Spam Diffusion in Social Networking Media using Latent Dirichlet Allocation
Poonam Tanwar1, Priyanka2
1Dr. Poonam Tanwar Manav Rachna International Institute of Research and Studies, Faridabad.
2Ms. Priyanka Manav Rachna International Institute of Research and Studies, Faridabad.
Manuscript received on September 14, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 881-885 | Volume-8 Issue-12, October 2019. | Retrieval Number: I7898078919/2019©BEIESP| DOI: 10.35940/ijitee.I7898.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Like web spam has been a major threat to almost every aspect of the current World Wide Web, similarly social spam especially in information diffusion has led a serious threat to the utilities of online social media. To combat this challenge the significance and impact of such entities and content should be analyzed critically. In order to address this issue, this work usedTwitter as a case study and modeled the contents of information through topic modeling and coupled it with the user oriented feature to deal it with a good accuracy. Latent Dirichlet Allocation (LDA) a widely used topic modeling technique is applied to capture the latent topics from the tweets’ documents. The major contribution of this work is twofold: constructing the dataset which serves as the ground-truth for analyzing the diffusion dynamics of spam/non-spam information and analyzing the effects of topics over the diffusibility. Exhaustive experiments clearly reveal the variation in topics shared by the spam and nonspam tweets. The rise in popularity of online social networks, not only attracts legitimate users but also the spammers. Legitimate users use the services of OSNs for a good purpose i.e., maintaining the relations with friends/colleagues, sharing the information of interest, increasing the reach of their business through advertisings.
Keywords: Spam Detection, SVM, LDA, Social Networking, Twitter
Scope of the Article: Social Networking