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A Distributed Framework for Early Trending Topics Detection on Big Social Networks Data Threads

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

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Abstract

Social networks have become big data production engines and their analytics can reveal insightful trending topics, such that hidden knowledge can be utilized in various applications and settings. This paper addresses the problem of popular topics’ and trends’ early prediction out of social networks data streams which demand distributed software architectures. Under an online time series classification model, which is implemented in a flexible and adaptive distributed framework, trending topics are detected. Emphasis is placed on the early detection process and on the performance of the proposed framework. The implemented framework builds on the lambda architecture design and the experimentation carried out highlights the usefulness of the proposed approach in early trends detection with high rates in performance and with a validation aligned with a popular microblogging service.

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Notes

  1. 1.

    https://twitter.com/whatstrending.

  2. 2.

    http://lambda-architecture.net/.

  3. 3.

    https://hadoop.apache.org/.

  4. 4.

    https://drill.apache.org/.

  5. 5.

    https://okeanos.grnet.gr.

  6. 6.

    http://spark.apache.org/.

  7. 7.

    https://flink.apache.org/.

References

  1. Arkaitz, Z.: Real-time classification of Twitter trends. J. Assoc. Inf. Sci. Technol. 66, 462–473 (2015)

    Article  Google Scholar 

  2. Salvatore, G., Lo Re, G., Morana, M.: A framework for real-time Twitter data analysis. Comput. Commun. 73, 236–242 (2016)

    Article  Google Scholar 

  3. Li, J.: Bursty event detection from microblog: a distributed and incremental approach. Concurrency Comput. Pract. Exp. 28, 3115–3130 (2015)

    Article  Google Scholar 

  4. Manirupa, D.: Towards methods for systematic research on big data. In: IEEE International Conference on Big Data (Big Data). IEEE (2015)

    Google Scholar 

  5. Giatsoglou, M., Chatzakou, D., Shah, N., Faloutsos, C., Vakali, A.: Retweeting activity on Twitter: signs of deception. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS, vol. 9077, pp. 122–134. Springer, Heidelberg (2015)

    Google Scholar 

  6. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the Nineteenth International WWW Conference. ACM (2010)

    Google Scholar 

  7. Stanislav, N., Chen, G., Shah, D.: A latent source model for nonparametric time series classification. In: Advances in Neural Information Processing Systems (2013)

    Google Scholar 

  8. Kontaki, M., Papadopoulos, A.N., Manolopoulos, Y.: Continuous trend-based classification of streaming time series. In: Eder, J., Haav, H.-M., Kalja, A., Penjam, J. (eds.) ADBIS 2005. LNCS, vol. 3631, pp. 294–308. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Szomszor, M., Kostkova, P., de Quincey, E.: #Swineflu: Twitter predicts swine flu outbreak in 2009. In: Szomszor, M., Kostkova, P. (eds.) e-Health. LNICST, vol. 69, pp. 18–26. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Lei, S.: Predicting US primary elections with Twitter. http://snap.stanford.edu/social2012/papers/shi.pdf. Accessed 2012

  11. Wang, Y.: To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter. arXiv:1603.08174 (2016)

  12. Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: SIGMOD ACM (2010)

    Google Scholar 

  13. Gorton, I., Klein, K.: Distribution, data, deployment: Software architecture convergence in big data systems. IEEE Softw. 32(3), 78–85 (2015)

    Article  Google Scholar 

  14. Tang, B.: A hierarchical distributed fog computing architecture for big data analysis in smart cities. In: Proceedings of the ASE BigData & SocialInformatics. ACM (2015)

    Google Scholar 

  15. Mariam, K.: Lambda architecture for cost-effective batch and speed big data processing. In: IEEE Big Data International Conference (2015)

    Google Scholar 

  16. Martínez-Prieto, M.: The solid architecture for real-time management of big semantic data. Future Gener. Comput. Syst. 47, 62–79 (2015)

    Article  Google Scholar 

  17. Marz, N.: Big data : principles and best practices of scalable realtime data systems. O’Reilly Media (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Informatics Department, Aristotle University, Thessaloniki, Greece

    Athena Vakali, Nikolaos Kitmeridis & Maria Panourgia

Authors
  1. Athena Vakali
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  2. Nikolaos Kitmeridis
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  3. Maria Panourgia
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Corresponding author

Correspondence to Athena Vakali .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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Vakali, A., Kitmeridis, N., Panourgia, M. (2017). A Distributed Framework for Early Trending Topics Detection on Big Social Networks Data Threads. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_20

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

  • eBook Packages: EngineeringEngineering (R0)

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