Skip to main content
SpringerLink
Log in

Tweet Length Matters: A Comparative Analysis on Topic Detection in Microblogs

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12657))

Included in the following conference series:

  • 2393 Accesses

Abstract

Microblogs are characterized as short and informal text; and therefore sparse and noisy. To understand topic semantics of short text, supervised and unsupervised methods are investigated, including traditional bag-of-words and deep learning-based models. However, the effectiveness of such methods are not together investigated in short-text topic detection. In this study, we provide a comparative analysis on topic detection in microblogs. We construct a tweet dataset based on the recent and important events worldwide, including the COVID-19 pandemic and BlackLivesMatter movement. We also analyze the effect of varying tweet length in both evaluation and training. Our results show that tweet length matters in terms of the effectiveness of a topic-detection method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The dataset can be accessed in https://github.com/avaapm/ECIR2021.

  2. 2.

    https://github.com/dennybritz/cnn-text-classification-tf.

References

  1. Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of SIGIR, pp. 37–45 (1998). https://doi.org/10.1145/290941.290954

  2. Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992). https://doi.org/10.1145/138859.138861

    Article  Google Scholar 

  3. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc., Newton (2009)

    MATH  Google Scholar 

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(1), 993–1022 (2003). https://doi.org/10.5555/944919.944937

    Article  MATH  Google Scholar 

  5. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017). https://doi.org/10.1162/tacl_a_00051

    Article  Google Scholar 

  6. Van Canneyt, S., Claeys, N., Dhoedt, B.: Topic-dependent sentiment classification on Twitter. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 441–446. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16354-3_48

    Chapter  Google Scholar 

  7. Cer, D., et al.: Universal sentence encoder for English. In: Proceedings of EMNLP: System Demonstrations, pp. 169–174 (2018). https://doi.org/10.18653/v1/D18-2029

  8. Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. In: Proceedings of EMNLP, pp. 670–680 (2017). https://doi.org/10.18653/v1/D17-1070

  9. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186 (2019). https://doi.org/10.18653/v1/N19-1423

  10. Fang, A., Ounis, I., Habel, P., Macdonald, C., Limsopatham, N.: Topic-centric classification of Twitter user’s political orientation. In: Proceedings of SIGIR, pp. 791–794 (2015). https://doi.org/10.1145/2766462.2767833

  11. Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext.zip: compressing text classification models. arXiv preprint arXiv:1612.03651 (2016)

  12. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of ACL, pp. 655–665 (2014). https://doi.org/10.3115/v1/P14-1062

  13. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of EMNLP, pp. 1746–1751 (2014). https://doi.org/10.3115/v1/D14-1181

  14. Li, Q., Shah, S., Liu, X., Nourbakhsh, A., Fang, R.: Tweetsift: tweet topic classification based on entity knowledge base and topic enhanced word embedding. In: Proceedings of CIKM, pp. 2429–2432 (2016). https://doi.org/10.1145/2983323.2983325

  15. Manning, C.D., Schütze, H., Raghavan, P.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008). https://doi.org/10.1017/CBO9780511809071

  16. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp. 3111–3119 (2013)

    Google Scholar 

  17. Onal, K.D., et al.: Neural information retrieval: at the end of the early years. Inf. Retrieval 21(2–3), 111–182 (2018). https://doi.org/10.1007/s10791-017-9321-y

    Article  Google Scholar 

  18. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  19. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of EMNLP, pp. 1532–1543 (2014). https://doi.org/10.3115/v1/D14-1162

  20. Ray Chowdhury, J., Caragea, C., Caragea, D.: Cross-lingual disaster-related multi-label tweet classification with manifold mixup. In: Proceedings of ACL: Student Research Workshop, pp. 292–298 (2020). https://doi.org/10.18653/v1/2020.acl-srw.39

  21. Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. In: NeurIPS EMC2 Workshop (2019)

    Google Scholar 

  22. Vaswani, A., et al.: Attention is all you need. In: Proceedings of NIPS, pp. 5998–6008 (2017)

    Google Scholar 

  23. Wolf, T., et al.: Huggingface’s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)

  24. Yuan, S., Wu, X., Xiang, Y.: Incorporating pre-training in long short-term memory networks for tweets classification. In: Proceedings of IEEE ICDM, pp. 1329–1334 (2016). https://doi.org/10.1109/ICDM.2016.0181

  25. Zeng, J., Li, J., Song, Y., Gao, C., Lyu, M.R., King, I.: Topic memory networks for short text classification. In: Proceedings of EMNLP, pp. 3120–3131 (2018). https://doi.org/10.18653/v1/D18-1351

Download references

Author information

Authors and Affiliations

  1. Aselsan Research Center, Ankara, Turkey

    Furkan Şahinuç & Cagri Toraman

Authors
  1. Furkan Şahinuç
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cagri Toraman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cagri Toraman .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse, Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Şahinuç, F., Toraman, C. (2021). Tweet Length Matters: A Comparative Analysis on Topic Detection in Microblogs. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72240-1_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72239-5

  • Online ISBN: 978-3-030-72240-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation