Incentivizing social media users for mobile crowdsourcing

Panagiota Micholia, Merkouris Karaliopoulos, Iordanis Koutsopoulos, Luca Maria Aiello, Gianmarco Morales, Daniele Quercia

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

We focus on the problem of contributor-task matching in mobile crowd-sourcing. The idea is to identify existing social media users who possess domain expertise (e.g., photography) and incentivize them to perform some tasks (e.g., take quality pictures). To this end, we propose a framework that extracts the potential contributors' expertise based on their social media activity and determines incentives for them within the constraint of a budget. This framework does so by preferentially targeting contributors who are likely to offer quality content. We evaluate our framework on Flickr data for the entire city of Barcelona and show that it ensures high levels of task quality and wide geographic coverage, all without compromising fairness.

Original languageEnglish
Pages (from-to)4-13
Number of pages10
JournalInternational Journal of Human Computer Studies
Volume102
DOIs
Publication statusPublished - 1 Jun 2017

Fingerprint

Photography
social media
expertise
photography
fairness
budget
coverage
incentive

Keywords

  • Flickr
  • Incentives
  • Mobile crowdsourcing

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Software
  • Education
  • Engineering(all)
  • Human-Computer Interaction
  • Hardware and Architecture

Cite this

Incentivizing social media users for mobile crowdsourcing. / Micholia, Panagiota; Karaliopoulos, Merkouris; Koutsopoulos, Iordanis; Aiello, Luca Maria; Morales, Gianmarco; Quercia, Daniele.

In: International Journal of Human Computer Studies, Vol. 102, 01.06.2017, p. 4-13.

Research output: Contribution to journalArticle

Micholia, Panagiota ; Karaliopoulos, Merkouris ; Koutsopoulos, Iordanis ; Aiello, Luca Maria ; Morales, Gianmarco ; Quercia, Daniele. / Incentivizing social media users for mobile crowdsourcing. In: International Journal of Human Computer Studies. 2017 ; Vol. 102. pp. 4-13.
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