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Estimating Time to Event of Future Events Based on Linguistic Cues on Twitter

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Intelligent Natural Language Processing: Trends and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 740))

Abstract

Given a stream of Twitter messages about an event, we investigate the predictive power of features generated from words and temporal expressions in the messages to estimate the time to event (TTE). From labeled training data average TTE values of the predictive features are learned, so that when they occur in an event-related tweet the TTE estimate can be provided for that tweet. We utilize temporal logic rules and a historical context integration function to improve the TTE estimation precision. In experiments on football matches and music concerts we show that the estimates of the method are off by 4 and 10 h in terms of mean absolute error on average, respectively. We find that the type and size of the event affect the estimation quality. An out-of-domain test on music concerts shows that models and hyperparameters trained and optimized on football matches can be used to estimate the remaining time to concerts. Moreover, mixing in concert events in training improves the precision of the average football event estimate.

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Notes

  1. 1.

    http://www.twitter.com.

  2. 2.

    http://www.en.wikipedia.org/wiki/Project_X_Haren.

  3. 3.

    http://www.zapaday.com.

  4. 4.

    http://www.daybees.com/.

  5. 5.

    https://www.songkick.com/.

  6. 6.

    https://www.recordedfuture.com.

  7. 7.

    http://www.twiqs.nl.

  8. 8.

    Ajax Amsterdam (aja), Feyenoord Rotterdam (fey), PSV Eindhoven (psv), FC Twente (twe), AZ Alkmaar (az), and FC Utrecht (utr).

  9. 9.

    An analysis of the tweet distribution shows that the 8-day window captures about 98% of all tweets comprises by means of the hashtags that we used.

  10. 10.

    http://www.eredivisie.nl.

  11. 11.

    http://www.lastfm.com.

  12. 12.

    Not all temporal expressions generated by the rules will prove to be correct. Since incorrect items are unlikely to occur and therefore are harmless, we refrained from manually checking the resulting set.

  13. 13.

    Dates, which denote the complete year, month and day of the month, are presently not covered by our patterns but will be added in future.

  14. 14.

    Note that nog does occur on the list as part of various multi-word expressions. Examples are nog twee dagen ‘another two days’ and nog 10 min ‘10 more minutes’.

  15. 15.

    We used Heideltime tagger version 1.7 by enabling the interval tagger and configured NEWS type as genre.

  16. 16.

    This subset is used to optimize the hyper-parameters as well.

  17. 17.

    We used the OpenTaal flexievormen, basis-gekeurd, and basis-ongekeurd word lists from the URL: http://www.opentaal.org/bestanden/doc_download/18-woordenlijst-v-210g-bronbestanden-.

  18. 18.

    The foreign word list currently contains 9 entries: different, indeed, am, ever, field, indeed, more, none, or, wants.

  19. 19.

    http://www.geonames.org/.

  20. 20.

    Features that occur multiple times in a tweet are filtered to have just one occurrence of each feature in a tweet.

  21. 21.

    We used the gaussian_kde method from SciPy v0.14.0 URL: http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html.

  22. 22.

    https://www.facebook.com.

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Acknowledgements

This research was supported by the Dutch national programme COMMIT as part of the Infiniti project.

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Authors and Affiliations

  1. Centre for Language Studies, Radboud University, P.O. Box 9103, 6500 HD, Nijmegen, The Netherlands

    Ali Hürriyetoǧlu, Nelleke Oostdijk & Antal van den Bosch

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  1. Ali Hürriyetoǧlu
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  2. Nelleke Oostdijk
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  3. Antal van den Bosch
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Correspondence to Ali Hürriyetoǧlu .

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Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  2. Faculty of Computers and Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Faculty of Computers and Information, Ain Shams University, Cairo, Egypt

    Fahmy Tolba

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Hürriyetoǧlu, A., Oostdijk, N., van den Bosch, A. (2018). Estimating Time to Event of Future Events Based on Linguistic Cues on Twitter. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_5

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