Skip to main content
Springer Nature Link
Log in

Analysing Protest-Related Tweets: An Evaluation of Techniques by the Open Source Intelligence Team

  • Conference paper
  • First Online:
Advances in Information and Communication (FICC 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 920))

Included in the following conference series:

  • 754 Accesses

Abstract

The police’s Open Source Intelligence (OSINT) team is constantly looking for better ways to analyze large corpora with high precision. This paper presents the evaluation by the OSINT team of more advanced methods than currently used to extract information from tweets related to an upcoming large-scale demonstration. An initial interview revealed the current way of working. Next, more advanced machine learning techniques such as sentiment analysis and network analysis are used to create visualizations that would better suit OSINT’s needs. Finally, our proposed visualizations are evaluated by two OSINT analysts who state that the results are clear, actionable, and relevant, whereas completeness of information and privacy pose additional challenges.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. fastText. https://fasttext.cc/. Accessed 12 July 2023

  2. AD. Verkeershinder verwacht op 11 maart vanwege demonstraties (2023). https://www.ad.nl/den-haag/verkeershinder-verwacht-op-11-maart-vanwege-demonstraties~a17f2841/. Accessed 11 Mar 2023

  3. Alfano, M., Reimann, R., Quintana, I.O., Chan, A., Cheong, M., Klein, C.: The affiliative use of emoji and hashtags in the Black Lives Matter movement in Twitter. Soc. Sci. Comput. Rev., 08944393221131928 (2022)

    Google Scholar 

  4. Amiri, B., Karimianghadim, R., Yazdanjue, N., Hossain, L.: Research topics and trends of the hashtag recommendation domain. Scientometrics 126, 2689–2735 (2021)

    Article  Google Scholar 

  5. Bauermeister, M.R.: Social capital and collective identity in the local food movement. Int. J. Agric. Sustain. 14(2), 123–141 (2016)

    Google Scholar 

  6. Berard, B.: I second that emoji: the standards, structures, and social production of emoji. First Monday (2018)

    Google Scholar 

  7. Biggs, M.: Size matters: quantifying protest by counting participants. Soc. Methods Res. 47(3), 351–383 (2018)

    Article  MathSciNet  Google Scholar 

  8. Burch, L.M., Frederick, E.L., Pegoraro, A.: Kissing in the carnage: an examination of framing on Twitter during the Vancouver riots. J. Broadcast. Electron. Media 59(3), 399–415 (2015)

    Article  Google Scholar 

  9. Chen, Y., Yuan, J., You, Q., Luo, J.: Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 117–125 (2018)

    Google Scholar 

  10. DVHN: Den Haag zet zich schrap: stad dreigt op 11 maart volledig vast te lopen door protesten, 6 March 2023. https://dvhn.nl/binnenland/Den-Haag-zet-zich-schrap-stad-dreigt-op-11-maart-volledig-vast-te-lopen-door-protesten-28282312.html. Accessed 12 July 2023

  11. Fede, H., Herrera, I., Mahdi Seyednezhad, S.M., Menezes, R.: Representing emoji usage using directed networks: a twitter case study. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds.) COMPLEX NETWORKS 2017 2017. SCI, vol. 689, pp. 829–842. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72150-7_67

    Chapter  Google Scholar 

  12. Gerlitz, C., Rieder, B.: Mining one percent of Twitter: collections, baselines, sampling. M/C J. 16(2) (2013)

    Google Scholar 

  13. Giugni, M.: How social movements matter: past research, present problems, future developments. How Social Movements Matter, pp. xiii–xxxiii (1999)

    Google Scholar 

  14. Rakibul Hasan, Md., Maliha, M., Arifuzzaman, M.: Sentiment analysis with NLP on Twitter data. In: 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), pp. 1–4. IEEE (2019)

    Google Scholar 

  15. Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 57–58 (2011)

    Google Scholar 

  16. Jongeneel, M.: Burgemeester Jan van Zanen (VVD) bereidt zich voor op mogelijke verkeers- en protestchaos op 11 maart in Den Haag (2023). https://www.dagelijksestandaard.nl/binnenland/burgemeester-jan-van-zanen-vvd-bereidt-zich-voor-op-mogelijke-verkeers-en-protestchaos-op-11-maart-in-den-haag. Accessed 12 July 2023

  17. Kejriwal, M., Wang, Q., Li, H., Wang, L.: An empirical study of emoji usage on Twitter in linguistic and national contexts. Online Soc. Netw. Media 24, 100149 (2021)

    Article  Google Scholar 

  18. Kimura-Thollander, P., Kumar, N.: Examining the “global” language of emojis: designing for cultural representation. In: Proceedings of the 2019 CHI conference on Human Factors in Computing Systems, pp. 1–14 (2019)

    Google Scholar 

  19. Kywe, S.M., Hoang, T.-A., Lim, E.-P., Zhu, F.: On recommending hashtags in twitter networks. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 337–350. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35386-4_25

    Chapter  Google Scholar 

  20. Lashari, I.A., Wiil, U.K.: Monitoring public opinion by measuring the sentiment of retweets on Twitter. In: 3rd European Conference on Social Media, pp. 153–161. Academic Conferences and Publishing International (2016)

    Google Scholar 

  21. Li, M., Ch’ng, E., Chong, A.Y.L., See, S.: Multi-class Twitter sentiment classification with emojis. Ind. Manage. Data Syst. 118, 1804–1820 (2018)

    Article  Google Scholar 

  22. Liu, Y., Kliman-Silver, C., Mislove, A.: The tweets they are a-Changin’: evolution of Twitter users and behavior. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

  23. Ljubešić, N., Fišer, D.: A global analysis of emoji usage. In: Proceedings of the 10th Web as Corpus Workshop, pp. 82–89 (2016)

    Google Scholar 

  24. Meijer, A.J.: Politie en sociale media. Van hype naar onderbouwde keuzen. Reed Business Information (2013)

    Google Scholar 

  25. Meyer, D.S., Tarrow, S.: A movement society: contentious politics for a new century. In: The Social Movement Society: Contentious Politics for a New Century, pp. 1–28 (1998)

    Google Scholar 

  26. RTL Nieuws: Den Haag wacht gespannen protestdag met boeren en klimaatactivisten: Vijf vragen, 11 March 2023. https://www.rtlnieuws.nl/nieuws/nederland/artikel/5370786/demonstranten-extinction-rebellion-a12-boerenprotest. Accessed 11 Mar 2023

  27. NRC: Farmers Defence Force wil op 11 maart weer demonstreren in Den Haag, 8 February 2023. https://www.nrc.nl/nieuws/2023/02/08/farmers-defence-force-wil-op-11-maart-weer-demonstreren-in-den-haag-a4156582. Accessed 12 July 2023

  28. Utrecht Universiy Institute of Information and Computing Sciences. Ethics and Privacy (2023). https://www.uu.nl/en/research/institute-of-information-and-computing-sciences/ethics-and-privacy. Accessed 21 July 2023

  29. Procter, R., Vis, F., Voss, A.: Reading the riots on Twitter: methodological innovation for the analysis of big data. Int. J. Soc. Res. Methodol. 16(3), 197–214 (2013)

    Article  Google Scholar 

  30. Rachman, F.H., et al.: Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regression. In: 2020 6th Information Technology International Seminar (ITIS), pp. 238–242. IEEE (2020)

    Google Scholar 

  31. Ranney, K.R.: Social media use and collective identity within the occupy movement. Ph.D. thesis, Honolulu, University of Hawaii at Manoa (2014)

    Google Scholar 

  32. Rivers. C.M., Lewis, B.L.: Ethical research standards in a world of big data. F1000Research 3, 38 (2014)

    Google Scholar 

  33. Sabucedo, J.-M., Gómez-Román, C., Alzate, M., van Stekelenburg, J., Klandermans, B.: Comparing protests and demonstrators in times of austerity: regular and occasional protesters in universalistic and particularistic mobilisations. Soc. Mov. Stud. 16(6), 704–720 (2017)

    Article  Google Scholar 

  34. Schäfer, M.T., Franzke, A., Utrecht, G., Fransen, R.: De ethische data assistent (DEDA) (2022). https://deda.dataschool.nl/wp-content/uploads/sites/415/2022/11/DEDA-NL.handbook.V3.1.pdf

  35. Schermer, B.W., Hagenauw, D., Falot, N.: Handleiding Algemene verordening gegevensbescherming en Uitvoeringswet Algemene verordening gegevensbescherming, 22 January 2018. https://www.rijksoverheid.nl/onderwerpen/privacy-en-persoonsgegevens/documenten/rapporten/2018/01/22/handleiding-algemene-verordening-gegevensbescherming. Accessed 16 Feb 2023

  36. Singh, S., Kumar, K., Kumar, B.: Sentiment analysis of Twitter data using TF-IDF and machine learning techniques. In: 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), vol. 1, pp. 252–255. IEEE (2022)

    Google Scholar 

  37. Sloan, L., Morgan, J., Burnap, P., Williams, M.: Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS ONE 10(3), e0115545 (2015)

    Article  Google Scholar 

  38. Soule, S.A., Olzak, S.: When do movements matter? The politics of contingency and the equal rights amendment. Am. Sociol. Rev. 69(4), 473–497 (2004)

    Article  Google Scholar 

  39. Tonkin, E., Pfeiffer, H.D., Tourte, G.: Twitter, information sharing and the London riots? Bull. Am. Soc. Inf. Sci. Technol. 38(2), 49–57 (2012)

    Article  Google Scholar 

  40. Utrecht University: Research Data Management Support (2023). https://www.uu.nl/en/research/research-data-management/guides/policies-codes-of-conduct-and-laws. Accessed 12 July 2023

  41. van Nederland, H.: Boeren kondigen ‘grootste demonstratie ooit’ aan op 11 maart in Den Haag, 8 February 2023. https://www.hartvannederland.nl/nieuws/politiek/boeren-kondigen-grootste-demonstratie-ooit-aan-op-11-maart-in-den-haag. Accessed 12 July 2023

  42. VRT: Duizenden boeren protesteren in Den Haag tegen stikstofbeleid, 700 klimaatactivisten opgepakt na protestactie op snelweg, 11 March 2023. https://www.vrt.be/vrtnws/nl/2023/03/11/ondanks-verbod-trekken-tractoren-in-kolonne-naar-den-haag-voor-p/. Accessed 21 July 2023

  43. Webb, H., et al.: The ethical challenges of publishing Twitter data for research dissemination. In: Proceedings of the 2017 ACM on Web Science Conference, pp. 339–348 (2017)

    Google Scholar 

  44. Wieringa, R.J.: Design Science Methodology for Information Systems and Software Engineering. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43839-8

    Book  Google Scholar 

  45. Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D.: EmojiNet: building a machine readable sense inventory for emoji. In: Spiro, E., Ahn, Y.-Y. (eds.) SocInfo 2016, Part I. LNCS, vol. 10046, pp. 527–541. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47880-7_33

    Chapter  Google Scholar 

  46. Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D.: EmojiNet: an open service and API for emoji sense discovery. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 11, pp. 437–446 (2017)

    Google Scholar 

  47. Yang, L., Sun, T., Zhang, M., Mei, Q.: We know what@ you# tag: does the dual role affect hashtag adoption? In: Proceedings of the 21st International Conference on World Wide Web, pp. 261–270 (2012)

    Google Scholar 

  48. Zaman, T.R., Herbrich, R., Van Gael, J., Stern, D.: Predicting information spreading in Twitter. In: Workshop on Computational Social Science and the Wisdom of Crowds, Nips, vol. 104, pp. 17599–601. Citeseer (2010)

    Google Scholar 

  49. Zhou, Y., Ai, W.: # Emoji: a study on the association between emojis and hashtags on Twitter. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 16, pp. 1169–1180 (2022)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Dutch Police, who provided insights into their open-source intelligence work. Our gratitude goes especially to the OSINT team, who provided valuable insights into their activities.

Author information

Authors and Affiliations

  1. Netherlands Police, Van Deventerlaan 50, 3528 AE, Utrecht, The Netherlands

    Laurens H. F. Müter

  2. Utrecht University, Nationaal Politielab AI, Leuvenlaan 4, 3584 CE, Utrecht, The Netherlands

    Laurens H. F. Müter & Remco C. Veltkamp

Authors
  1. Laurens H. F. Müter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Remco C. Veltkamp
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laurens H. F. Müter .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Müter, L.H.F., Veltkamp, R.C. (2024). Analysing Protest-Related Tweets: An Evaluation of Techniques by the Open Source Intelligence Team. In: Arai, K. (eds) Advances in Information and Communication. FICC 2024. Lecture Notes in Networks and Systems, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-031-53963-3_5

Download citation

Publish with us

Policies and ethics