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A syntax-based learning approach to geo-locating abnormal traffic events using social sensing

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Dong WangAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 663 - 670

https://doi.org/10.1145/3341161.3343708

Published: 15 January 2020 Publication History

Abstract

Social sensing has emerged as a new sensing paradigm to observe the physical world by exploring the "wisdom of crowd" on social media. This paper focuses on the abnormal traffic event localization problem using social media sensing. Two critical challenges exist in the state-of-the-arts: i) "content-only inference": the limited and unstructured content of a social media post provides little clue to accurately infer the locations of the reported traffic events; ii) "informal and scarce data": the language of the social media post (e.g., tweet) is informal and the number of the posts that report the abnormal traffic events is often quite small. To address the above challenges, we develop SyntaxLoc, a syntax-based probabilistic learning framework to accurately identify the location entities by exploring the syntax of social media content. We perform extensive experiments to evaluate the SyntaxLoc framework through real world case studies in both New York City and Los Angeles. Evaluation results demonstrate significant performance gains of the SyntaxLoc framework over state-of-the-art baselines in terms of accurately identifying the location entities that can be directly used to locate the abnormal traffic events.

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https://doi.org/10.1109/SMARTCOMP55677.2022.00022
Russo MMirulla GAvanzato RBeritelli F(2021)Car Social Network: Contact a Driver Through the License Plate2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS53288.2021.9660976(924-927)Online publication date: 22-Sep-2021
https://doi.org/10.1109/IDAACS53288.2021.9660976
Rashid MWang D(2020)CovidSens: a vision on reliable social sensing for COVID-19Artificial Intelligence Review10.1007/s10462-020-09852-3Online publication date: 12-Jun-2020
https://doi.org/10.1007/s10462-020-09852-3
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A syntax-based learning approach to geo-locating abnormal traffic events using social sensing
1. Human-centered computing
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    1. Interaction paradigms

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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 January 2020

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ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Shang LZhang YYe QWei NWang D(2022)SmartWaterSens: A Crowdsensing-based Approach to Groundwater Contamination Estimation2022 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP55677.2022.00022(48-55)Online publication date: Jun-2022
https://doi.org/10.1109/SMARTCOMP55677.2022.00022
Russo MMirulla GAvanzato RBeritelli F(2021)Car Social Network: Contact a Driver Through the License Plate2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS53288.2021.9660976(924-927)Online publication date: 22-Sep-2021
https://doi.org/10.1109/IDAACS53288.2021.9660976
Rashid MWang D(2020)CovidSens: a vision on reliable social sensing for COVID-19Artificial Intelligence Review10.1007/s10462-020-09852-3Online publication date: 12-Jun-2020
https://doi.org/10.1007/s10462-020-09852-3
Udgata SSuryadevara NUdgata SSuryadevara N(2020)COVID-19: Challenges and AdvisoryInternet of Things and Sensor Network for COVID-1910.1007/978-981-15-7654-6_1(1-17)Online publication date: 23-Jul-2020
https://doi.org/10.1007/978-981-15-7654-6_1
Rashid MZhang DWang D(2019)SocialCar: A Task Allocation Framework for Social Media Driven Vehicular Network Sensing Systems2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)10.1109/MSN48538.2019.00035(125-130)Online publication date: Dec-2019
https://doi.org/10.1109/MSN48538.2019.00035

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