Have a personal or library account? Click to login

A multi-viewpoint spectrum paradigm for inter-actor relationship analysis in non-social textual corpora: The case of the UN General Debate Corpus

Journal of Data and Information Science
Volume 10 (2025): Issue 3 (June 2025)
By:
Open Access
|Jun 2025

References

  1. Adamic, L. A., & Glance, N. (2005). The political blogosphere and the 2004 U.S. election: Divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery (LinkKDD '05) (pp. 36-43). Association for Computing Machinery. https://doi.org/10.1145/1134271.1134277
    Search in Google ScholarBack to article
  2. Arias, S. B. (2022). Who securitizes? Climate change discourse in the United Nations. International Studies Quarterly, 66(2). https://doi.org/10.1093/isq/sqac020
    Search in Google ScholarBack to article
  3. Baclawski, K., Bennett, M., Berg-Cross, G., Casanave, C., Fritzsche, D., Luciano, J., Schneider, T., Sharma, R., Singer, J., Sowa, J., Sriram, R. D., Westerinen, A., & Whitten, D. (2018). Ontology summit 2018 communiqué: Contexts in context. Applied Ontology, 13(3), 181-200. https://doi.org/10.3233/AO-180200
    Search in Google ScholarBack to article
  4. Bailey, M. A., Strezhnev, A., & Voeten, E. (2017). Estimating dynamic state preferences from United Nations voting data. Journal of Conflict Resolution, 61(2), 430-456. https://doi.org/10.1177/0022002715595700
    Search in Google ScholarBack to article
  5. Barbieri, K., Keshk, O. M. G., & Pollins, B. M. (2009). Trading data: Evaluating our assumptions and coding rules. Conflict Management and Peace Science., 26(5), 471-491. https://doi.org/10.1177/0738894209343887
    Search in Google ScholarBack to article
  6. Baturo, A., Dasandi, N., & Mikhaylov, S. J. (2017). Understanding state preferences with text as data: Introducing the UN general debate corpus. Research and Politics, 4(2). https://doi.org/10.1177/2053168017712821
    Search in Google ScholarBack to article
  7. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3, 993-1022.
    Search in Google ScholarBack to article
  8. Chelotti, N., Dasandi, N., & Jankin Mikhaylov, S. (2022). Do intergovernmental organizations have a socialization effect on member state preferences? Evidence from the UN General Debate. International Studies Quarterly, 66(1). https://doi.org/10.1093/isq/sqab069
    Search in Google ScholarBack to article
  9. Claude, I. L. (1966). Collective legitimization as a political function of the United Nations. International Organization, 20(3), 367-379. https://doi.org/10.1017/S0020818300012832
    Search in Google ScholarBack to article
  10. Dasandi, N., Jankin, S., & Baturo, A. (2023). Words to unite nations: The complete UN General Debate Corpus, 1946-present [Project]. OSF. https://osf.io/z69fj
    Search in Google ScholarBack to article
  11. Dieng, A. B., Ruiz, F. J. R., & Blei, D. M. (2019). The dynamic embedded topic model. arXiv. https://doi. org/10.48550/arXiv.1907.05545
    Search in Google ScholarBack to article
  12. Doan, T. M., & Gulla, J. A. (2022). A Survey on Political Viewpoints Identification. Online Social Networks and Media, 30, 100208. https://doi.org/10.1016/j.osnem.2022.100208
    Search in Google ScholarBack to article
  13. Dong, X., & Lian, Y. (2021). A review of social media-based public opinion analyses: Challenges and recommendations. Technology in Society, 67, 101724. https://doi.org/10.1016/j.techsoc.2021.101724
    Search in Google ScholarBack to article
  14. Gangula, R. R. R., Duggenpudi, S. R., & Mamidi, R. (2019). Detecting political bias in news articles using headline attention. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (pp. 77-84). https://doi.org/10.18653/v1/w19-4809
    Search in Google ScholarBack to article
  15. Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2016). Quantifying controversy in social media. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (pp. 33-42). https://doi.org/10.1145/2835776.2835792
    Search in Google ScholarBack to article
  16. Gentzkiow, M., & Shapiro, J. M. (2010). What drives media slant? Evidence from U.S. Daily Newspapers. Econometrica, 78(1), 35-71. https://doi.org/10.3982/ecta7195
    Search in Google ScholarBack to article
  17. Gibler, D. M. (2008). International military alliances, 1648-2008. CQ Press.
    Search in Google ScholarBack to article
  18. Gu, Y., Chen, T., Sun, Y., & Wang, B. (2017). Ideology detection for Twitter users via link analysis. In D. Lee, Y. R. Lin, N. Osgood, & R. Thomson (Eds.), Social, cultural, and behavioral modeling: SBP-BRiMS 2017 (Lecture Notes in Computer Science, Vol. 10354, pp. 262-268). Springer. https://doi.org/10.1007/978-3-319-60240-0_32
    Search in Google ScholarBack to article
  19. Gurciullo, S., & Mikhaylov, S. J. (2017a). Topology analysis of international networks based on debates in the United Nations. arXiv. https://doi.org/10.48550/arXiv.1707.09491
    Search in Google ScholarBack to article
  20. Gurciullo, S., & Mikhaylov, S. J. (2017b). Detecting policy preferences and dynamics in the UN general debate with neural word embeddings. In 2017 International Conference on the Frontiers and Advances in Data Science (FADS) (pp. 74-79). IEEE. https://doi.org/10.1109/FADS.2017.8253197
    Search in Google ScholarBack to article
  21. Hecht, C. (2016). The shifting salience of democratic governance: Evidence from the United Nations General Assembly General Debates. Review of International Studies, 42(5), 915-938. https://doi.org/10.1017/S0260210516000073
    Search in Google ScholarBack to article
  22. Høyland, B., Godbout, J. F., Lapponi, E., & Velldal, E. (2014). Predicting Party Affiliations from European Parliament Debates. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp.56–60). https://doi.org/10.3115/v1/w14-2516
    Search in Google ScholarBack to article
  23. Iyyer, M., Enns, P., Boyd-Graber, J., & Resnik, P. (2014). Political ideology detection using recursive neural networks. In Proceedings of the 52nd annual meeting of the Association for Computational Linguistics (volume 1, pp. 1113-1122). https://doi.org/10.3115/v1/p14-1105
    Search in Google ScholarBack to article
  24. Kentikelenis, A., & Voeten, E. (2021). Legitimacy challenges to the liberal world order: Evidence from United Nations speeches, 1970–2018. Review of International Organizations, 16(4), 721-754. https://doi.org/10.1007/S11558-020-09404-Y
    Search in Google ScholarBack to article
  25. Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding echo chambers and filter bubbles: The impact of social media on diversification and partisan shifts in news consumption. MIS Quarterly, 44(4). https://doi. org/10.25300/MISQ/2020/16371
    Search in Google ScholarBack to article
  26. Kozareva, Z., & Hovy, E. (2010). Learning arguments and supertypes of semantic relations using recursive patterns. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, (pp.1482-1491).
    Search in Google ScholarBack to article
  27. Lauderdale, B. E., & Clark, T. S. (2012). The Supreme Court’s many median justices. American Political Science Review, 106(4), 847-866. https://doi.org/10.1017/S0003055412000469
    Search in Google ScholarBack to article
  28. Lin, W. H., Wilson, T., Wiebe, J., & Hauptmann, A. (2006). Which side are you on? Identifying perspectives at the document and sentence levels. In CoNLL 2006 - Proceedings of the 10th Conference on Computational Natural Language Learning (pp.109-116).
    Search in Google ScholarBack to article
  29. Lotan, N., & Minkov, E. (2023). Social world knowledge: Modeling and applications. PLoS ONE, 18(7), e0283700. https://doi.org/10.1371/journal.pone.0283700
    Search in Google ScholarBack to article
  30. Mitrani, M. (2017). The Discursive construction of the international community: Evidence from the United Nations General Assembly. KFG Working Paper Series, 78, 1-30. http://nbn-resolving.de/urn:nbn:de:0168-ssoar-51596-1
    Search in Google ScholarBack to article
  31. Mitrani, M. (2023). In search of the bellwether: A text as data approach for assessing trend-making in international discourse [conference paper]. EISA Annual Conference, Potsdam, Germany.
    Search in Google ScholarBack to article
  32. Paul, M. J., Zhai, C. X., & Girju, R. (2010). Summarizing contrastive viewpoints in opinionated text. In Proceedings of the 2010 conference on empirical methods in natural language processing (pp. 66-76).
    Search in Google ScholarBack to article
  33. Prati, R. C., & Said-Hung, E. (2019). Predicting the ideological orientation during the Spanish 24M elections in Twitter using machine learning. AI and Society, 34(3), 589-598. https://doi.org/10.1007/s00146-017-0761-0
    Search in Google ScholarBack to article
  34. Preotiuc-Pietro, D., Hopkins, D. J., Liu, Y., & Ungar, L. (2017). Beyond binary labels: Political ideology prediction of Twitter users. In ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers, 1, pp.729-740). https://doi.org/10.18653/v1/P17-1068
    Search in Google ScholarBack to article
  35. Quraishi, M., Fafalios, P., & Herder, E. (2018). Viewpoint discovery and understanding in social networks. In WebSci 2018 - Proceedings of the 10th ACM Conference on Web Science (pp.47-56). https://doi.org/10.1145/3201064.3201076
    Search in Google ScholarBack to article
  36. Rao, A., & Spasojevic, N. (2016). Actionable and political text classification using word embeddings and LSTM. arXiv. http://arxiv.org/abs/1607.02501
    Search in Google ScholarBack to article
  37. Ren, Z., Inel, O., Aroyo, L., & De Rijke, M. (2016). Time-aware multi-viewpoint summarization of multilingual social text streams. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 387-396). https://doi.org/10.1145/2983323.2983710
    Search in Google ScholarBack to article
  38. Rheault, L., & Cochrane, C. (2020). Word embeddings for the analysis of ideological placement in parliamentary corpora. Political Analysis, 28(1), 112-133. https://doi.org/10.1017/pan.2019.26
    Search in Google ScholarBack to article
  39. Sataloff, R. T., Johns, M. M., Kost, K. M., Schoenfeld, M., Eckhard, S., Patz, R., & van Meegdenburg, H. (2018). Discursive landscapes and unsupervised topic modeling in IR: A validation of text-as-data approaches through a new corpus of UN Security Council speeches on Afghanistan. arXiv. https://doi.org/10.48550/arXiv.1810.05572
    Search in Google ScholarBack to article
  40. Shukla, D., & Unger, S. (2022). Sentiment analysis of international relations with Artificial Intelligence. Athens Journal of Sciences, 9(2), 91-106. https://doi.org/10.30958/ajs.9-2-1
    Search in Google ScholarBack to article
  41. Sim, Y., Acree, B. D. L., Gross, J. H., & Smith, N. A. (2013). Measuring ideological proportions in political speeches. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 91-101).
    Search in Google ScholarBack to article
  42. Simmons, B. A., & Shaffer, R. (2019). Globalization and border securitization in international discourse. SSRN Electronic Journal, 3480613. https://doi.org/10.2139/ssrn.3480613
    Search in Google ScholarBack to article
  43. Singer, D., Bremer, S., & Stuckey, J. (1972). Capability Distribution, Uncertainty, and Major Power War, 18201965. In B. M. Russett (Ed.), Peace, War, and Numbers (pp.19-48). SAGE Publications Ltd.
    Search in Google ScholarBack to article
  44. Thonet, T., Cabanac, G., Boughanem, M., & Pinel-Sauvagnat, K. (2016). VODUM: A topic model unifying viewpoint, topic and opinion discovery. In Proceedings of Advances in Information Retrieval: 38th European Conference on IR Research (pp. 533-545). Springer. https://doi.org/10.1007/978-3-319-30671-1_39
    Search in Google ScholarBack to article
  45. Trabelsi, A., & Zaïane, O. R. (2019). Phaitv: A phrase author interaction topic viewpoint model for the summarization of reasons expressed by polarized stances. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, pp. 482-492). https://ojs.aaai.org/index.php/ICWSM/article/view/3246
    Search in Google ScholarBack to article
  46. Vilares, D., & He, Y. (2017). Detecting perspectives in political debates. In EMNLP 2017-Conference on Empirical Methods in Natural Language Processing (pp. 1573-1582). Association for Computational Linguistics. https://doi.org/10.18653/v1/d17-1165
    Search in Google ScholarBack to article
  47. Vliegenthart, R., Walgrave, S., & Zicha, B. (2013). How preferences, information and institutions interactively drive agenda-setting: Questions in the Belgian parliament, 1993-2000. European Journal of Political Research, 52(3), 390-418. https://doi.org/10.1111/J.1475-6765.2012.02070.X
    Search in Google ScholarBack to article
  48. Watanabe, K., & Zhou, Y. (2020). Theory-driven analysis of large corpora: Semisupervised topic classification of the UN speeches. Social Science Computer Review, 40(2), 1-21. https://doi.org/10.1177/0894439320907027
    Search in Google ScholarBack to article
  49. Wong, F. M. F., Tan, C. W., Sen, S., & Chiang, M. (2016). Quantifying political leaning from tweets, retweets, and retweeters. IEEE Transactions on Knowledge and Data Engineering, 28(8), 2158-2172. https://doi.org/10.1109/TKDE.2016.2553667
    Search in Google ScholarBack to article
  50. Xiao, Z., Song, W., Xu, H., Ren, Z., & Sun, Y. (2020). TIMME: Twitter Ideology-detection via Multi-task Multirelational embedding. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol.20, pp. 2258-2268). https://doi.org/10.1145/3394486.3403275
    Search in Google ScholarBack to article
  51. Zhitomirsky-Geffet, M. (2019). Towards a diversified knowledge organization system: An open network of interlinked subsystems with multiple validity scopes. Journal of Documentation, 75(5), 1124-1138. https://doi.org/10.1108/JD-10-2018-0163
    Search in Google ScholarBack to article
  52. Zhitomirsky-Geffet, M. (2022). Turning filter bubbles into bubblesphere with multi-viewpoint KOS and diverse similarity. In Proceedings of the Association for Information Science and Technology, 59(1), 533-538. https://doi.org/10.1002/pra2.665
    Search in Google ScholarBack to article
  53. Zhitomirsky-Geffet, M., & Avidan, G. (2021). A new framework for systematic analysis and classification of inconsistencies in multi-viewpoint ontologies. Knowledge Organization, 48(5), 331-344. https://doi.org/10.5771/0943-7444-2021-5-331
    Search in Google ScholarBack to article
  54. Zhitomirsky-Geffet, M., & Hajibayova, L. (2020). A new framework for ethical creation and evaluation of multiperspective knowledge organization systems. Journal of Documentation, 76(6), 1459-1471. https://doi.org/10.1108/JD-04-2020-0053/FULL/XML
    Search in Google ScholarBack to article
  55. Zhou, D. X., Resnick, P., & Mei, Q. (2011). Classifying the political leaning of news articles and users from user votes. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (vol. 5, No. 1, pp. 417-424). https://doi.org/10.1609/icwsm.v5i1.14108
    Search in Google ScholarBack to article
  56. Zhou, Y., & Kurusu, K. (2021). How major powers diverge on global governance? Evidence from the United Nations General Debate. Kobe University Law Review, 54, 63-80. https://doi.org/10.24546/81013152
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/jdis-2025-0026 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Journal RSS Feed
Language: English
Page range: 32 - 51
Submitted on: Dec 29, 2024
Accepted on: Apr 7, 2025
Published on: Jun 11, 2025
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Viewpoints,
United Nations,
Politics,
International relations,
Knowledge organization
Related subjects:
Computer sciences,
Information technology,
Project management,
Databases and data mining

© 2025 Efrat Miller, Maayan Zhitomirsky-Geffet, Mor Mitrani, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 10 (2025): Issue 3 (June 2025)Next article