Exploring Crowdsourced Work in Low-Resource Settings

Chopra, Manu; Thies, Indrani Medhi; Pal, Joyojeet; Scott, Colin; Thies, William; Seshadri, Vivek

doi:10.1145/3290605.3300611

Cited by 17 publications

(2 citation statements)

References 22 publications

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“…Our work relates to paid crowdsourcing platforms that have targeted low-resources areas. Karya [7] and mClerk [11] disburse mobile micropayments in exchange for workers digitizing local-language text. ReSpeak [41] offers micropayments to workers for fresh narration of audio snippets, making them more suitable for automatic recognition and transcription.…”

Section: Related Workmentioning

confidence: 99%

Learn2Earn

Swaminathan

Thies

Mehta

et al. 2019

Proc. ACM Hum.-Comput. Interact.

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In rural parts of the developing world, spreading awareness about critical issues in health, governance, and other topics is challenging and costly. Traditional media such as print, radio and TV each have limitations and offer little guarantee that new information is absorbed or retained by the target population. This paper describes Learn2Earn, a system that leverages mobile payments to bolster public awareness campaigns in rural India. Users call an Interactive Voice Response (IVR) system, listen to a brief audio tutorial, and take a multiple-choice quiz to check their understanding. People who pass the quiz receive a mobile top-up (about $0.14) and have the opportunity to earn additional credits by referring others to the system. We describe a pilot deployment of Learn2Earn in rural India that spread via word-of-mouth to over 15,000 people within seven weeks. Usage was concentrated among young men, many of them students. In a mixed-methods study, we draw upon call logs, electronic surveys, qualitative interviews, and other sources of data to suggest that Learn2Earn could be an effective way to build awareness about important topics. CCS Concepts: • Human-centered computing → Human computer interaction (HCI).

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Section: Related Workmentioning

confidence: 99%

Learn2Earn

Swaminathan

Thies

Mehta

et al. 2019

Proc. ACM Hum.-Comput. Interact.

Self Cite

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“…Although the data quality can be a concern, promising results have shown otherwise. Chopra et al (2019) tested the quality of crowdsourced data in rural regions of In-dia, tasking individuals with the digitization of Hindi/Marathi handwritten documents. A 96.7% accuracy of annotation was yielded, proving that there is potential in this area.…”

Section: Crowdsourcingmentioning

confidence: 99%

Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities

Joshi,

Barnes,

Santy

et al. 2019

Preprint

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In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to lowresource language communities. While doing so, we bring to light the successes and failures of past work in this area, challenges being faced in doing so, and what they have achieved. Throughout this paper, we take a problem-facing approach and describe essential factors which the success of such technologies hinges upon. We present the various aspects in a manner which clarify and lay out the different tasks involved, which can aid organizations looking to make an impact in this area. We take the example of Gondi, an extremelylow resource Indian language, to reinforce and complement our discussion.

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