research-article

Eyewitness: identifying local events via space-time signals in twitter feeds

Authors:

Eric HorvitzAuthors Info & Claims

SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 20, Pages 1 - 10

https://doi.org/10.1145/2820783.2820801

Published: 03 November 2015 Publication History

Abstract

We present a methodology for automatically extracting and summarizing reports of significant local events from large-scale Twitter feeds. While previous work has relied on an analysis of tweet text to identify local events, we show how to reliably detect events using only time series analysis of geotagged tweet volumes from localized regions. The algorithm sweeps through different spatial and temporal resolutions and finds events as anomalous spikes in the rate of geotagged tweets. We applied the approach to a corpus of over 733 million geotagged tweets. Using a panel of 103 crowdsourced judges who tagged 2400 detected events, we achieved a local event detection precision of 70%. Using these judged events as ground truth, a decision tree classifier was able to raise the detection precision to 93%.

References

[1]

J. Teevan, D. Ramage, and M. R. Morris, "#TwitterSearch: a comparison of microblog search and web search," in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM 2011), 2011, pp. 35--44.

Digital Library

[2]

Twitter, "The 2014 #YearOnTwitter," in The Official Twitter Blog vol. 2014, ed, 2014.

[3]

C.-H. Lee, "Mining Spatio-Temporal Information on Microblogging Streams Using a Density-Based Online Clustering Method," Expert Systems with Applications, vol. 39, pp. 9623--9641, 2012.

Digital Library

[4]

C. A. Davis Jr, G. L. Pappa, D. R. R. de Oliveira, and F. de L Arcanjo, "Inferring the Location of Twitter Messages based on User Relationships," Transactions in GIS, vol. 15, pp. 735--751, 2011.

[5]

K. Watanabe, M. Ochi, M. Okabe, and R. Onai, "Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs," in 20th ACM International Conference on Information and Knowledge Management (CIKM 2011), Glasgow, UK, 2011, pp. 2541--2544.

Digital Library

[6]

C.-H. Lee, H.-C. Yang, T.-F. Chien, and W.-S. Wen, "A Novel Approach for Event Detection by Mining Spatio-Temporal Information on Microblogs," in International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011), 2011, pp. 254--259.

Digital Library

[7]

A. Ritter, O. Etzioni, and S. Clark, "Open Domain Event Extraction from Twitter," in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012, pp. 1104--1112.

Digital Library

[8]

C.-H. Lee, T.-F. Chien, and H.-C. Yang, "An Automatic Topic Ranking Approach for Event Detection on Microblogging Messages," in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, 2011, pp. 1358--1363.

[9]

A.-M. Popescu and M. Pennacchiotti, "Detecting Controversial Events from Twitter," in 19th ACM International Conference on Information and Knowledge Management (CIKM '10), 2010, pp. 1873--1876.

Digital Library

[10]

Z. Yin, L. Cao, J. Han, C. Zhai, and T. Huang, "Geographical Topic Discovery and Comparison," in Proceedings of the 20th International World Wide Web Conference (WWW 2011), 2011, pp. 247--256.

Digital Library

[11]

H. Samet, J. Sankaranarayanan, M. D. Lieberman, M. D. Adelfio, B. C. Fruin, J. M. Lotkowski, et al., "Reading News with Maps by Exploiting Spatial Synonyms," Communcations of the ACM, vol. 57, pp. 64--77, 2014.

Digital Library

[12]

X. Zhou and L. Chen, "Event Detection over Twitter Social Media Streams," The VLDB Journal---The International Journal on Very Large Data Bases, vol. 23, pp. 381--400, 2014.

Digital Library

[13]

T. Sakaki, M. Okazaki, and Y. Matsuo, "Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors," in 19th International Conference on World Wide Web (WWW '10), Raleigh, NC USA, 2010, pp. 851--860.

Digital Library

[14]

H. Abdelhaq, C. Sengstock, and M. Gertz, "EvenTweet: Online Localized Event Detection from Twitter," Proceedings of the VLDB Endowment, vol. 6, pp. 1326--1329 August 2013 2013.

Digital Library

[15]

Y. Hu, A. John, D. D. Seligmann, and F. Wang, "What Were the Tweets About? Topical Associations Between Public Events and Twitter Feeds," in 6th International Conference on Weblogs and Social Media (ICWSM-12), 2012.

[16]

A. S. Szalay, J. Gray, G. Fekete, P. Z. Kunszt, P. Kukol, and A. Thakar, "Indexing the Sphere with the Hierarchical Triangular Mesh," Microsoft Research, Redmond, WA USA, Technical Report MSR-TR-2005-123, August 2005 2005.

[17]

R. Lee and K. Sumiya, "Measuring Geographical Regularities of Crowd Behaviors for Twitter-Based Geo-Social Event Detection," in Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, 2010, pp. 1--10.

Digital Library

[18]

J. H. Friedman, "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics, vol. 29, pp. 1189--1232, 2001.

[19]

J. Chae, D. Thom, H. Bosch, Y. Jang, R. Maciejewski, D. S. Ebert, et al., "Spatiotemporal Social Media Analytics for Abnormal Event Eetection and Examination Using Seasonal-Trend Decomposition," in Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, 2012, pp. 143--152.

Digital Library

[20]

C. Li, A. Sun, and A. Datta, "Twevent: Segment-Based Event Detection from Tweets," in 21st ACM International Conference on Information and Knowledge Management, 2012, pp. 155--164.

Digital Library

[21]

H. Yin, B. Cui, H. Lu, Y. Huang, and J. Yao, "A Unified Model for Stable and Temporal Topic Detection from Social Media Data," in 29th IEEE International Conference on Data Engineering (ICDE 2013), 2013, pp. 661--672.

Digital Library

[22]

Q. Diao, J. Jiang, F. Zhu, and E.-P. Lim, "Finding Bursty Topics from Microblogs," in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, 2012, pp. 536--544.

Digital Library

[23]

A. Guille and C. Favre, "Mention-anomaly-based Event Detection and tracking in Twitter," in EEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014, pp. 375--382.

[24]

A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden, and R. C. Miller, "Twitinfo: Aggregating and Visualizing Microblogs for Event Exploration," in SIGCHI Conference on Human Factors in Computing Systems, 2011, pp. 227--236.

Digital Library

[25]

D. Inouye and J. K. Kalita, "Comparing Twitter Summarization Algorithms for Multiple Post Summaries," in 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011, pp. 298--306.

[26]

L. Vanderwende, H. Suzuki, C. Brockett, and A. Nenkova, "Beyond SumBasic: Task-Focused Summarization with Sentence Simplification and Lexical Expansion," Information Processing & Management, vol. 43, pp. 1606--1618, 2007.

Digital Library

[27]

M. Buhrmester, T. Kwang, and S. D. Gosling, "Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?," Perspectives on psychological science, vol. 6, pp. 3--5, 2011

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Kovalchuk MFilatova AKorneev AKoreneva MNasonov DVoskresenskii ABoukhanovsky A(2024)SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart CitySmart Cities10.3390/smartcities70501077:5(2763-2780)Online publication date: 28-Sep-2024
https://doi.org/10.3390/smartcities7050107
Ochiai KTerada MSano HHanashima MUsuda Y(2023)Anomaly Detection for Population Dynamics using Autoencoder Leveraging Periodic Residual Component in Disaster SituationsProceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3615886.3627739(34-42)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615886.3627739
Russell AValdez DChiang SMontemayor BBarry ALin HMassey P(2022)Using Natural Language Processing to Explore “Dry January” Posts on Twitter: Longitudinal Infodemiology StudyJournal of Medical Internet Research10.2196/4016024:11(e40160)Online publication date: 18-Nov-2022
https://doi.org/10.2196/40160
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Index Terms

Eyewitness: identifying local events via space-time signals in twitter feeds
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

cover image ACM Conferences

SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2015

646 pages

ISBN:9781450339674

DOI:10.1145/2820783

General Chairs:
Mohamed Ali
University of Washington, Tacoma
,
Yan Huang
University of North Texas
,
Program Chairs:
Michael Gertz
Heidelberg University, Germany
,
Matthias Renz
LMU Munich, Germany
,
Jagan Sankaranarayanan
NEC Labs

Copyright © 2015 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2015

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SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3 - 6, 2015

Washington, Seattle

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SIGSPATIAL '15 Paper Acceptance Rate 38 of 212 submissions, 18%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Kovalchuk MFilatova AKorneev AKoreneva MNasonov DVoskresenskii ABoukhanovsky A(2024)SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart CitySmart Cities10.3390/smartcities70501077:5(2763-2780)Online publication date: 28-Sep-2024
https://doi.org/10.3390/smartcities7050107
Ochiai KTerada MSano HHanashima MUsuda Y(2023)Anomaly Detection for Population Dynamics using Autoencoder Leveraging Periodic Residual Component in Disaster SituationsProceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3615886.3627739(34-42)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615886.3627739
Russell AValdez DChiang SMontemayor BBarry ALin HMassey P(2022)Using Natural Language Processing to Explore “Dry January” Posts on Twitter: Longitudinal Infodemiology StudyJournal of Medical Internet Research10.2196/4016024:11(e40160)Online publication date: 18-Nov-2022
https://doi.org/10.2196/40160
Ochiai KTerada MHanashima MSano HUsuda YRenz MSarwat M(2022)Detection of non-designated shelters by extracting population concentrated areas after a disaster (industrial paper)Proceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560985(1-9)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560985
Comito C(2022)How COVID-19 Information Spread in U.S.? The Role of Twitter as Early Indicator of EpidemicsIEEE Transactions on Services Computing10.1109/TSC.2021.309128115:3(1193-1205)Online publication date: 1-May-2022
https://doi.org/10.1109/TSC.2021.3091281
Choi DPark SHam DLim HBok KYoo J(2021)Local Event Detection Scheme by Analyzing Relevant Documents in Social NetworksApplied Sciences10.3390/app1102057711:2(577)Online publication date: 8-Jan-2021
https://doi.org/10.3390/app11020577
Filatova ANasonov D(2021)SeSAM: semi-automated semantic analysis method of urban areas’ events with extreme levels of popularity based on public open dataProcedia Computer Science10.1016/j.procs.2021.10.006193(52-61)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.10.006
Chen ZChen LCong GJensen C(2021)Location- and keyword-based querying of geo-textual data: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00661-w30:4(603-640)Online publication date: 30-Mar-2021
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Zhang CHan J(2021)Data Mining and Knowledge DiscoveryUrban Informatics10.1007/978-981-15-8983-6_42(797-814)Online publication date: 7-Apr-2021
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Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
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