Difference between revisions of "Milestone 3 TuringMachine DarkHorseIdea: Social Network for Crowdsourcing"

From crowdresearch
Jump to: navigation, search
(Leaderboard, social Network for crowdsourcing)
(Background and Motivation)
Line 9: Line 9:
 
Based on brainstorming and analysis of various missing components in the existing crowdsourcing system, we draw our design from the social network theory. We propose a crowdsourcing system architecture surrounded by Artificial Intelligence and Machine Learning algorithms. Below diagram gives abstract overview of interactions between various human-human, machine-machine, and human-machine workflows. Furthermore, we zoom into a dark horse  specific component of the system i.e . Leaderboard.  
 
Based on brainstorming and analysis of various missing components in the existing crowdsourcing system, we draw our design from the social network theory. We propose a crowdsourcing system architecture surrounded by Artificial Intelligence and Machine Learning algorithms. Below diagram gives abstract overview of interactions between various human-human, machine-machine, and human-machine workflows. Furthermore, we zoom into a dark horse  specific component of the system i.e . Leaderboard.  
  
[[File:Process.png|800px|center|Class]]       
+
[[File:Process.png|900px|center|Class]]       
  
 
[[File:Crowdgaikwad.png|1002px|center|Class]]
 
[[File:Crowdgaikwad.png|1002px|center|Class]]

Revision as of 04:08, 19 March 2015

Leaderboard, social Network for crowdsourcing

Leveraging social network for ensuring Reward, Respect, and Recognition .

Background and Motivation

The Future of Work, Kittur etal 2013 and Genomes of Collective Intelligence Framework, Malone etal 2010 have shown that Reputation, Incentives, and Motivation play a big role in developing sustainable crowd sourcing communities. However, the question remains how do we motivate people for a long period of time and how do we build the trust? HCI research provides guidelines for developing sustainable online communities. In Building Successful Online Communities: Evidence-Based Social Design, Tausczik, Dabbish, and Kraut 2012 discuss the Identity and bond Based attachments.

Class

Based on brainstorming and analysis of various missing components in the existing crowdsourcing system, we draw our design from the social network theory. We propose a crowdsourcing system architecture surrounded by Artificial Intelligence and Machine Learning algorithms. Below diagram gives abstract overview of interactions between various human-human, machine-machine, and human-machine workflows. Furthermore, we zoom into a dark horse specific component of the system i.e . Leaderboard.

Class
Class

Leaderboard profile for Requestors (individual profile as well as group ranking)

  • How might we increase the reputation of requestors?
  • Motivate Requestors to be transparent and attract quality workers.
  • Workers can vote for top requestors who are providing clear instructions about the tasks and fairness. Borda Count Voting algorithm can be implanted to design the system; see Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Kleinberg
  • Create Requestors' profile highlighting their track-records. Make an announcement of top performers.
Top
  • Workers can trust the requestors who are high ranked and provide good value for their time
  • Requestors' reputation will help them attract new talent for accomplishing complex tasks
  • Requestors' reputation will them earn reward from the crowdsourcing system administrators
Top
  • How might we increase the reputation of workers?
  • Motivate workers by being recognized in the community
  • Provide incentive to reach to top using hierarchy


Top
  • Provide value to their commitment and ability to get things done
  • Requestors can trust the worker's profile based on making payments or recruiting them
Top