%0 Journal Article %1 noauthororeditor %A Sarita, Yadav %A Aakanksha, Saini %A Akanksha, Dhamija %A Yoganta, Narnauli %D 2016 %J Advances in Vision Computing: An International Journal (AVC) %K API’s Algorithm Bayes Facebook Honeypots K Naïve Networking SVM Simple Social Spam Twitter Weka clustering. means websites %N 2 %P 10 %R 10.5121/avc.2016.3201 %T DISCERNING SPAM IN SOCIAL NETWORKING SITES %U http://aircconline.com/avc/V3N2/3216avc01.pdf %V 3 %X Social Networking Sites, in the present scenario, are an amalgam of knowledge and spam. As their popularity surges among the users day by day so does it among the spammers looking at easy targets for their campaigns. The threat due to spams causing atrocious harm to the bandwidth, overloading the servers, spreading malicious pages online et cetera has increased manifold making it necessary for researchers to foray into this field of spam detection and reduce their effect on the various social networking sites. In this paper, we propose a framework for spam detection in the two largest social networking sites namely, Twitter and Facebook. We’ll be utilizing the data publically available on these two giants of social networking era. Initially, we’ll be citing the various approaches that have already been explored in this field. After that we’ll briefly explain the two methods that we used to collect the datasets from these websites.

@article{noauthororeditor, abstract = {Social Networking Sites, in the present scenario, are an amalgam of knowledge and spam. As their popularity surges among the users day by day so does it among the spammers looking at easy targets for their campaigns. The threat due to spams causing atrocious harm to the bandwidth, overloading the servers, spreading malicious pages online et cetera has increased manifold making it necessary for researchers to foray into this field of spam detection and reduce their effect on the various social networking sites. In this paper, we propose a framework for spam detection in the two largest social networking sites namely, Twitter and Facebook. We’ll be utilizing the data publically available on these two giants of social networking era. Initially, we’ll be citing the various approaches that have already been explored in this field. After that we’ll briefly explain the two methods that we used to collect the datasets from these websites. }, added-at = {2018-02-05T13:18:35.000+0100}, author = {Sarita, Yadav and Aakanksha, Saini and Akanksha, Dhamija and Yoganta, Narnauli}, biburl = {https://www.bibsonomy.org/bibtex/23746c1ad388dace9942e1c6d344b340a/vinston}, doi = {10.5121/avc.2016.3201}, interhash = {c5cf05fd2a0cd80b8eff7e187eb39d8f}, intrahash = {3746c1ad388dace9942e1c6d344b340a}, journal = {Advances in Vision Computing: An International Journal (AVC)}, keywords = {API’s Algorithm Bayes Facebook Honeypots K Naïve Networking SVM Simple Social Spam Twitter Weka clustering. means websites}, month = {June}, number = 2, pages = 10, timestamp = {2018-02-05T13:18:35.000+0100}, title = {DISCERNING SPAM IN SOCIAL NETWORKING SITES}, url = {http://aircconline.com/avc/V3N2/3216avc01.pdf}, volume = 3, year = 2016 }