research-article

Identifying Disruptive Events from Social Media to Enhance Situational Awareness

Authors:

Nasser Alsaedi,

Omer RanaAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 934 - 941

https://doi.org/10.1145/2808797.2808879

Published: 25 August 2015 Publication History

Abstract

Decision makers use information from a range of terrestrial and online sources to help underpin the processes through which they develop policies and react to events as they unfold. One such source of online information is social media. Twitter, as a form of social media, is a popular micro-blogging Web application serving hundreds of millions of users. User-generated content can be exploited as a rich source of information for identifying 'real-world' disruptive events. In this paper, we present an in-depth comparison of three types of features that could be useful for identifying disruptive events: temporal, spatial and textual. We make several interesting observations: first, disruptive events are identifiable regardless of the "influence of the user" discussing them, and over a variety of topics. Second, temporal features are the best event identifiers and hence should not be disregarded or ignored. Third, a combination of optimum textual features with temporal and spatial features achieves best performance in the event detection task. We believe that these findings provide new insights for gathering information around real-world events as well as a useful resource for improving situational awareness and decision support.

References

[1]

Alsaedi, N., Burnap, P. and Rana, O. 2014. A Combined Classification-Clustering Framework for Identifying Disruptive Events. SocialCom 2014, pp. 1--10.

[2]

Julian E. Barnes (2014) U.S. Military Plugs Into Social Media for Intelligence Gathering, The wall street journal Politics and policy.

[3]

Oh, O., Agrawal, M. and Rao, H.R. 2011. Information control and terrorism: Tracking the Mumbai terrorist attack through twitter. Information Systems Frontiers 13(1), pp. 33--43.

Digital Library

[4]

Backstrom, L., Kleinberg, J., Lee, L. and Danescu, C. 2013. Characterizing and Curating Conversation Threads: Expansion, Focus, Volume, Re-entry, Wsdm, pp. 13--22.

Digital Library

[5]

Cheng, Z., Caverlee, J. and Lee, K. 2010. You are where you tweet: a content-based approach to geo-locating twitter users. CIKM '10 pp. 759--768.

Digital Library

[6]

Hecht, B., Hong, L., Suh, B. and Chi, E. 2011. Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. CHI '11, pp. 237--246.

Digital Library

[7]

Cha, M., Haddadi, H., Benevenuto, F. and Gummadi, P. 2010. Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM'10.

[8]

Ma, Z., Sun, A. and Cong, G. 2013. On predicting the popularity of newly emerging hashtags in twitter. Journal of the American Society for Information Science and Technology 64(7), pp. 1399--1410.

[9]

Thelwall, M., Buckley, K. and Paltoglou, G. 2011. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology 62(2), pp. 406--418.

Digital Library

[10]

Department of Homeland Security. 2014. Using Social Media for Enhanced Situational Awareness and Decision Support. Version 5.0. June 2013, pp. 1--44.

[11]

Cui, Y., Wong, W. and Cheung, D. 2009. Privacy-Preserving Clustering with High Accuracy and Low Time Complexity. DASFAA 2009, pp. 456--470.

Digital Library

[12]

Mills, A., Chen, R., Lee, J. and Rao, H.R. 2009. Web 2.0 emergency applications: how useful can Twitter be for emergency response? Journal of Information Privacy & Security 5(3), pp. 3--26.

[13]

Cheng, J., Adamic, L., Dow, P., Jon, K. and Jure, L. 2014. Can cascades be predicted? WWW '14.

Digital Library

[14]

Agarwal, A., Xie, B., Vovsha, I., Rambow, O. and Passonneau, R. 2011. Sentiment analysis of twitter data. Proceedings of the ACL 2011 Workshop on Languages in Social Media, pp. 30--38.

Digital Library

[15]

James, A. 2002. Introduction to topic detection and tracking. In Topic detection and tracking: event-based information, pp. 1--16.

Digital Library

[16]

Gabrilovich, E., Dumais, S. and Horvitz, E. 2004. Newsjunkie: providing personalized newsfeeds via analysis of information novelty. WWW '13, pp. 482--490.

Digital Library

[17]

Elsas, J.L. and Dumais, S.T. 2010. Leveraging temporal dynamics of document content in relevance ranking. WSDM '10.

Digital Library

[18]

Lee, K., Caverlee, J. and Webb, S. 2010. Uncovering social spammers: social honeypots+ machine learning. SIGIR '33.

Digital Library

[19]

Petrovic, S., Osborne, M. and Lavrenko, V. 2011. RT to Win! Predicting Message Propagation in Twitter. ICWSM'11.

[20]

Dou, W., Wang, X., Skau, D., Ribarsky, W. and Zhou, M.X. 2012. LeadLine: Interactive visual analysis of text data through event identification. VAST 2012, pp. 93--102.

Digital Library

[21]

Becker, H., Naaman, M. and Gravano, L. 2011. Beyond Trending Topics: Real-Event Identification on Twitter. ICWSM'11, pp. 1--17.

[22]

Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27(8), pp. 861--874.

Digital Library

[23]

Mahmud, J., Nichols, J. and Drews, C. 2012. Where Is This Tweet From? Inferring Home Locations of Twitter Users. ICWSM'12, pp. 511--514.

[24]

Atefeh, F. and Khreich, W. 2013. A Survey of techniques for event detection in twitter. Computational Intelligence 0(0).

Digital Library

[25]

Mitra, P., Murthy, C. a and Pal, S.K. 2002. Unsupervised Feature Selection Using Feature Similarity. PAMI 24(3), pp. 301--312.

Digital Library

[26]

Eisenstein, J., O'Connor, B., Noah, S. and Eric, X. 2010. A latent variable model for geographic lexical variation. EMNLP'10.

Digital Library

[27]

Tsur, O. and Rappoport, A. 2012. What' s in a Hashtag? Content based Prediction of the Spread of Ideas in Microblogging Communities. WSDM'12. pp. 16--23.

Digital Library

[28]

Burnap, P. and Williams, M. 2015. Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making, Policy & Internet (in press)

[29]

Burnap, P., Williams, M.L., Sloan, L., Rana, O., Housley, W., Edwards, A., Knight, V., Procter, R. and Voss, A. 2014, Tweeting the Terror: Modelling the Social Media Reaction to the Woolwich Terrorist Attack, Social Network Analysis and Mining 4:2

Cited By

Warnke SRunfola D(2024)Predicting Protests and Riots in Urban Environments With Satellite Imagery and Deep LearningTransactions in GIS10.1111/tgis.13236Online publication date: 30-Aug-2024
https://doi.org/10.1111/tgis.13236
Koh E(2022)Resilience analysis of infrastructure systems in incremental design changeComputers in Industry10.1016/j.compind.2022.103734142:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.compind.2022.103734
Chowdhury SBasu SMaulik U(2022)A survey on event and subevent detection from microblog data towards crisis managementInternational Journal of Data Science and Analytics10.1007/s41060-022-00335-y14:4(319-349)Online publication date: 10-Jun-2022
https://doi.org/10.1007/s41060-022-00335-y
Show More Cited By

Identifying Disruptive Events from Social Media to Enhance Situational Awareness
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information retrieval
  2. Information systems applications

Recommendations

Feature Extraction and Analysis for Identifying Disruptive Events from Social Media
ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Disruptive event identification is a concept that is crucial to ensuring public safety regarding large-scale events. Recent work on detecting events from social media shows that although these platforms are used for social purposes, they have been ...
Identifying content for planned events across social media sites
WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

User-contributed Web data contains rich and diverse information about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on ...
Nowcasting Events from the Social Web with Statistical Learning

We present a general methodology for inferring the occurrence and magnitude of an event or phenomenon by exploring the rich amount of unstructured textual information on the social part of the Web. Having geo-tagged user posts on the microblogging ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ASONAM '15

Sponsor:

SIGKDD

ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

Acceptance Rates

Overall Acceptance Rate 116 of 549 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
225
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Warnke SRunfola D(2024)Predicting Protests and Riots in Urban Environments With Satellite Imagery and Deep LearningTransactions in GIS10.1111/tgis.13236Online publication date: 30-Aug-2024
https://doi.org/10.1111/tgis.13236
Koh E(2022)Resilience analysis of infrastructure systems in incremental design changeComputers in Industry10.1016/j.compind.2022.103734142:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.compind.2022.103734
Chowdhury SBasu SMaulik U(2022)A survey on event and subevent detection from microblog data towards crisis managementInternational Journal of Data Science and Analytics10.1007/s41060-022-00335-y14:4(319-349)Online publication date: 10-Jun-2022
https://doi.org/10.1007/s41060-022-00335-y
Simon RIgnjatovic DNeubauer GGutschi CPan JVossner S(2021)Applying data mining techniques in the context of social media to improve situational awareness at large-scale events2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME52200.2021.9591154(1-6)Online publication date: 7-Oct-2021
https://doi.org/10.1109/ICECCME52200.2021.9591154
Weerasinghe SUdadeniya AWaduge Nde Zoysa RGanegoda U(2021)Novel Method to Analyze and Forecast Social Impact on Macro- and Micro-Economies Using Social Media DataProceedings of International Conference on Sustainable Expert Systems10.1007/978-981-33-4355-9_21(261-277)Online publication date: 31-Mar-2021
https://doi.org/10.1007/978-981-33-4355-9_21
Bonchi FBordino IGullo FStilo G(2019)The importance of unexpectedness: Discovering buzzing stories in anomalous temporal graphsWeb Intelligence10.3233/WEB-19041217:3(177-198)Online publication date: 16-Aug-2019
https://doi.org/10.3233/WEB-190412
Saeed ZAbbasi RMaqbool OSadaf ARazzak IDaud AAljohani NXu G(2019)What's Happening Around the World? A Survey and Framework on Event Detection Techniques on TwitterJournal of Grid Computing10.1007/s10723-019-09482-217:2(279-312)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10723-019-09482-2
Alsaedi NBurnap PRana O(2017)Can We Predict a Riot? Disruptive Event Detection Using TwitterACM Transactions on Internet Technology10.1145/299618317:2(1-26)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.1145/2996183
Alsaedi NBurnap PRana O(2016)Temporal TF-IDF: A High Performance Approach for Event Summarization in Twitter2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0087(515-521)Online publication date: Oct-2016
https://doi.org/10.1109/WI.2016.0087

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents