skip to main content
10.1145/2505515.2505572acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

How the live web feels about events

Published: 27 October 2013 Publication History

Abstract

Microblogging platforms, such as Twitter, Tumblr etc., have been established as key components in the contemporary Web ecosystem. Users constantly post snippets of information regarding their actions, interests or perception of their surroundings, which is why they have been attributed the term Live Web. Nevertheless, research on such platforms has been quite limited when it comes to identifying events, but is rapidly gaining ground. Event identification is a key step to news reporting, proactive or reactive crisis management at multiple scales, efficient resource allocation, etc. In this paper, we focus on the problem of automatically identifying events as they occur, in such a user-driven, fast paced and voluminous setting. We propose a novel and natural way to address the issue using notions from emotional theories, combined with spatiotemporal information and employ online event detection mechanisms to solve it at large scale in a distributed fashion. We present a modular framework that incorporates all of our key ideas and experimentally validate its superiority, in terms of both efficiency and effectiveness, over the state-of-the-art using real life data from the Twitter stream. We also present empirical evidence on the importance of spatiotemporal information in event detection for this setting.

References

[1]
A. Ahmed, L. Hong, and A. J. Smola. Hierarchical geographical modeling of user locations from social media posts. WWW, 2013.
[2]
F. Alvanaki, S. Michel, K. Ramamritham, and G. Weikum. See what's enblogue: real-time emergent topic identification in social media. In EDBT, 2012.
[3]
B. Babcock, M. Datar, and R. Motwani. Sampling from a moving window over streaming data. In SODA, 2002.
[4]
L. Barbosa and J. Feng. Robust sentiment detection on twitter from biased and noisy data. COLING, 2010.
[5]
H. Becker, F. Chen, D. Iter, M. Naaman, and L. Gravano. Automatic identification and presentation of twitter content for planned events. In ICWSM, 2011.
[6]
H. Becker, D. Iter, M. Naaman, and L. Gravano. Identifying content for planned events across social media sites. In WSDM, 2012.
[7]
H. Becker, M. Naaman, and L. Gravano. Learning similarity metrics for event identification in social media. WSDM, 2010.
[8]
E. Benson, A. Haghighi, and R. Barzilay. Event discovery in social media feeds. In ACL-HLT, 2011.
[9]
J. Bollen, H. Mao, and A. Pepe. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In ICWSM, 2011.
[10]
D. Chakrabarti and K. Punera. Event summarization using tweets. In ICWSM, 2011.
[11]
J. Eisenstein, B. O'Connor, N. A. Smith, and E. P. Xing. A latent variable model for geographic lexical variation. In EMNLP, 2010.
[12]
P. Ekman, W. Friesen, and P. Ellsworth. Emotion in the human face: guide-lines for research and an integration of findings. Pergamon Press, 1972.
[13]
A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. Processing, 2009.
[14]
C. Grier, K. Thomas, V. Paxson, and M. Zhang. @spam: the underground on 140 characters or less. In CCS, 2010.
[15]
D. Gunopulos, G. Kollios, V. J. Tsotras, and C. Domeniconi. Approximating multi-dimensional aggregate range queries over real attributes. SIGMOD, 2000.
[16]
M. Gupta, P. Zhao, and J. Han. Evaluating event credibility on twitter. In SDM, 2012.
[17]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. SIGKDD Explor. Newsl., 11(1), nov 2009.
[18]
L. Hong, A. Ahmed, S. Gurumurthy, A. J. Smola, and K. Tsioutsiouliklis. Discovering geographical topics in the twitter stream. WWW, 2012.
[19]
N. Katariya, A. Iyer, and S. Sarawagi. Active evaluation of classifiers on large datasets. In ICDM, 2012.
[20]
J. Kleinberg. Bursty and hierarchical structure in streams. In SIGKDD, 2002.
[21]
T. Lansdall-Welfare, V. Lampos, and N. Cristianini. Effects of the recession on public mood in the uk. In WWW Companion, 2012.
[22]
T. Lappas, M. R. Vieira, D. Gunopulos, and V. J. Tsotras. On the spatiotemporal burstiness of terms. PVLDB, 5(9), 2012.
[23]
G. Leshed and J. J. Kaye. Understanding how bloggers feel: recognizing affect in blog posts. In CHI, 2006.
[24]
M. Lewis, J. Haviland-Jones, and L. Barrett. Handbook of Emotions, Third Edition. Guilford Publications, 2010.
[25]
C. Li, A. Sun, and A. Datta. Twevent: segment-based event detection from tweets. In CIKM, 2012.
[26]
B. M. Luisa Bentivogli, Pamela Forner and E. Pianta. Revising wordnet domains hierarchy: Semantics, coverage, and balancing. COLING, 2004.
[27]
M. Mathioudakis and N. Koudas. Twittermonitor: trend detection over the twitter stream. In SIGMOD, 2010.
[28]
M. Mikolajczak, V. Tran, C. Brotheridge, and J. J. Gross. Using an emotion regulation framework to predict the outcomes of emotional labour. Emerald, Bingley, UK, 2009.
[29]
G. Mishne and M. de Rijke. Capturing global mood levels using blog posts. In AAAI-CAAW, 2006.
[30]
J. Nichols, J. Mahmud, and C. Drews. Summarizing sporting events using twitter. In IUI, 2012.
[31]
M. Osborne, S. Petrovic, R. McCreadie, C. Macdonald, and I. Ounis. Bieber no more: First story detection using twitter and wikipedia.
[32]
B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), jan 2008.
[33]
J. W. Pennebaker and L. A. King. Linguistic styles: language use as an individual difference. Journal of personality and social psychology, 77(6), 1999.
[34]
A. Ritter, Mausam, O. Etzioni, and S. Clark. Open domain event extraction from twitter. SIGKDD, 2012.
[35]
T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In WWW, 2010.
[36]
J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling. Twitterstand: news in tweets. In SIGSPATIAL-GIS, 2009.
[37]
D. Scott. Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley series in probability and mathematical statistics: Applied probability and statistics. Wiley, 1992.
[38]
D. A. Shamma, L. Kennedy, and E. F. Churchill. Tweet the debates: understanding community annotation of uncollected sources. In WSM, 2009.
[39]
S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos. Online outlier detection in sensor data using non-parametric models. VLDB, 2006.
[40]
J. Sutton, L. Palen, and I. Shlovski. Back-channels on the front lines: Emerging use of social media in the 2007 southern california wildfires. 2008.
[41]
G. Valkanas and D. Gunopulos. Location extraction from social networks with commodity software and online data. In ICDM Workshops (SSTDM), 2012.
[42]
G. Valkanas and D. Gunopulos. A ui prototype for emotion-based event detection in the live web. In CHI-KDD, pages 89--100, 2013.
[43]
J. Vosecky, D. Jiang, and W. Ng. Limosa: a system for geographic user interest analysis in twitter. In EDBT, 2013.
[44]
J. Weng and B.-S. Lee. Event detection in twitter. In ICWSM, 2011.

Cited By

View all
  • (2024)Evaluating the Approach to Detecting and Monitoring Social Media Events to Combat Natural DisastersAutomatic Documentation and Mathematical Linguistics10.3103/S000510552470008058:2(117-128)Online publication date: 5-Jun-2024
  • (2022)Sem-TED: Semantic Twitter Event Detection and Adapting with News Stories2022 8th International Conference on Web Research (ICWR)10.1109/ICWR54782.2022.9786234(61-69)Online publication date: 11-May-2022
  • (2022)TedGram: Twitter Event Detection using Graphbased Methods2022 8th International Conference on Web Research (ICWR)10.1109/ICWR54782.2022.9786233(16-23)Online publication date: 11-May-2022
  • Show More Cited By

Index Terms

  1. How the live web feels about events

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
    October 2013
    2612 pages
    ISBN:9781450322638
    DOI:10.1145/2505515
    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: 27 October 2013

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. emotions
    2. event identification
    3. live web
    4. sentiment analysis

    Qualifiers

    • Research-article

    Conference

    CIKM'13
    Sponsor:
    CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
    October 27 - November 1, 2013
    California, San Francisco, USA

    Acceptance Rates

    CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

    Upcoming Conference

    CIKM '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)9
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 18 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Evaluating the Approach to Detecting and Monitoring Social Media Events to Combat Natural DisastersAutomatic Documentation and Mathematical Linguistics10.3103/S000510552470008058:2(117-128)Online publication date: 5-Jun-2024
    • (2022)Sem-TED: Semantic Twitter Event Detection and Adapting with News Stories2022 8th International Conference on Web Research (ICWR)10.1109/ICWR54782.2022.9786234(61-69)Online publication date: 11-May-2022
    • (2022)TedGram: Twitter Event Detection using Graphbased Methods2022 8th International Conference on Web Research (ICWR)10.1109/ICWR54782.2022.9786233(16-23)Online publication date: 11-May-2022
    • (2022)Predicting emotions in online social networks: challenges and opportunitiesMultimedia Tools and Applications10.1007/s11042-022-12345-w81:7(9567-9605)Online publication date: 1-Mar-2022
    • (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
    • (2020)Image Analysis Enhanced Event Detection from Geo-Tagged Tweet StreamsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_31(398-410)Online publication date: 11-May-2020
    • (2019)Multi-Modal Description of Public Safety Events Using Surveillance and Social MediaIEEE Transactions on Big Data10.1109/TBDATA.2017.26569185:4(529-539)Online publication date: 1-Dec-2019
    • (2018)Enhancing Local Live Tweet Stream to Detect NewsProceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3282866.3282868(1-10)Online publication date: 6-Nov-2018
    • (2018)Unified domain-specific language for collecting and processing data of social mediaJournal of Intelligent Information Systems10.1007/s10844-018-0508-551:2(389-414)Online publication date: 1-Oct-2018
    • (2017)Crowdsourcing-based timeline description of urban emergency events using social mediaInternational Journal of Ad Hoc and Ubiquitous Computing10.5555/3079766.307977025:1-2(41-51)Online publication date: 1-Jan-2017
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media