Abstract
Agendas in online media have become a scholarly focus nearly two decades ago, leading to shifting conceptualizations of what we see as agenda. Thus, agendas and agenda shifts inside online discussions have shown its potential to influence offline deliberation, aggregate support, fuel protest, passing through and/or bypassing traditional media’s gatekeeping. Real-time (or nearly-real-time) learning about quick agenda movement inside globalized public debate might be particularly important for international organizations like UN or EU. However, we today lack both knowledge on how agendas move in such discussions and instruments on such analysis. In particular, we are next-to-unaware of to what extent globally relevant themes get contextualized within language-based discussion segments, as well as to what extent the latter depend on each other and lag behind each other in developing agendas and public opinion on quickly evolving issues or conflicts. In this paper, we propose a method of agenda detection based on neural-network text summarization and compare summaries of tweet packages across three languages within the Twitter hashtag #jesuischarlie. We show that sentiment detection may allow for quality assessment of the text summaries, as compared to aggregated sentiment to the original tweets. We show that, outside France, agendas were more interpretational, abstract, and non-contextualized. The pattern of news changing to ‘issue outburt’ was simultaneous in dense discussion segments and lagged behind in a sparser one. We also show that, globally, main issues of the discussion may be spotted within the first hour.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McCombs, M.: Setting the agenda: the mass media and public opinion. Polity, Malden (2004)
Koltsova, O., Nagornyy, O.: Redefining media agendas: topic problematization in online reader comments. Media Commun. 7(3), 145–156 (2019)
Papacharissi, Z.: Affective Publics: Sentiment, Technology, and Politics. Oxford UP, Oxford (2015)
Smoliarova, A.S., Bodrunova, S.S., Yakunin, A.V., Blekanov, I., Maksimov, A.: Detecting pivotal points in social conflicts via topic modeling of Twitter content. In: Bodrunova, S.S., et al. (eds.) INSCI 2018. LNCS, vol. 11551, pp. 61–71. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17705-8_6
Koltcov, S., Koltsova, O., Nikolenko, S.: Latent Dirichlet allocation: stability and applications to studies of user-generated content. In: Proceedings of the 2014 ACM Conference on Web Science, pp. 161–165. ACM (2014)
Bodrunova, S.S., Koltsova, O., Koltcov, S., Nikolenko, S.: Who’s bad? Attitudes toward resettlers from the post-Soviet south versus other nations in the Russian blogosphere. Int. J. Commun. 11, 3242–3264 (2017)
Kim, S.-T., Lee, Y.-H.: New functions of Internet mediated agenda-setting: agenda-rippling and reversed agenda-setting. Korean J. Commun. Stud. 50(3), 175–205 (2006)
Russell Neuman, W., Guggenheim, L., Mo Jang, S., Bae, S.Y.: The dynamics of public attention: agenda-setting theory meets big data. J. Commun. 64(2), 193–214 (2014)
Guo, L.: Media agenda diversity and intermedia agenda setting in a controlled media environment: a computational analysis of china’s online news. J. Stud. 20(16), 2460–2477 (2019)
Koltsova, O., Bodrunova, S.S.: Public discussion in Russian social media: an introduction. Media Commun. 7(3), 114–118 (2019)
Koltsova, O., Koltcov, S.: Mapping the public agenda with topic modeling: the case of the Russian Livejournal. Policy Internet 5(2), 207–227 (2013)
Fuchs, C.: Social Media: A Critical Introduction. Sage, Thousand Oaks (2017)
Martin, S., Grüb, B.: Towards a process of agenda setting driven by social media. Int. J. Energy Sect. Manage. 10(1), 38–55 (2016)
Posegga, O., Jungherr, A.: Characterizing political talk on Twitter: a comparison between public agenda, media agendas, and the Twitter agenda with regard to topics and dynamics. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, pp. 2590–2599 (2019)
Casas, A., Morar, D.: Different channel, same strategy? Filling empirical gaps in Congress literature. In: Paper presented at the 2015 Annual Meeting of the American Political Science Association (APSA), San Francisco (2015)
Bruns, A., Stieglitz, S.: Twitter data: what do they represent? IT Inf. Technol. 56(5), 240–245 (2014)
Barberá, P., et al.: Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data. Am. Polit. Sci. Rev. 113(4), 883–901 (2019)
Tsur, O., Calacci, D., Lazer, D.: A frame of mind: using statistical models for detection of framing and agenda setting campaigns. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 1629–1638. ACL (2015)
Alam, M.H., Ryu, W.-J., Lee, S.: Hashtag-based topic evolution in social media. World Wide Web 20(6), 1527–1549 (2017). https://doi.org/10.1007/s11280-017-0451-3
Zhang, Y., Mao, W., Lin, J.: Modeling topic evolution in social media short texts. In: 2017 IEEE International Conference on Big Knowledge (ICBK), pp. 315–319. IEEE (2017)
Zhang, Y., Mao, W., Zeng, D.: Topic evolution modeling in social media short texts based on recurrent semantic dependent CRP. In: 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 119–124). IEEE (2017)
Saha, A., Sindhwani, V.: Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 693–702. ACM (2012)
Huang, J., Peng, M., Wang, H., Cao, J., Gao, W., Zhang, X.: A probabilistic method for emerging topic tracking in microblog stream. World Wide Web 20(2), 325–350 (2017)
Deng, Q., Cai, G., Zhang, H., Liu, Y., Huang, L., Sun, F.: Enhancing situation awareness of public safety events by visualizing topic evolution using social media. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, pp. 1–10 (2018)
Dermouche, M., Velcin, J., Khouas, L., Loudcher, S.: A joint model for topic-sentiment evolution over time. In: 2014 IEEE international conference on data mining, pp. 773–778. IEEE (2014)
Wang, Y., Agichtein, E., Benzi, M.: TM-LDA: efficient online modeling of latent topic transitions in social media. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 123–131. ACM (2012)
Bodrunova, S.S., Smoliarova, A.S., Blekanov, I.S., Zhuravleva, N.N., Danilova, Y.S.: A global public sphere of compassion? #JeSuisCharlie and #JeNeSuisPasCharlie on Twitter and their language boundaries. Monitoring Obshchestvennogo Mneniya: Ekonomicheskie i Sotsial’nye Peremeny 1(143), 267–294 (2018)
Bodrunova, S.S., Blekanov, I., Smoliarova, A., Litvinenko, A.: Beyond left and right: real-world political polarization in Twitter discussions on inter-ethnic conflicts. Media Commun. 7(3), 119–132 (2019)
Bodrunova, S.S.: When context matters. analyzing conflicts with the use of big textual corpora from Russian and international social media. Partecipazione e conflitto 11(2), 497–510 (2018)
Bodrunova, S.S.: The boundaries of context: contextual knowledge in research on networked discussions. In: Antonyuk, A., Basov, N. (eds.), Proceedings of the Fifth Networks in the Global World Conference (NetGloW2020). Springer, Cham (2021)
Kalyanam, J., Mantrach, A., Saez-Trumper, D., Vahabi, H., Lanckriet, G.: Leveraging social context for modeling topic evolution. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 517–526 (2015)
Ahmed, A., Xing, E.: Dynamic non-parametric mixture models and the recurrent Chinese restaurant process: with applications to evolutionary clustering. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 219–230. Society for Industrial and Applied Mathematics (2008)
Lu, Z., Tan, H., Li, W.: An evolutionary context-aware sequential model for topic evolution of text stream. Inf. Sci. 473, 166–177 (2019)
Aggarwal, C.C., Zhai, C. (eds.): Mining Text Data. Springer, Cham (2012). https://doi.org/10.1007/978-3-319-14142-8_13
Nenkova, A., McKeown, K.: A survey of text summarization techniques. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 43–76. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_3
Tas, O., Kiyani, F.: A survey automatic text summarization. PressAcademia Proc. 5(1), 205–213 (2007)
Ferreira, R., et al.: A context based text summarization system. In: 2014 11th IAPR International Workshop on Document Analysis Systems, pp. 66–70. IEEE (2014)
Kaikhah, K.: Automatic text summarization with neural networks. In: 2004 2nd International IEEE Conference on Intelligent Systems. Proceedings (IEEE Cat. No. 04EX791), vol. 1, pp. 40–44. IEEE (2004)
Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B.: Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv:1602.06023 (2016)
Celis, L.E., Keswani, V.: Dialect Diversity in Text Summarization on Twitter. arXiv:2007.07860 (2020)
Li, Q., Zhang, Q.: Abstractive Event Summarization on Twitter. Companion Proceedings of the Web Conference 2020, 22–23 (2020)
Mottaghinia, Z., Feizi-Derakhshi, M.R., Farzinvash, L., Salehpour, P.: A review of approaches for topic detection in Twitter. J. Exp. Theor. Artif. Intell. (2020). https://doi.org/10.1080/0952813X.2020.1785019
Asgari-Chenaghlu, M., Nikzad-Khasmakhi, N., Minaee, S.: Covid-transformer: Detecting trending topics on twitter using universal sentence encoder. arXiv:2009.03947 (2020)
Beltagy, I., Peters, M.E., Cohan, A.: Longformer: The long-document transformer. arXiv:2004.05150 (2020)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv:1910.10683 (2019)
Bodrunova, S.S., Blekanov, I.S., Kukarkin, M.: Topics in the Russian Twitter and relations between their interpretability and sentiment. In: 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 549–554. IEEE (2019)
Blekanov, I.S., Sergeev, S.L., Martynenko, I.A.: Constructing topic-oriented web crawlers with generalized core. Sci. Res. Bull. St. Petersburg State Polytech. Univ. 5(157), 9–15 (2012)
Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., Vespignani, A.: The Twitter of Babel: Mapping world languages through microblogging platforms. PLoS ONE 8(4), e61981 (2013)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv:1607.01759 (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)
Acknowledgement
This research has been supported in full by Russian Science Foundation, grant 21-18-00454.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bodrunova, S.S., Blekanov, I.S., Tarasov, N. (2021). Global Agendas: Detection of Agenda Shifts in Cross-National Discussions Using Neural-Network Text Summarization for Twitter. In: Meiselwitz, G. (eds) Social Computing and Social Media: Experience Design and Social Network Analysis . HCII 2021. Lecture Notes in Computer Science(), vol 12774. Springer, Cham. https://doi.org/10.1007/978-3-030-77626-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-77626-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77625-1
Online ISBN: 978-3-030-77626-8
eBook Packages: Computer ScienceComputer Science (R0)