research-article

Using stranger as sensors: temporal and geo-sensitive question answering via social media

Authors:

Todorka Alexandrova,

Tatsuo NakajimaAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 803 - 814

https://doi.org/10.1145/2488388.2488458

Published: 13 May 2013 Publication History

Abstract

MoboQ is a location-based real-time social question answering service deployed in the field in China. Using MoboQ, people can ask temporal and geo-sensitive questions, such as how long is the line at a popular business right now, and then receive answers that crowdsourced from other users in a timely fashion. To obtain answers for questions, the system analyzes the live stream from public microblogging service Sina Weibo to identify people who are likely to currently be at the place that is associated with a question and sends them the unsolicited question through the microblogging service from which they were identified. MoboQ was deployed in China at the beginning of 2012, until October of the same year, it was used to ask 15,224 questions by 35,214 registered users, and it gathered 29,491 answers; 74.6% of the questions received at least one answer, 28% received a first response within 10 minutes, and 51% of the questions got first answer within 20 minutes. In total, 91% of the questions successfully found at least one answer candidate, and they were sent to 162,954 microblogging service users. We analyze the usage patterns and behaviors of the real-world end-users, discuss the lessons learned, and outline the future directions and possible applications that could be built on top of MoboQ.

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Cited By

Lin FTsai PLee CHo YChen YYen YChang Y(2024)“I Prefer Regular Visitors to Answer My Questions”: Users’ Desired Experiential Background of Contributors for Location-based Crowdsourcing PlatformProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642520(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642520
Lin FLee CHo YChen YYen GChang Y(2022)What Kinds of Experiences Do You Desire? A Preliminary Study of the Desired Experiences of Contributors to Location-Based Mobile CrowdsourcingExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519744(1-7)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3519744
Hamrouni AGhazzai HFrikha MMassoud Y(2020)A Spatial Mobile Crowdsourcing Framework for Event ReportingIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.29675857:2(477-491)Online publication date: Apr-2020
https://doi.org/10.1109/TCSS.2020.2967585
Show More Cited By

Index Terms

Using stranger as sensors: temporal and geo-sensitive question answering via social media
1. Human-centered computing
2. Information systems
  1. Information systems applications

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cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Lin FTsai PLee CHo YChen YYen YChang Y(2024)“I Prefer Regular Visitors to Answer My Questions”: Users’ Desired Experiential Background of Contributors for Location-based Crowdsourcing PlatformProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642520(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642520
Lin FLee CHo YChen YYen GChang Y(2022)What Kinds of Experiences Do You Desire? A Preliminary Study of the Desired Experiences of Contributors to Location-Based Mobile CrowdsourcingExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519744(1-7)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3519744
Hamrouni AGhazzai HFrikha MMassoud Y(2020)A Spatial Mobile Crowdsourcing Framework for Event ReportingIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.29675857:2(477-491)Online publication date: Apr-2020
https://doi.org/10.1109/TCSS.2020.2967585
Xu ZLiu YYen NMei LLuo XWei XHu C(2020)Crowdsourcing Based Description of Urban Emergency Events Using Social Media Big DataIEEE Transactions on Cloud Computing10.1109/TCC.2016.25176388:2(387-397)Online publication date: 1-Apr-2020
https://doi.org/10.1109/TCC.2016.2517638
Kimura RNakajima T(2020)Collectively Sharing People’s Visual and Auditory Capabilities: Exploring Opportunities and PitfallsSN Computer Science10.1007/s42979-020-00313-w1:5Online publication date: 10-Sep-2020
https://doi.org/10.1007/s42979-020-00313-w
Rose JLehmann J(2020)CASQAD – A New Dataset for Context-Aware Spatial Question AnsweringThe Semantic Web – ISWC 202010.1007/978-3-030-62466-8_1(3-17)Online publication date: 2-Nov-2020
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Kimura RNakajima T(2020)Digitally Enhancing Society Through Structuralism: Virtualizing Collective Human Eyesight and Hearing Capabilities as a Case StudyDistributed, Ambient and Pervasive Interactions10.1007/978-3-030-50344-4_29(400-414)Online publication date: 19-Jul-2020
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Der Weth CAbdul AKashyap AKankanhalli M(2019)CloseUp—A Community-Driven Live Online Search EngineACM Transactions on Internet Technology10.1145/330144219:3(1-21)Online publication date: 27-Aug-2019
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Sun YTan W(2019)Combining Spatial Optimization and Multi-Agent Temporal Difference Learning for Task Assignment in Uncertain CrowdsourcingInformation Systems Frontiers10.1007/s10796-019-09938-6Online publication date: 10-Jul-2019
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Kimura RNakajima T(2019)A Ubiquitous Computing Platform for Virtualizing Collective Human Eyesight and Hearing CapabilitiesAmbient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence10.1007/978-3-030-24097-4_4(27-35)Online publication date: 23-Jun-2019
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