A Network-Driven Framework for Bidimensional Analysis of Information Dissemination on Social Media Platforms
DOI:
https://doi.org/10.5753/jis.2025.5526Keywords:
Social Media Platforms, Information Dissemination, Network Modeling and Analysis, Network Backbone ExtractionAbstract
Network modeling has become a foundational approach for analyzing information dissemination on social media platforms. Moreover, backbone extraction techniques, designed to isolate the most relevant structural patterns in noisy networks, have been widely employed to identify salient interactions, especially in the context of coordinated behavior and campaign detection. However, most of these approaches rely on a one-dimensional perspective, focusing primarily on interaction volume while neglecting temporal dynamics that are crucial to understanding how content spreads in real time. This limited view can obscure important distinctions between dissemination strategies that are fast but sparse or slow but voluminous. To address this gap, we introduce a bidimensional framework that integrates both interaction volume and speed, enabling a more comprehensive modeling of dissemination dynamics. Our approach applies state-of-the-art backbone extraction techniques independently to each dimension, classifying edges into four distinct dissemination profiles. This classification provides new analytical affordances for exploring both structural and textual patterns of information flow across social platforms. Applied to two case studies on Twitter/X and Telegram, the framework reveals contrasting dissemination strategies across platforms and shows how different edge classes contribute to the amplification of specific narratives. These findings advance the study of information dynamics by offering a finer-grained, multidimensional perspective on user interactions in complex social networks.
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Abilov, A., Hua, Y., Matatov, H., Amir, O., and Naaman, M. (2021). Voterfraud2020: a multi-modal dataset of election fraud claims on twitter. In International Conference on Web and Social Media. DOI: http://dx.doi.org/10.1609/icwsm.v15i1.18113.
Albarrak, M. S., Elnahass, M., Papagiannidis, S., and Salama, A. (2020). The effect of twitter dissemination on cost of equity: A big data approach. International Journal of Information Management, 50:1–16. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.04.014.
Antonakaki, D., Fragopoulou, P., and Ioannidis, S. (2021). A survey of twitter research: Data model, graph structure, sentiment analysis and attacks. Expert systems with applications, 164:114006. DOI: https://doi.org/10.1016/j.eswa.2020.114006.
Araujo, M. M., Ferreira, C. H., Reis, J. C., Silva, A. P., and Almeida, J. M. (2023). Identificação e caracterização de campanhas de propagandas eleitorais antecipadas brasileiras no twitter. In Anais do XII Brazilian Workshop on Social Network Analysis and Mining, pages 67–78. SBC. DOI: https://doi.org/10.5753/brasnam.2023.229879.
Armstrong, G., Martino, C., Rahman, G., Gonzalez, A., Vázquez-Baeza, Y., Mishne, G., and Knight, R. (2021). Uniform manifold approximation and projection (umap) reveals composite patterns and resolves visualization artifacts in microbiome data. MSystems, 6(5):10–1128. DOI: https://doi.org/10.1128/msystems.00691-21.
Badawy, A., Addawood, A., Lerman, K., and Ferrara, E. (2019). Characterizing the 2016 russian ira influence campaign. Social Network Analysis and Mining, 9:1–11. DOI: https://doi.org/10.1007/s13278-019-0578-6.
Barabási, A.-L. (2013). Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987):20120375. DOI: https://doi.org/10.1098/rsta.2012.0375.
Barbosa, C., Félix, L., Vieira, V., and Xavier, C. (2019). Sara - a semi-automatic framework for social network analysis. In Anais Estendidos do XXV Simpósio Brasileiro de Sistemas Multimídia e Web. DOI: https://doi.org/10.5753/webmedia_estendido.2019.8137.
Bellutta, D. and Carley, K. M. (2023). Investigating coordinated account creation using burst detection and network analysis. Journal of big Data, 10(1):20. DOI: https://doi.org/10.1186/s40537-023-00695-7.
Bizel, G. and Singh, A. K. (2023). Political polarization, misinformation, and sentiments: A social media analysis about ‘capitol hill 2021 attack’by tweets. Journal of Social, Humanities and Administrative Sciences, 9(61):2257–2266. DOI: http://dx.doi.org/10.29228/JOSHAS.64755.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of comm. in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008. DOI: https://doi.org/10.1088/1742-5468/2008/10/P10008.
Castagna, S., Porrino, G., and Borgonovo, F. (2023). The italian pro-russia digital ecosystem on telegram. Cybersecurity and Law, 10(2):299–317.
Cinelli, M., Cresci, S., Quattrociocchi, W., Tesconi, M., and Zola, P. (2022). Coordinated inauthentic behavior and information spreading on twitter. Decision Support Systems, 160:113819. DOI: https://doi.org/10.1016/j.dss.2022.113819.
Coscia, M. and Neffke, F. M. (2017). Network backboning with noisy data. In 2017 IEEE 33rd international conference on data engineering (ICDE), pages 425–436. IEEE. DOI: https://doi.ieeecomputersociety.org/10.1109/ICDE.2017.10.
da Rosa, J. M., Linhares, R. S., Ferreira, C. H. G., Nobre, G. P., Murai, F., and Almeida, J. M. (2022). Uncovering discussion groups on claims of election fraud from twitter. In Proc. of Social Informatics: 13th International Conference. DOI: https://doi.org/10.1007/978-3-031-19097-1_20.
Danaditya, A., Ng, L. H. X., and Carley, K. M. (2022). From curious hashtags to polarized effect: profiling coordinated actions in indonesian twitter discourse. Social Network Analysis and Mining, 12(1):105. DOI: https://doi.org/10.1007/s13278-022-00936-2.
Dargahi Nobari, A., Reshadatmand, N., and Neshati, M. (2017). Analysis of telegram, an instant messaging service. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17. DOI: https://doi.org/10.1145/3132847.3133132.
de Souza, F. C. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese BERTimbau: modelos BERT pré-treinados para Português Brasileiro. PhD thesis, [sn].
Elmas, T., Overdorf, R., Özkalay, A. F., and Aberer, K. (2021). Ephemeral astroturfing attacks: The case of fake twitter trends. In 2021 IEEE European symposium on security and privacy (EuroS&P), pages 403–422. IEEE. DOI: https://doi.org/10.1109/EuroSP51992.2021.00035.
Ferreira, C., Murai, F., Silva, A., Almeida, J., Trevisan, M., Vassio, L., Mellia, M., and Drago, I. (2021). On the dynamics of political discussions on instagram: A network perspective. Online Social Networks and Media. DOI: https://doi.org/10.1016/j.osnem.2021.100155.
Ferreira, C. H., Murai, F., Matos, B., and Almeida, J. M. (2019). Modeling dynamic ideological behavior in political networks. Web Science Journal, 7. DOI: https://doi.org/10.34962/jws-80.
Ferreira, C. H. G., Murai, F., Couto da Silva, A. P., de Almeida, J. M., Trevisan, M., Vassio, L., Drago, I., and Mellia, M. (2020). Unveiling community dynamics on instagram political network. In 12th ACM Conference on Web Science. DOI: https://doi.org/10.1145/3394231.3397913.
Fortunato, S. (2010). Community detection in graphs. Physics reports, 486(3-5):75–174. DOI: https://doi.org/10.1016/j.physrep.2009.11.002.
Fortunato, S. and Hric, D. (2016). Community detection in networks: A user guide. Physics reports, 659:1–44. DOI: https://doi.org/10.1016/j.physrep.2016.09.002.
G. Da Fonseca, L. G., Gomes Ferreira, C. H., and Soares Dos Reis, J. C. (2024). The role of news source certification in shaping tweet content: Textual and dissemination patterns in brazil’s 2022 elections. In Proceedings of the 20th Brazilian Symposium on Information Systems, pages 1–10. DOI: https://doi.org/10.1145/3658271.3658303.
Giglietto, F., Righetti, N., Rossi, L., and Marino, G. (2020). It takes a village to manipulate the media: coordinated link sharing behavior during 2018 and 2019 italian elections. Information, Communication & Society, 23(6):867–891. DOI: https://doi.org/10.1080/1369118X.2020.1739732.
Golovchenko, Y., Buntain, C., Eady, G., Brown, M. A., and Tucker, J. A. (2020). Cross-platform state propaganda: Russian trolls on twitter and youtube during the 2016 us presidential election. The International Journal of Press/Politics, 25(3):357–389. DOI: https://doi.org/10.1177/1940161220912682.
Gomes Ferreira, C. H., Murai, F., Silva, A. P. C., Trevisan, M., Vassio, L., Drago, I., Mellia, M., and Almeida, J. M. (2022). On network backbone extraction for modeling online collective behavior. PLOS ONE, 17(9):1–36. DOI: https://doi.org/10.1371/journal.pone.0274218.
Grady, D., Thiemann, C., and Brockmann, D. (2012). Robust classification of salient links in complex networks. Nature communications, 3(1):864. DOI: https://doi.org/10.1038/ncomms1847.
Graham, T., Hames, S., and Alpert, E. (2024). The coordination network toolkit: a framework for detecting and analysing coordinated behaviour on social media. Journal of Computational Social Science, pages 1–22. DOI: https://doi.org/10.1007/s42001-024-00260-z.
Grimminger, L. and Klinger, R. (2021). Hate towards the political opponent: A twitter corpus study of the 2020 us elections on the basis of offensive speech and stance detection. In Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis.
Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794. DOI: http://dx.doi.org/10.48550/arXiv.2203.05794.
Gunawan, D., Harahap, S. H., and Rahmat, R. F. (2019). Multi-document summarization by using textrank and maximal marginal relevance for text in bahasa indonesia. In 2019 International conference on ICT for smart society (ICISS), volume 7, pages 1–5. IEEE. DOI: https://doi.org/10.1109/ICISS48059.2019.8969785.
Keller, F. B., Schoch, D., Stier, S., and Yang, J. (2020). Political astroturfing on twitter: How to coordinate a disinformation campaign. Political communication, 37(2):256–280. DOI: https://doi.org/10.1080/10584609.2019.1661888.
Khan, B. S. and Niazi, M. A. (2017). Network community detection: A review and visual survey. arXiv preprint arXiv:1708.00977. DOI: https://doi.org/10.48550/arXiv.1708.00977.
Lanius, C., Weber, R., and MacKenzie Jr, W. I. (2021). Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey. Social network analysis and mining, 11(1):32. DOI: https://doi.org/10.1007/s13278-021-00739-x.
Lima, F. M. d. C., de Miranda, L. C., Vasiljevic, G. A. M., and Baranauskas, M. C. C. (2024). An analysis of the authorship and co-authorship networks of the brazilian human-computer interaction conference. Journal on Interactive Systems, 15(1):265–293. DOI: https://doi.org/10.5753/jis.2024.3340.
Linhares, R. S., Rosa, J. M., Ferreira, C. H. G., Murai, F., Nobre, G., and Almeida, J. (2022). Uncovering coordinated communities on twitter during the 2020 u.s. election. In Proc. of ASONAM. DOI: https://doi.org/10.1109/ASONAM55673.2022.10068628.
Malagoli, L., Piorino, G., Ferreira, C. H., and da Silva, A. P. C. (2024). Twitter and the 2022 brazilian elections portrait: A network and content-driven analysis. In Brazilian Symposium on Multimedia and the Web (WebMedia), pages 283–291. SBC. DOI: https://doi.org/10.5753/webmedia.2024.241926.
Mao, Y., Qu, Y., Xie, Y., Ren, X., and Han, J. (2020). Multi-document summarization with maximal marginal relevance-guided reinforcement learning. arXiv preprint arXiv:2010.00117. DOI: https://doi.org/10.18653/v1/2020.emnlp-main.136.
Marcaccioli, R. and Livan, G. (2019). A pólya urn approach to information filtering in complex networks. Nature communications, 10(1):745. DOI: https://doi.org/10.1038/s41467-019-08667-3.
Markopoulou, A., Iannaccone, G., Bhattacharyya, S., Chuah, C.-N., Ganjali, Y., and Diot, C. (2008). Characterization of failures in an operational ip backbone network. IEEE/ACM transactions on networking, 16(4):749–762. DOI: https://doi.org/10.1109/TNET.2007.902727.
Neal, Z. P. (2022). backbone: An r package to extract network backbones. PloS one, 17(5):e0269137. DOI: https://doi.org/10.1371/journal.pone.0269137.
Neal, Z. P., Domagalski, R., and Sagan, B. (2021). Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections. Scientific reports, 11(1):23929. DOI: https://doi.org/10.1038/s41598-021-03238-3.
Neal, Z. P. and Neal, J. W. (2024). Illustrating the importance of edge constraints in backbones of bipartite projections. Plos one, 19(5):e0302973. DOI: https://doi.org/10.1371/journal.pone.0302973.
Newman, M. (2018). Network structure from rich but noisy data. Nature Physics, 14:542–546. DOI: https://doi.org/10.1038/s41567-018-0076-1.
Newman, M. E. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical review E, 74(3):036104. DOI: https://doi.org/10.1103/PhysRevE.74.036104.
Newman, M. E. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69:026113. DOI: https://doi.org/10.1103/PhysRevE.69.026113.
Ng, L. H. X., Kloo, I., Clark, S., and Carley, K. M. (2024). An exploratory analysis of covid bot vs human disinformation dissemination stemming from the disinformation dozen on telegram. Journal of Computational Social Science, pages 1–26. DOI: https://doi.org/10.1007/s42001-024-00253-y.
Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., and Tesconi, M. (2021). Coordinated behavior on social media in 2019 uk general election. In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 443–454. DOI: https://doi.org/10.1609/icwsm.v15i1.18074.
Nobre, G., Ferreira, C., and Almeida, J. (2020). Beyond groups: Uncovering dynamic communities on the whatsapp network of information dissemination. In SocInfo’ 2020. DOI: https://doi.org/10.1007/978-3-030-60975-7_19.
Nobre, G. P., Ferreira, C. H., and Almeida, J. M. (2022). A hierarchical network-oriented analysis of user participation in misinformation spread on whatsapp. Information Processing and Management, 59(1). DOI: https://doi.org/10.1016/j.ipm.2021.102757.
Oliveira, G. S., Venâncio, O., Vieira, V., Almeida, J., Silva, A. P., Ferreira, R., and Ferreira, C. H. (2024). Um framework para análise bidimensional de disseminação de informações em plataformas de mídias sociais. In Proceedings of the 30th Brazilian Symposium on Multimedia and the Web (WebMedia 2024). Sociedade Brasileira de Computação-SBC. DOI: https://doi.org/10.5753/webmedia.2024.241957.
Pacheco, D., Flammini, A., and Menczer, F. (2020). Unveiling coordinated groups behind white helmets disinformation. In The Web Conference. DOI: https://doi.org/10.1145/3366424.3385775.
Pacheco, D., Hui, P.-M., Torres-Lugo, C., Truong, B. T., Flammini, A., and Menczer, F. (2021). Uncovering coordinated networks on social media: Methods and case studies. In International Conference on Web and Social Media. DOI: https://doi.org/10.1609/icwsm.v15i1.18075.
Pohl, J. S., Markmann, S., Assenmacher, D., and Grimme, C. (2023). Invasion@ ukraine: providing and describing a twitter streaming dataset that captures the outbreak of war between russia and ukraine in 2022. In Proceedings of the International AAAI Conference on Web and Social Media, volume 17, pages 1093–1101. DOI: https://doi.org/10.1609/icwsm.v17i1.22217.
Reimers, N. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084. DOI: https://doi.org/10.18653/v1/D19-1410.
Rossetti, G., Pappalardo, L., Pedreschi, D., and Giannotti, F. (2017). Tiles: an online algorithm for community discovery in dynamic social networks. Machine Learning, 106:1213–1241. DOI: https://doi.org/10.1007/s10994-016-5582-8.
Rossini, P., Mont’Alverne, C., and Kalogeropoulos, A. (2023). Explaining beliefs in electoral misinformation in the 2022 brazilian election: The role of ideology, political trust, social media, and messaging apps. Harvard Kennedy School Misinformation Review, 4(3). DOI: https://doi.org/10.37016/mr-2020-115.
Saxena, N., Sinha, A., Bansal, T., and Wadhwa, A. (2023). A statistical approach for reducing misinformation propagation on twitter social media. Information Processing & Management, 60(4):103360. DOI: https://doi.org/10.1016/j.ipm.2023.103360.
Serrano, M. Á., Boguná, M., and Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106(16):6483–6488. DOI: https://doi.org/10.1073/pnas.0808904106.
Sharma, K., Zhang, Y., Ferrara, E., and Liu, Y. (2021). Identifying coordinated accounts on social media through hidden influence and group behaviours. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1441–1451. DOI: https://doi.org/10.1145/3447548.3467391.
Silva, F. and Freitas, L. (2022). Brazilian portuguese hate speech classification using bertimbau. In The International FLAIRS Conference Proceedings, volume 35. DOI: https://doi.org/10.32473/flairs.v35i.130594.
Souravlas, S., Sifaleras, A., Tsintogianni, M., and Katsavounis, S. (2021). A classification of community detection methods in social networks: a survey. International Journal of General Systems, 50(1):63–91. DOI: https://doi.org/10.1080/03081079.2020.1863394.
Souza, F., Nogueira, R., and Lotufo, R. (2020). Bertimbau: Pretrained bert models for brazilian portuguese. In Cerri, R. and Prati, R. C., editors, Intelligent Systems, pages 403–417, Cham. Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-61377-8_28.
Stieglitz, S. and Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3:1277–1291. DOI: https://doi.org/10.1007/s13278-012-0079-3.
Traag, V. A., Waltman, L., and Van Eck, N. J. (2019). From louvain to leiden: guaranteeing well-connected communities. Scientific reports, 9(1):1–12. DOI: https://doi.org/10.1038/s41598-019-41695-z.
Ulizko, M., Artamonov, A., Tukumbetova, R., Antonov, E., and Vasilev, M. (2022). Critical paths of information dissemination in networks. Scientific Visualization, 14(2):98–107. DOI: https://doi.org/10.26583/sv.14.2.09.
Urman, A. and Katz, S. (2020). What they do in the shadows: examining the far-right networks on telegram. Information, Communication & Society, pages 1–20. DOI: https://doi.org/10.1080/1369118X.2020.1803946.
Vargas, L., Emami, P., and Traynor, P. (2020). On the detection of disinformation campaign activity with network analysis. In ACM Cloud Computing Security Workshop, pages 133–146. DOI: https://doi.org/10.1145/3411495.3421363.
Varshney, D. and Vishwakarma, D. K. (2021). A review on rumour prediction and veracity assessment in online social network. Expert Systems with Applications, 168:114208. DOI: https://doi.org/10.1016/j.eswa.2020.114208.
Venâncio, O. R., Ferreira, C. H., Almeida, J. M., and da Silva, A. P. C. (2024a). Unraveling user coordination on telegram: A comprehensive analysis of political mobilization during the 2022 brazilian presidential election. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 1545–1556. DOI: https://doi.org/10.1609/icwsm.v18i1.31408.
Venâncio, O. R., Gonçalves, G. H., Ferreira, C. H., and da Silva, A. P. C. (2024b). Evidências de disseminação de conteúdo no telegram durante o ataque aos órgãos públicos brasileiros em 2023. In Brazilian Symposium on Multimedia and the Web (WebMedia), pages 385–389. SBC. DOI: https://doi.org/10.5753/webmedia.2024.241972.
Vishnuprasad, P. S., Nogara, G., Cardoso, F., Cresci, S., Giordano, S., and Luceri, L. (2024). Tracking fringe and coordinated activity on twitter leading up to the us capitol attack. In Proceedings of the international AAAI conference on web and social media, volume 18, pages 1557–1570. DOI: http://dx.doi.org/10.1609/icwsm.v18i1.31409.
Weber, D. and Neumann, F. (2020). Who's in the gang? revealing coordinating communities in social media. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 89–93. IEEE. DOI: https://doi.org/10.1109/asonam49781.2020.9381418.
Weber, D. and Neumann, F. (2021). Amplifying influence through coordinated behaviour in social networks. Social Network Analysis and Mining, 11(1):111. DOI: https://doi.org/10.1007/s13278-021-00815-2.
Zayats, V. and Ostendorf, M. (2018). Conversation modeling on reddit using a graph-structured lstm. Transactions of the Association for Computational Linguistics, 6:121–132. DOI: https://doi.org/10.1162/tacl_a_00009.
Zhuravskaya, E., Petrova, M., and Enikolopov, R. (2020). Political effects of the internet and social media. Annual review of economics, 12(1):415–438. DOI: https://doi.org/10.1146/annurev-economics-081919-050239.
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Copyright (c) 2025 Geovana S. Oliveira, João Pedro Lobo, Otávio Venâncio, Vinícius da F. Vieira, Jussara M. Almeida, Ana P. C. Silva, Ronan S. Ferreira, Carlos H. G. Ferreira
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