Milestone 6 NCPD

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Crowd Sourcing with Social Networking


Informal communication bridges the gap of differences between the worker’s and the requester’s perception of the task. However, existing crowdsourcing platforms do not give much importance to such interactions. Facilitating informal communication is an extremely powerful and underappreciated way to improve market outcomes. In recent times social networking has become the most common and easiest way for people to reach out to each other. The future of crowdsourcing can be envisaged as a workspace where the parties involved in the contract work interactively to successfully complete the task. In this paper, we propose a system which integrates the concept of social networking to crowdsourcing.


The interactions in current systems are limited and more machine oriented. As Six Silberman stated [], open informal communication helps workers better understand the context of a requester’s task, leading to better work. It helps requesters better understand worker’s processes and contexts, leading to better task design, higher work quality, and fairer pay and review processes. Such an informal communication can be achieved by adding social component to the crowdsourcing platform.

Related Work

As it’s rightly mentioned in [1], crowdsourcing, in the years to come, will definitely change the nature of current work and creativity. In the near future, the techniques involved in crowdsourcing will revolutionize the way work gets done by making use of the most rampant communication technologies to utilize the extent of talent that exists in the large pools of people. This leads to an implication that an upcoming crowdsourcing platform will be required to have an amiable nature when it comes to the relationship between the workers and requesters, which can be precisely achieved by maintaining some means for an informal communication, or employing the right people and giving them the right incentives. Stewart et al [2] proposes a developed version of a crowdsourcing model called SCOUT for describing the worker/requester participation on quantifiable effort-level metrics. There are three components to this model – super contributors, contributors and outliers. It focuses on an individualistic community where individual participants are engaged in the translation task. The setup was carried in an enterprise yielding good results. On similar lines, our work tries to extend this in a different perspective where we maintain portfolios for workers and requestors, with information about their performance and rating.


Requesters and workers need to have trust in each other and on the platform in order to achieve better results. But building trust amongst each other is one of the biggest challenges faced in crowdsourcing. One of the ways to achieve this trust is by having accounts of requesters and workers as portfolios. Also, at times, workers have to deal with technical issues which requesters did not plan for. Such issues need to be resolved quickly for the work to be completed with high quality results. The best way to communicate is to have a chatting mechanism as seen in many social networking sites [4]. This will help in better understanding of the problem or the context and the problem to be resolved in real time.


One-to-one communication between the requester and the worker will now transform into many-to-many. This provides a channel of communication for every individual who is a part of the crowdsourcing platform. Hence forming a social network helps in giving feedbacks, rate and recommend profiles.

Current Crowdsourcing
Crowdsourcing with social networking

The system works by building the profile over time based on the ratings as explained as follows [5]. Once a task is successfully completed by the worker, the requester can rate the work, which will be added to the worker’s portfolio. This will help in improving the worker’s profile so that other requesters can trust him, which in turn results in more opportunities. Same goes for the requester as well. The worker can rate the requester on the grounds of the work allotted to him and how the requester’s attitude was towards the worker. Also, mentioning if he was satisfied with the pay he received for the work. This will help create a better impression of both requester and worker in public. This will also motivate the workers and requesters perform better to improve their profile.


The problem this system tries to solve is to improve the communication amongst the requesters and workers to obtain better working environment. As the network grows, the ratings help to identify different roles among the requesters and the workers. These roles may be a reviewer, helper and many more. Each of these will then help to review the workers’ task, help the fellow worker and provide a feedback for all the individuals in the system. At this stage, we can say that the system has rightly reached the end goal.


[1] Jeff, Howe. "Crowdsourcing: Why the power of the crowd is driving the future of business." (2009). [2] Stewart, Osamuyimen, David Lubensky, and Juan M. Huerta. "Crowdsourcing participation inequality: a SCOUT model for the enterprise domain."Proceedings of the ACM SIGKDD Workshop on Human Computation. ACM, 2010. [3] [4] [5]