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Relevancer: Finding and Labeling Relevant Information in Tweet Collections

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10047))

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Abstract

We introduce a tool that supports knowledge workers who want to gain insights from a tweet collection, but due to time constraints cannot go over all tweets. Our system first pre-processes, de-duplicates, and clusters the tweets. The detected clusters are presented to the expert as so-called information threads. Subsequently, based on the information thread labels provided by the expert, a classifier is trained that can be used to classify additional tweets. As a case study, the tool is evaluated on a tweet collection based on the key terms ‘genocide’ and ‘Rohingya’. The average precision and recall of the classifier on six classes is 0.83 and 0.82 respectively. At this level of performance, experts can use the tool to manage tweet collections efficiently without missing much information.

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Notes

  1. 1.

    For example, https://wiki.ushahidi.com/display/WIKI/SwiftRiver, https://github.com/qcri-social/AIDR/wiki/AIDR-Overview, and https://github.com/JakobRogstadius/CrisisTracker.

  2. 2.

    https://bitbucket.org/hurrial/relevancer.

  3. 3.

    http://relevancer.science.ru.nl.

  4. 4.

    http://www.oed.com/view/Entry/71808.

  5. 5.

    https://dev.twitter.com/rest/public.

  6. 6.

    We used scikit-learn v0.17.1 for all machine learning tasks in this study http://scikit-learn.org.

  7. 7.

    The annotation is designed to be done or coordinated by a single person in our setting.

  8. 8.

    The expert may prefer to tolerate a few different tweets at the end of the group in case majority of the tweets are coherent and treat the cluster as coherent.

  9. 9.

    We note that the repetition pattern analysis is valuable in its own right. However, this information is not within the scope of the present study.

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Acknowledgements

This research was funded by the Dutch national research programme COMMIT. We gratefully acknowledge the contribution by Statistics Netherlands.

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Correspondence to Ali Hürriyetoǧlu .

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Hürriyetoǧlu, A., Gudehus, C., Oostdijk, N., van den Bosch, A. (2016). Relevancer: Finding and Labeling Relevant Information in Tweet Collections. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-47874-6_15

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-47874-6

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