Improving Spam Detection on Online Social Media with hybrid classification techniques on Twitter platform
Rohini More
, Sunilkumar N. Jaiswal
Tweets spam, Classification, Feature Extraction, Naive Bayesian Classifier,Stanford Classifier
As the World Wide web is increasing day by day, tweets ar a reliable thanks to communicate and conjointly the fastest thanks to send information from one place to a special. Most transactions, whether or not or not they're a business or a business, use Twitter as a communications mode. Twitter is also a completely effective declare communication as a results of it helps in amount of your time communication, that saves time and cash. in addition to their edges, tweets have jointly been sick with spam attacks. Spam tweets ar typically accustomed send tweets in bulk to the sender. Spam will flood world wide web with many copies of comparable messages scattered at intervals the main points. These messages ar sent to unwanted recipients. we'll analyze information|the info|the information} mining ways for spam information throughout a spread of the thanks to obtain the foremost effective classification for Tweeting. As a region of this text, we'll describe the classification of Tweet to identify spam, not spam. For this reason, we've got an inclination to use the Naive theorem Classifier and build a speaker organization to exclude spam and not spam.
"Improving Spam Detection on Online Social Media with hybrid classification techniques on Twitter platform", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 6, page no.26 - 32, June-2018, Available :https://ijsdr.org/papers/IJSDR1806006.pdf
Volume 3
Issue 6,
June-2018
Pages : 26 - 32
Paper Reg. ID: IJSDR_180364
Published Paper Id: IJSDR1806006
Downloads: 000347362
Research Area: Engineering
Country: Aurangabad, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
