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Identification of tweets that mention books

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Abstract

We address the task of identifying tweets that mention books from amongst tweets that contain the same strings as book titles. Assuming the existence of a comprehensive list of book titles, this task can be defined as text classification targeting tweets that contain the same string as book titles. In carrying out the task, we need to exclude two types of tweets. The first is automatically posted, spam-like tweets that promote book sales or post recommendations (bot tweets). This type of tweets is excluded because we are developing an online surrogate to book exposure embedded within human communication on social media, and the results of the present task are to be used in this system. The second is tweets that contain the same string as book titles but are not about books (noise tweets). We proposed a two-step, machine learning-based pipeline consisting of bot filtering followed by noise reduction. Evaluation of experiments showed that our proposed method achieved an F1-score of 0.76, which is comparable to the best performance reported in similar tasks and sufficient as a first step for use in practical applications. We also analysed the detailed performance and errors, which suggested that the proposed method maintained an appropriate balance between precision and recall, and can be further improved by increasing the data size and taking into account word senses.

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Notes

  1. https://en.unesco.org/themes/literacy.

  2. For instance, Sullivan and Brown [64] showed an evidence for mathematics.

  3. The Telegraph on 11 September 2011 (https://bit.ly/2AVlW8J), and Independent on 21 February 2014 (https://ind.pn/2U5nlk9)

  4. In 1999, there were 22,296 bookstores in Japan, and the number had fallen to 13,488 by 2015 according to the survey of Almedia, Co., Ltd. (Tokyo, Japan).

  5. Traditionally, electronic advertisements have been intended for unintentional information display, although internet users usually perceive them as just annoying [1].

  6. Evidences of US are available in Twenge et al. [68] and The New Yorker which reports ‘fewer people were reading at all, a proportion falling from 26.3% of the population in 2003 to 19.5% in 2016’ based on American Time Use Survey by US Department of Labor (https://bit.ly/2lHvUCV). According to Statistics Bureau of Japan, the average number of people reading books for pleasure declines from 39.5 to 38.7% and average days per year spent for reading decreases from 94.6 to 79.7 days during 2011–2016.

  7. In this paper, italic strings within tweet examples denote actual book titles.

  8. F1-score (F) is defined by \(F = 2 \cdot P \cdot R / (P + R)\), or the harmonic mean of precision (P: the number of relevant documents divided by the number of retrieved documents) and recall (R: the number of relevant documents in the retrieved documents divided by the total number of relevant documents).

  9. https://www.librarything.com/.

  10. ProActive Information Retrieval (ProActIR) workshop (https://sites.google.com/site/proactir/) is an example of the popularity of this field.

  11. https://www.wikipedia.org/.

  12. https://developers.google.com/freebase/.

  13. This was formerly called BotOrNot [20, 71] (https://botometer.iuni.iu.edu/#!/).

  14. From the Twitter public stream, we collected 2258 tweets that contain the word ‘ (finished reading)’ in Japanese between 12 and 22 September 2016 and annotated a 10% sample (226 tweets). We obtained 211 (93%) mentions of book titles but only 122 (54%) author names and less than 100 other bibliographic fields.

  15. Since Twitter Inc. announced on 27 September 2017,Footnote 16 a tweet has been able to contain up to 280 characters for all languages except Japanese, Korean, and Chinese.

  16. http://bit.ly/2fQ2b7W.

  17. See, for example, https://www.goodreads.com/help/show/68-how-do-i-add-my-reading-updates-to-twitter.

  18. https://www.seleniumhq.org/.

  19. In Japanese, book titles are supposed to be enclosed by double quotation marks (i.e. ).

  20. Note that all values in every vector are normalised with its L2 norm.

  21. We adopted a popular normalisation scheme for Japanese informal texts (https://github.com/neologd/mecab-ipadic-neologd/wiki/Regexp).

  22. Some tweets use blank spaces and line breaks to be more readable.

  23. https://taku910.github.io/mecab/.

  24. This example does not consider any preprocessing which is typically applied in English text processing. Note that we target Japanese tweets.

  25. Webcat Plus is a unified bibliographic database run by the Japan National Institute of Informatics. http://webcatplus.nii.ac.jp/.

  26. We adopted implementations of scikit-learn [51] (v0.20.2) for both algorithms.

  27. Replacing the algorithms we adopted in this paper to simple NN models (e.g. multilayer perceptron and convolutional or recurrent-NN layers) resulted in no significant improvement.

  28. We used Webcat Plus which unifies the wide range of bibliographic databases, such as the national library (National Diet Library, Japan), university libraries, and commercial book catalogues in Japan. See also the footnote 25.

  29. Since some TMBs mention books to be published in the near future at the period of making the tweets, we used the book title list that contains books published by 2016.

  30. https://stream.twitter.com/1.1/statuses/sample.json.

  31. https://api.twitter.com/1.1/search/tweets.json.

  32. While the area under the curve is often calculated in receiver operating characteristic curves, precision–recall curves is preferred in imbalanced data sets [21, 56].

  33. https://rapidapi.com/OSoMe/api/botometer-pro.

  34. In this section, the term TMBs is mentioned in its general meaning.

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Acknowledgements

We thank Associate Professor Takeshi Abekawa for allowing us to use the Webcat Plus data. We show our appreciation for the annotator who labelled a part of our data set. Finally, we express our gratitude to all the reviewers for their thoughtful comments.

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Authors and Affiliations

  1. Graduate School of Education, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Shuntaro Yada

  2. Graduate School of Interdisciplinary Information Studies and Graduate School of Education, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Kyo Kageura

  3. Data61, The Commonwealth Scientific and Industrial Research Organisation, Corner Vimiera and Pembroke Roads, Marsfield, 2122, Australia

    Cecile Paris

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This research is supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP 16K12542 and JSPS Invitational Fellowships for Research in Japan S18111 (2018)

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Yada, S., Kageura, K. & Paris, C. Identification of tweets that mention books. Int J Digit Libr 21, 265–287 (2020). https://doi.org/10.1007/s00799-019-00273-4

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