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Efficient temporal mining of micro-blog texts and its application to event discovery

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Abstract

In this paper we present a novel method for clustering words in micro-blogs, based on the similarity of the related temporal series. Our technique, named SAX*, uses the Symbolic Aggregate ApproXimation algorithm to discretize the temporal series of terms into a small set of levels, leading to a string for each. We then define a subset of “interesting” strings, i.e. those representing patterns of collective attention. Sliding temporal windows are used to detect co-occurring clusters of tokens with the same or similar string. To assess the performance of the method we first tune the model parameters on a 2-month 1 % Twitter stream, during which a number of world-wide events of differing type and duration (sports, politics, disasters, health, and celebrities) occurred. Then, we evaluate the quality of all discovered events in a 1-year stream, “googling” with the most frequent cluster n-grams and manually assessing how many clusters correspond to published news in the same temporal slot. Finally, we perform a complexity evaluation and we compare SAX* with three alternative methods for event discovery. Our evaluation shows that SAX* is at least one order of magnitude less complex than other temporal and non-temporal approaches to micro-blog clustering.

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Notes

  1. http://trec.nist.gov/data/tweets/ sampled from Jan 23rd to Feb 8th, 2011.

  2. http://wordnet.princeton.edu.

  3. In any case a limit is fixed a priori for the number of topics.

  4. https://dev.twitter.com/docs/streaming-apis.

  5. Words are stemmed to reduce sparseness, even though, as discussed in the paper, this might not be strictly necessary with more dense Twitter streams. In what follows we will refer to clustered items interchangeably as words, stems, or tokens.

  6. http://code.google.com/p/jmotif/wiki/ZNormalization.

  7. http://en.wikipedia.org/wiki/2012.

  8. https://www.google.it/trends/.

  9. http://libalf.informatik.rwth-aachen.de/index.php?page=home.

  10. See Fig. 8 of the mentioned paper, in which 6 shapes of attention of Twitter hashtags are shown.

  11. For example, in many algorithms the number of cluster K is a parameter.

  12. We use the euclidean distance, but other measures, e.g. the edit distance, produce very similar results.

  13. For example, there are many available implementations of LDA.

  14. Some of the events shown in the related papers are world-wide, but several are local events, e.g. “Super Junior’s Yesung (@shfly3424) created his Twitter account”.

  15. https://github.com/lintool/twitter-tools/wiki/TREC-2013-Track-Guidelines.

  16. In what follows we omit the “big-o” notation for simplicity: complexity formulas are all to be interpreted as “order of”.

  17. [\(1{\ldots }B\)] in the original paper (Xie et al. 2013).

  18. Table I of (Xie et al. 2013).

  19. http://en.wikipedia.org/wiki/Computational_complexity_of_mathematical_operations.

  20. In agreement with http://firstmonday.org/ojs/index.php/fm/article/view/4366/3654.

  21. This is also confirmed by the fact that we noticed an increment of daily tweets from an average of 3.3M per day during May to 4.6M during August.

  22. www.foreignpolicy.com/articles/2014/09/26/why_big_data_missed_the_early_warning_signs_of_ebola.

  23. Here we show only one tweet for the sake of space, whereas in our testing dataset we retrieve 10–20 tweets.

  24. These events can easily be filtered out by a classifier, however teen events could be of interest.

  25. http://jgibblda.sourceforge.net/.

  26. http://code.google.com/p/btm/.

  27. Requests must be addressed to the authors.

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Authors and Affiliations

  1. Department of Computer Science, Sapienza University of Roma, Via Salaria 113, Rome, Italy

    Giovanni Stilo & Paola Velardi

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  1. Giovanni Stilo
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  2. Paola Velardi
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Correspondence to Paola Velardi.

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Responsible editor: Eamonn Keogh.

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Appendix

Appendix

See Table 6.

Table 6 “Summer 2014” experiments
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Stilo, G., Velardi, P. Efficient temporal mining of micro-blog texts and its application to event discovery. Data Min Knowl Disc 30, 372–402 (2016). https://doi.org/10.1007/s10618-015-0412-3

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