Abstract
Numerous disinformation campaigns are operating on social networks to influence public opinion. Detecting these campaigns primarily involves identifying coordinated communities. As disinformation campaigns can take place on several social networks at the same time, the detection must be cross-platform to get a proper picture of it. To encode the different types of coordination, a multi-layer network is built. We propose a scalable coordination detection algorithm, adapted from the Louvain algorithm and the Iterative Probabilistic Voting Consensus algorithm. This algorithm is applied to the previously built multi-layer network. Users from different social networks are then embedded in a common space to link communities with similar interests. This paper introduces an interpretable and modular framework used on three datasets to prove its effectiveness for coordination detection method and to illustrate it with real examples.
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Notes
- 1.
It should be noted that once communities have been matched, OSNs communities structures can be studied in detail for other purposes.
References
Coordinated inauthentic behavior explained. https://about.fb.com/news/2018/12/inside-feed-coordinated-inauthentic-behavior/
Doppelganger - media clones serving russian propaganda. https://www.disinfo.eu/doppelganger/
Raising online defenses through transparency and collaboration. https://about.fb.com/news/2023/08/raising-online-defenses/
Alimadadi, F., Khadangi, E., Bagheri, A.: Community detection in facebook activity networks and presenting a new multilayer label propagation algorithm for community detection 33(10), 089 (1950). https://doi.org/10.1142/S0217979219500899
Baumgartner, J., Zannettou, S., Squire, M., Blackburn, J.: The pushshift telegram dataset
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation 15(6), 1373–1396. https://doi.org/10.1162/089976603321780317
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks 2008(10), P10,008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Bovet, A., Makse, H.A.: Influence of fake news in twitter during the 2016 US presidential election 10(1), 7. https://doi.org/10.1038/s41467-018-07761-2
Calatayud, J., Bernardo-Madrid, R., Neuman, M., Rojas, A., Rosvall, M.: Exploring the solution landscape enables more reliable network community detection 100(5), 052308. https://doi.org/10.1103/PhysRevE.100.052308
Chacón, J.E., Rastrojo, A.I.: Minimum adjusted rand index for two clusterings of a given size 17(1), 125–133. https://doi.org/10.1007/s11634-022-00491-w
De Domenico, M.: More is different in real-world multilayer networks. https://doi.org/10.1038/s41567-023-02132-1
Enryu: Fun with large-scale tweet analysis. https://medium.com/@enryu9000/fun-with-large-scale-tweet-analysis-783c96b45df4
Gao, S., Zhang, Z., Su, S., Yu, P.S.: REBORN: transfer learning based social network alignment 589, 265–282. https://doi.org/10.1016/j.ins.2021.12.081
Graham, T., Bruns, A., Zhu, G., Campbell, R.: Like a virus: the coordinated spread of coronavirus disinformation
Greene, D., Cunningham, P.: Producing a unified graph representation from multiple social network views
Huang, X., Chen, D., Ren, T., Wang, D.: A survey of community detection methods in multilayer networks 35(1), 1–45. https://doi.org/10.1007/s10618-020-00716-6
Jakesch, M., Garimella, K., Eckles, D., Naaman, M.: Trend alert: How a cross-platform organization manipulated twitter trends in the indian general election 5, 1–19. https://doi.org/10.1145/3479523
Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection 467, 312–322. https://doi.org/10.1016/j.ins.2018.08.019
Lei, T., Ji, L., Wang, G., Liu, S., Wu, L., Pan, F.: Transformer-based user alignment model across social networks 12(7), 1686. https://doi.org/10.3390/electronics12071686
Liu, L., Zhang, Y., Fu, S., Zhong, F., Hu, J., Zhang, P.: ABNE: an attention-based network embedding for user alignment across social networks 7, 23,595–23,605. https://doi.org/10.1109/ACCESS.2019.2900095
Malhotra, A., Totti, L., Meira Jr., W., Kumaraguru, P., Almeida, V.: Studying user footprints in different online social networks. https://doi.org/10.48550/arXiv.1301.6870
Micallef, N., Sandoval-Castañeda, M., Cohen, A., Ahamad, M., Kumar, S., Memon, N.: Cross-platform multimodal misinformation: Taxonomy, characteristics and detection for textual posts and videos 16, 651–662. https://doi.org/10.1609/icwsm.v16i1.19323
Moore, M.: Fake accounts on social media, epistemic uncertainty and the need for an independent auditing of accounts. https://doi.org/10.14763/2023.1.1680
Morstatter, F., Shao, Y., Galstyan, A., Karunasekera, S.: From Alt-Right to Alt-Rechts: Twitter analysis of the 2017 german federal election. In: Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18, pp. 621–628. ACM Press. https://doi.org/10.1145/3184558.3188733
Murero, M.: Coordinated inauthentic behavior: An innovative manipulation tactic to amplify COVID-19 anti-vaccine communication outreach via social media 8, 1141416. https://doi.org/10.3389/fsoc.2023.1141416
Nguyen, N., Caruana, R.: Consensus clusterings. In: Seventh IEEE International Conference on Data Mining (ICDM 2007), pp. 607–612. https://doi.org/10.1109/ICDM.2007.73. ISSN: 2374-8486
Nori, H., Jenkins, S., Koch, P., Caruana, R.: InterpretML: A unified framework for machine learning interpretability
Pierri, F., Artoni, A., Ceri, S.: HoaxItaly: a collection of italian disinformation and fact-checking stories shared on twitter in 2019. https://doi.org/10.48550/arXiv.2001.10926
Pierri, F., Piccardi, C., Ceri, S.: A multi-layer approach to disinformation detection in US and italian news spreading on twitter 9(1), 1–17. https://doi.org/10.1140/epjds/s13688-020-00253-8
Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using siamese BERT-networks. https://doi.org/10.48550/arXiv.1908.10084
Satuluri, V., et al.: SimClusters: Community-based representations for heterogeneous recommendations at twitter. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’20, pp. 3183–3193. Association for Computing Machinery. https://doi.org/10.1145/3394486.3403370
Schönemann, P.H.: A generalized solution of the orthogonal procrustes problem 31(1), 1–10. https://doi.org/10.1007/BF02289451
Sharma, K., Zhang, Y., Ferrara, E., Liu, Y.: 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, pp. 1441–1451. ACM. https://doi.org/10.1145/3447548.3467391
Vargas, L., Emami, P., Traynor, P.: On the detection of disinformation campaign activity with network analysis. https://doi.org/10.48550/arXiv.2005.13466
Weber, D., Neumann, F.: Amplifying influence through coordinated behaviour in social networks 11(1), 111. https://doi.org/10.1007/s13278-021-00815-2
Wilson, T., Starbird, K.: Cross-platform disinformation campaigns: Lessons learned and next steps 1(1). https://doi.org/10.37016/mr-2020-002
Zhang, C., Gupta, A., Kauten, C., Deokar, A.V., Qin, X.: Detecting fake news for reducing misinformation risks using analytics approaches 279(3), 1036–1052. https://doi.org/10.1016/j.ejor.2019.06.022
Zhang, Y., Sharma, K., Liu, Y.: Capturing cross-platform interaction for identifying coordinated accounts of misinformation campaigns. In: Kamps, J., et al. (eds.) Advances in Information Retrieval, Lecture Notes in Computer Science, pp. 694–702. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-28238-6_61
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Emeric, A., Victor, C. (2024). Interpretable Cross-Platform Coordination Detection on Social Networks. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_12
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