• Bomma Sirichandana
• Kande.Hema
• Kukkala Sowmya

Crowd sourcing is increasingly being used as a means to tackle problems requiring human intelligence. With the ever growing worker base that aims to complete micro tasks on crowd sourcing platforms in exchange for financial gains, there is a need for stringent mechanisms to prevent exploitation of deployed tasks. Using the large data sets models are generated which produce small sets of data models automatically. Human will develop the data according to their capacity of thinking and supplied by domain expert. Non-domain experts are collectively formulating features while the crowd sourcing describes the new approach to the predictors; behavioral features are predictive by predictors based on the values of featured providers. Based on our analysis of the typical malicious activity, we define and identify different types of workers in the crowd, propose a method to measure malicious activity, and finally present guidelines for the efficient design of crowd sourced surveys.

Crowd sourcing; Microtasks; Online Surveys; User Behavior; Malicious Intent

"Understanding of Crowd Sourcing Signs of Behavioral Effects: Case Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.47-49, March-2019, Available :http://www.jetir.org/papers/JETIRAR06010.pdf

"Understanding of Crowd Sourcing Signs of Behavioral Effects: Case Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp47-49, March-2019, Available at : http://www.jetir.org/papers/JETIRAR06010.pdf