Skip to main content
SpringerLink
Log in

Detecting Shifts in Public Opinion: A Big Data Study of Global News Content

  • Conference paper
  • First Online:
Advances in Intelligent Data Analysis XVII (IDA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11191))

Included in the following conference series:

Abstract

Rapid changes in public opinion have been observed in recent years about a number of issues, and some have attributed them to the emergence of a global online media sphere [1, 2]. Being able to monitor the global media sphere, for any sign of change, is an important task in politics, marketing and media analysis. Particularly interesting are sudden changes in the amount of attention and sentiment about an issue, and their temporal and geographic variations. In order to automatically monitor media content, to discover possible changes, we need to be able to access sentiment across various languages, and specifically for given entities or issues. We present a comparative study of sentiment in news content across several languages, assembling a new multilingual corpus and demonstrating that it is possible to detect variations in sentiment through machine translation. Then we apply the method on a number of real case studies, comparing changes in media coverage about Weinstein, Trump and Russia in the US, UK and some other EU countries.

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.nytimes.com/2017/10/05/us/harvey-weinstein-harassment-allegations.html.

References

  1. Tribou, A., Collins, K.: This is how fast America changes its mind, Bloomberg, 26 June [Online]. Available at: https://www.bloomberg.com/graphics/2015-pace-of-social-change/ (2015)

  2. Silver, N.: ’Change doesnt usually come this fast, FiveThirtyEight, 26 June [Online]. Available at: https://fivethirtyeight.com/features/change-doesnt-usually-come-this-fast/ (2015)

  3. Lansdall-Welfare, T., Sudhahar, S., Veltri, G. A., Cristianini, N.: On the coverage of science in the media: a big data study on the impact of the Fukushima disaster. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 60–66. IEEE (2014, October)

    Google Scholar 

  4. Maynard, D., Gossen, G., Funk, A., Fisichella, M.: Should I care about your opinion? Detection of opinion interestingness and dynamics in social media. Future Internet 6(3), 457–481 (2014)

    Article  Google Scholar 

  5. Mutz, D., Soss, J.: Reading public opinion: the influence of news coverage on perceptions of public sentiment. Public Opin. Q. 61(3), 431–451 (1997)

    Article  Google Scholar 

  6. Su, L.Y.F., Cacciatore, M.A., Liang, X., Brossard, D., Scheufele, D.A., Xenos, M.A.: Analyzing public sentiments online: combining human-and computer-based content analysis. Inf. Commun. Soc. 20(3), 406–427 (2017)

    Article  Google Scholar 

  7. Young, L., Soroka, S.: Affective news: the automated coding of sentiment in political texts. Polit. Commun. 29(2), 205–231 (2012)

    Article  Google Scholar 

  8. Nyman, R., Kapadia, S., Tuckett, D., Gregory, D., Ormerod, P., Smith, R.: News and narratives in financial systems: exploiting big data for systemic risk assessment. Bank of England Working Paper No. 704. Available at SSRN: https://ssrn.com/abstract=3135262 or https://doi.org/10.2139/ssrn.3135262 (2018)

  9. McLaren, L., Boomgaarden, H., Vliegenthart, R.: News coverage and public concern about immigration in britain. Int. J. Public Opin. Res. edw033 (2017)

    Google Scholar 

  10. Koehn, P., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 177–180. Association for Computational Linguistics (2007)

    Google Scholar 

  11. Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count: LIWC 2001. Mathway: Lawrence Erlbaum Associates, vol. 71 (2001)

    Google Scholar 

  12. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)

    Google Scholar 

  13. Flaounas, I., et al.: NOAM: news outlets analysis and monitoring system. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 1275–1278. ACM (2011)

    Google Scholar 

  14. Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 48–54. Association for Computational Linguistics (2003)

    Google Scholar 

  15. Jakarta, A.: Apache Lucene-a High-performance, Full-featured Text Search Engine Library. Apache Lucene (2004)

    Google Scholar 

  16. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. Icwsm 11(122–129), 1–2 (2010)

    Google Scholar 

  17. Wilson, T., et al.: OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, pp. 34–35. Association for Computational Linguistics (2005)

    Google Scholar 

  18. Gonzalez-Bailon, S., Banchs, R.E., Kaltenbrunner, A.: Emotional reactions and the pulse of public opinion: measuring the impact of political events on the sentiment of online discussions (2010). arXiv preprint arXiv:1009.4019

  19. Lin, Y.R., Margolin, D., Keegan, B., Lazer, D.: Voices of victory: a computational focus group framework for tracking opinion shift in real time. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 737–748. ACM (2013)

    Google Scholar 

  20. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  21. Balahur, A., Turchi, M.: Multilingual sentiment analysis using machine translation? In: Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, pp. 52–60. Association for Computational Linguistics (2012)

    Google Scholar 

  22. Balahur, A., Turchi, M.: Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Comput. Speech Lang. 28(1), 56–75 (2014)

    Article  Google Scholar 

  23. Banea, C., Mihalcea, R., Wiebe, J., Hassan, S.: Multilingual subjectivity analysis using machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 127–135. Association for Computational Linguistics (2008)

    Google Scholar 

  24. Bautin, M., Vijayarenu, L., Skiena, S.: International sentiment analysis for news and blogs. In: ICWSM (2008)

    Google Scholar 

  25. Bradley, M.M., Lang, P.J.: Affective norms for english words (ANEW): Stimuli, instruction manual and affective ratings. Technical report C-1, Gainesville, FL. The Center for Research in Psychophysiology, University of Florida (1999)

    Google Scholar 

  26. Flaounas, I., Lansdall-Welfare, T., Antonakaki, P., Cristianini, N.: The anatomy of a modular system for media content analysis (2014). arXiv preprint arXiv:1402.6208

  27. Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 160–167. Association for Computational Linguistics (2003)

    Google Scholar 

  28. Johnson, H., Martin, J., Foster, G., Kuhn, R.: Improving translation quality by discarding most of the phrasetable. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 967–975 (2007)

    Google Scholar 

  29. Malyon, E.: Donald Trump attacks ’disrespectful’ NFL decision to allow players to continue protests against racial inequality’, 18 October [Online]. Available at: https://www.independent.co.uk/sport/us-sport/national-football-league/donald-trump-nfl-players-protests-racial-inequality-kneel-anthem-colin-kaepernick-a8006806.html (2017)

  30. Jacobs, B., Laughland, O.: Charlottesville: trump reverts to blaming both sides including ‘violent alt-left’, 16 Aug [Online]. Available at: https://www.theguardian.com/us-news/2017/aug/15/donald-trump-press-conference-far-right-defends-charlottesville (2017)

  31. Wikipedia contributors: Annexation of Crimea by the Russian Federation’, (Mar 2014 [Online]. Available at: https://en.wikipedia.org/wiki/Annexation_of_Crimea_by_the_Russian_Federation (2014)

Download references

Acknowledgements

Saatviga Sudhahar and Nello Cristianini are supported by the ERC Advanced Grant “ThinkBig awarded to NC.

Author information

Authors and Affiliations

  1. University of Bristol, Bristol, UK

    Saatviga Sudhahar & Nello Cristianini

Authors
  1. Saatviga Sudhahar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nello Cristianini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saatviga Sudhahar .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Wouter Duivesteijn

  2. Department of Information and Computing Sciences, University Utrecht, Utrecht, The Netherlands

    Arno Siebes

  3. University of Helsinki, Helsinki, Finland

    Antti Ukkonen

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sudhahar, S., Cristianini, N. (2018). Detecting Shifts in Public Opinion: A Big Data Study of Global News Content. In: Duivesteijn, W., Siebes, A., Ukkonen, A. (eds) Advances in Intelligent Data Analysis XVII. IDA 2018. Lecture Notes in Computer Science(), vol 11191. Springer, Cham. https://doi.org/10.1007/978-3-030-01768-2_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01768-2_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01767-5

  • Online ISBN: 978-3-030-01768-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation