Journal of Informatics Electrical and Electronics Engineering, 2021, Vol. 02, Iss. 02, S. No. 004, pp. 1-4 ISSN (Online): 2582-7006 ISSN (Online) : 2582-7006 International Conference on Artificial Intelligence (ICAI-2021) 1 Journal of Informatics Electrical and Electronics Engineering (JIEEE) A2Z Journals A Study on Opinion Spamming: Fake Consumer Review Detection Aditya S. Bisht 1 , Manish M. Tripathi 2 1 M.Tech, Scholar, Department of Computer Science & Engineering, Integral University, Lucknow, India, 2 Associate Professor, Department of Computer Science & Engineering, Integral University, Lucknow, India, connect2asbisht@gmail.com 1 , mmt@iul.ac.in 2 How to cite this paper: A. S. Bisht, M. M. Tripathi (2021) A Study on Opinion Spam- ming: Fake Consumer Review Detection. Jour- nal of Informatics Electrical and Electronics Engineering, Vol. 02, Iss. 02, S. No. 004, pp. 1- 4, 2021. https://doi.org/10.54060/JIEEE/002.02.004 Received: 02/04/2021 Accepted: 23/05/2021 Published: 04/06/2021 Copyright © 2021 The Author(s). This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0 / Abstract Online audits are the most important wellsprings of data about client feelings and are considered the columns on which the standing of an association is assembled. From a client's viewpoint, audit data is vital to settle on an appropriate choice with respect to an online buy. Surveys are for the most part thought to be a fair-minded assessment of a person's very own involvement in an item, however, the fundamental truth about these audits recounts an alternate story. Spammers abuse these audit stages unlawfully on account of impetuses engaged with composing counterfeit surveys, subsequently at- tempting to acquire a bit of leeway over contenders bringing about an unstable devel- opment of assessment spamming. This training is known as Opinion (Review) Spam, where spammers control and toxic substance surveys (i.e., making phony, untruthful, or misleading audits) for benefit or gain. It has become a typical practice for individuals to discover and to understand assessments/surveys on the Web for some reasons. For in- stance, in the event that one needs to purchase an item, one commonly goes to a vendor or audit site (e.g., amazon.com) to peruse a few surveys of existing clients of the item. In the event that one sees numerous positive audits of the item, one is probably going to purchase the item. Notwithstanding, in the event that one sees many negative sur- veys, he/she will in all probability pick another item. Positive suppositions can bring about huge monetary benefits and additionally popularities for associations and people. This, sadly, offers great motivating forces for input spam. Most of the momentum re- search has zeroed in on regulated learning strategies, which require named information, a shortage with regards to online survey spam. Examination of techniques for Big Data is of revenue, since there are a huge number of online audits, with a lot seriously being produced every day. Until now, we have not discovered any papers that review the im- pacts of Big Data examination for survey spam identification. The essential objective of this paper is to give a solid and far-reaching similar investigation of flow research on identifying audit spam utilizing different AI procedures and to devise a strategy for di- recting further examination. Keywords Spam, Big data, machine learning, detection Open Access