J. F., P. Nemethy, Protheroe R. J., R. Atkins, R. Fleysher, R. S. Delay, R. S. Miller, R. W. Ellsworth, S. Hugenberger, S. Westerhoff, T. J. Haines, T. N. Thompson, T. Tumer, T. Yang, W. Benbow   +47 more
core   +2 more sources

Fake review detection using transformer-based enhanced LSTM and RoBERTa

International Journal of Cognitive Computing in Engineering
Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews,
Rami Mohawesh, Haythem Bany Salameh, Yaser Jararweh, Mohannad Alkhalaileh, Sumbal Maqsood   +4 more
doaj   +1 more source