5.4 Crowdsourcing

Big Idea 5.4

• 2 min read

Big Idea5

Big Idea 5.4: Crowdsourcing

What is Crowdsourcing?

Definition: Method of collecting input from a large, diverse group of people (typically online)

Key Aspects:

  • Reduces computer bias through varied perspectives
  • Enables shared knowledge and resources
  • Uses distributed computing across locations

Types of Crowdsourcing

1. Crowdfunding

  • Purpose: Raise money from many small contributors
  • Examples: Kickstarter, GoFundMe
  • Use Case: Startup launching new product

2. Crowd Creation

  • Purpose: Collaborative content generation
  • Examples: Wikipedia, Threadless
  • Use Case: Company sourcing logo designs

3. Crowd Voting

  • Purpose: Collective decision-making
  • Examples: Reddit upvotes, talent show voting
  • Use Case: Choosing product features

4. Crowd Wisdom

  • Purpose: Aggregate knowledge
  • Examples: Stack Overflow, Quora
  • Use Case: Predicting market trends

Popcorn Hack 1

Question: What are the different types of crowdsourcing?
Answer:

  1. Crowdfunding - Raising money from many people (e.g., Kickstarter)
  2. Crowd Creation - Collaborative content making (e.g., Wikipedia)
  3. Crowd Voting - Collective decision systems (e.g., Reddit)
  4. Crowd Wisdom - Knowledge aggregation (e.g., Quora)

Data Crowdsourcing

Definition: Collecting data from public contributions

Examples:

  • Wikipedia (crowd-edited knowledge)
  • Google Maps (user-reported traffic)
  • Zooniverse (citizen science data)

Benefits: ✔ Fast data collection
✔ Diverse perspectives
✔ Cost-effective

Challenges: ❌ Data quality control
❌ Privacy concerns

Open Source Development

Characteristics:

  • Publicly accessible code
  • Community-driven improvements
  • Hosted on platforms like GitHub

Examples:

  1. Linux Operating System
  2. WordPress
  3. Apache HTTP Server

Ethical Considerations:

  • Maintainer burnout
  • License compliance

Popcorn Hack 2

Question: How does data crowdsourcing help open-source development?
Answer: Data crowdsourcing provides free, diverse datasets that fuel open-source projects:

  1. Kaggle Datasets - For machine learning
  2. Google Open Images - For computer vision
  3. NASA NEO - For climate research

Distributed Computing

Definition: Multiple computers working together over a network

Examples:

  • SETI@home (space research)
  • Bitcoin mining
  • Cloud computing (AWS)
Benefit Challenge
Scalability Network latency
Fault tolerance Security risks

Popcorn Hack 3

Question: How does distributed computing enable crowdsourcing?
Answer: Distributed computing enhances crowdsourcing by enabling large-scale data processing via multiple devices.

  1. SETI@home - Uses idle home computers for research
  2. Folding@home - Crowdsourced disease research
  3. Bitcoin - Distributed transaction verification

Homework Hack

Prompt: Discuss crowdsourcing’s impact (4+ sentences)

Sample Answer: Crowdsourcing revolutionizes innovation through mass collaboration. Wikipedia demonstrates crowd creation, while Kickstarter exemplifies crowdfunding. Distributed computing (like SETI@home) scales these efforts globally. Public datasets (Kaggle, NASA) enable open-source advancements. Future applications may include AI training and decentralized science.

College Board MCQs

2020 Q53 (Pet Finder)

Correct Answer: C (User-reported sightings)
Why? Classic crowdsourcing model

2018 Q58 (Internet Crowdsourcing)

Correct Answer: I & II only
Key Point: Internet enables but doesn’t eliminate computational limits

Key Takeaways

  • Crowdsourcing = Collective intelligence
  • 4 main types (funding, creation, voting, wisdom)
  • Distributed computing enables scale
  • Future potential in AI/blockchain