Skip to main content
SpringerLink
Log in

Exploiting Online Newspaper Articles Metadata for Profiling City Areas

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11872))

Abstract

News websites are among the most popular sources from which internet users read news articles. Such articles are often freely available and updated very frequently. Apart from the description of the specific news, these articles often contain metadata that can be automatically extracted and analyzed using data mining and machine learning techniques. In this work, we discuss how online news articles can be integrated as a further source of information in a framework for profiling city areas. We present some preliminary results considering online news articles related to the city of Rome. We characterize the different areas of Rome in terms of criminality, events, services, urban problems, decay and accidents. Profiles are identified using the k-means clustering algorithm. In order to offer better services to citizens and visitors, the profiles of the city areas may be a useful support for the decision making process of local administrations.

This work was partially supported by Tuscany Region, in the context of the projects Talent and Sibilla in the framework of regional program “FESR 2014–2020”, and by the Italian Ministry of Education and Research (MIUR), in the framework of the CrossLab project (Departments of Excellence).

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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.

    https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html.

  2. 2.

    http://www.foursquare.com/.

  3. 3.

    https://news.google.com/.

  4. 4.

    https://news.yahoo.com/.

  5. 5.

    http://www.today.it/.

  6. 6.

    https://www.primaonline.it/2019/02/01/284200/classifica-informazione-online-a-dicembre-su-dati-comscore/.

  7. 7.

    https://developers.google.com/maps/documentation/geocoding/start.

  8. 8.

    http://flask.pocoo.org.

  9. 9.

    https://scikit-learn.org/.

  10. 10.

    https://pandas.pydata.org/.

  11. 11.

    https://www.mongodb.com.

  12. 12.

    www.romatoday.it.

References

  1. Abdullah, M.S., Zainal, A., Maarof, M.A., Kassim, M.N.: Cyber-attack features for detecting cyber threat incidents from online news. In: 2018 Cyber Resilience Conference, pp. 1–4. IEEE (2018)

    Google Scholar 

  2. D’Andrea, E., Ducange, P., Loffreno, D., Marcelloni, F., Zaccone, T.: Smart profiling of city areas based on web data. In: 2018 IEEE International Conference on Smart Computing, pp. 226–233. IEEE (2018)

    Google Scholar 

  3. Giatsoglou, M., Chatzakou, D., Gkatziaki, V., Vakali, A., Anthopoulos, L.: Citypulse:a platform prototype for smart city social data mining. J. Knowl. Econ. 7, 344–372 (2016)

    Article  Google Scholar 

  4. Hussain, F.: Internet of Everything. In: Hussain, F. (ed.) Internet of Things, pp. 1–11. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55405-1_1

    Chapter  Google Scholar 

  5. Iglesias, J.A., Tiemblo, A., Ledezma, A., Sanchis, A.: Web news mining in an evolving framework. Inf. Fusion 28, 90–98 (2016)

    Article  Google Scholar 

  6. Karimi, M., Jannach, D., Jugovac, M.: News recommender systems-survey and roads ahead. Inf. Process. Manag. 54(6), 1203–1227 (2018)

    Article  Google Scholar 

  7. Lin, A.Y., Ford, J., Adar, E., Hecht, B.: VizByWiki: mining data visualizations from the web to enrich news articles. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 873–882. International World Wide Web Conference Committee (2018)

    Google Scholar 

  8. Po, L., Rollo, F.: Building an urban theft map by analyzing newspaper crime reports. In: 2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization, pp. 13–18. IEEE (2018)

    Google Scholar 

  9. Ramisa, A., Yan, F., Moreno-Noguer, F., Mikolajczyk, K.: BreakingNews: article annotation by image and text processing. IEEE Trans. Pattern Anal. Mach. Intell. 40(5), 1072–1085 (2018)

    Article  Google Scholar 

  10. Silva, B.N., Khan, M., Han, K.: Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 38, 697–713 (2018)

    Article  Google Scholar 

  11. Teske, A., Falcon, R., Abielmona, R., Petriu, E.: Automatic identification of maritime incidents from unstructured articles. In: 2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management, pp. 42–48. IEEE (2018)

    Google Scholar 

  12. Yang, D., Heaney, T., Tonon, A., Wang, L., Cudré-Mauroux, P.: Crimetelescope: crime hotspot prediction based on urban and social media data fusion. World Wide Web 21, 1323–1347 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Pisa, Largo L. Lazzarino 1, 56122, Pisa, Italy

    Livio Cascone, Pietro Ducange & Francesco Marcelloni

Authors
  1. Livio Cascone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pietro Ducange
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco Marcelloni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pietro Ducange .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cascone, L., Ducange, P., Marcelloni, F. (2019). Exploiting Online Newspaper Articles Metadata for Profiling City Areas. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33617-2_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33616-5

  • Online ISBN: 978-3-030-33617-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation