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2024 | OriginalPaper | Buchkapitel

Interpretable Cross-Platform Coordination Detection on Social Networks

verfasst von : Auriant Emeric, Chomel Victor

Erschienen in: Complex Networks & Their Applications XII

Verlag: Springer Nature Switzerland

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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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Vorheriges Kapitel Algorithmic Amplification of Politics and Engagement Maximization on Social Media

Nächstes Kapitel Time-Dynamics of (Mis)Information Spread on Social Networks: A COVID-19 Case Study

It should be noted that once communities have been matched, OSNs communities structures can be studied in detail for other purposes.

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

10.

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

11.

De Domenico, M.: More is different in real-world multilayer networks. https://doi.org/10.1038/s41567-023-02132-1

12.

Enryu: Fun with large-scale tweet analysis. https://medium.com/@enryu9000/fun-with-large-scale-tweet-analysis-783c96b45df4

13.

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

14.

Graham, T., Bruns, A., Zhu, G., Campbell, R.: Like a virus: the coordinated spread of coronavirus disinformation

15.

Greene, D., Cunningham, P.: Producing a unified graph representation from multiple social network views

16.

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

17.

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

18.

Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection 467, 312–322. https://doi.org/10.1016/j.ins.2018.08.019

19.

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

20.

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

21.

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

22.

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

23.

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

24.

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

25.

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

26.

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

27.

Nori, H., Jenkins, S., Koch, P., Caruana, R.: InterpretML: A unified framework for machine learning interpretability

28.

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

29.

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

30.

Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using siamese BERT-networks. https://doi.org/10.48550/arXiv.1908.10084

31.

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

32.

Schönemann, P.H.: A generalized solution of the orthogonal procrustes problem 31(1), 1–10. https://doi.org/10.1007/BF02289451

33.

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

34.

Vargas, L., Emami, P., Traynor, P.: On the detection of disinformation campaign activity with network analysis. https://doi.org/10.48550/arXiv.2005.13466

35.

Weber, D., Neumann, F.: Amplifying influence through coordinated behaviour in social networks 11(1), 111. https://doi.org/10.1007/s13278-021-00815-2

36.

Wilson, T., Starbird, K.: Cross-platform disinformation campaigns: Lessons learned and next steps 1(1). https://doi.org/10.37016/mr-2020-002

37.

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

38.

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

Titel: Interpretable Cross-Platform Coordination Detection on Social Networks
verfasst von: Auriant Emeric
Chomel Victor
Verlag: Springer Nature Switzerland
Buch: Complex Networks & Their Applications XII
Print ISBN: 978-3-031-53502-4

Electronic ISBN: 978-3-031-53503-1

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53503-1_12

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