Summary of Crowdsourced Adaptive Surveys, by Yamil Velez
Crowdsourced Adaptive Surveys
by Yamil Velez
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a crowdsourced adaptive survey methodology (CSAS) that combines natural language processing and adaptive algorithms to generate question banks that evolve with user input. This approach allows for the exploration of new survey questions while minimizing survey length. The method is applied to domains such as Latino information environments, national issue importance, and local politics, demonstrating its ability to identify topics that might otherwise be overlooked. The paper concludes by highlighting CSAS’s potential to bridge conceptual gaps between researchers and participants in survey research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to make surveys better. Traditional surveys can’t keep up with changing information and miss important opinions from smaller groups of people. The new method uses computers to analyze what people write and asks more questions based on their answers. This helps find important topics that might be missed. It’s useful for learning more about Latino communities, national issues, and local politics. |
Keywords
* Artificial intelligence * Natural language processing
