مجلة الجامعة الإسلامية للعلوم التطبيقية

Diversity of Information Diffusion in Online Social Networks: A Comparative Study

, Aaquib Hussain Ganai, Rana Hashmy, Hilal Ahmad Khanday, Hufsa Manzoor

التخصص العام: Engineering

التخصص الدقيق: Computer Networks

https://doi.org/10.63070/jesc.2025.015
DownloadPDF
الملخص

Online social networks are being created for social interactions and those users that fulfill this paradigm are the real users of online social networks. As these interactions became diverse, the information that these diverse interactions were carrying also flood the online social networks, thereby creates the diffusions of this diverse information and which in turn creates the numerous phenomena among the users of online social networks. These phenomena are so diverse that as you will change the scale of your view, you will notice a different phenomenon that has been adopted by online social network under scanner, from macroscopic view to microscopic view you will notice this flip of phenomena. Keeping an eye on the already sorted out diversity of works, that are hidden or prevalent, we are going to lay a concrete survey to this diversity in this paper by keeping the motive that every existing attempt paves a way for the new one.

 

Keywords: Social networks, Online social networks, Social Network Analysis (SNA), Information diffusion, Influential user, Spam, Online social graph.

مراجع

[1]        Li, M., Wang, X., Gao, K., & Zhang, S. (2017). A Survey on Information Diffusion in Online Social Networks: Models and Methods. Information, 8(4), 118. https://doi.org/10.3390/info8040118

[2]        Iacopini, I., Karsai, M., & Barrat, A. (2024). The temporal dynamics of group interactions in higher-order social networks. Nature Communications, 15, 7391. https://doi.org/10.1038/s41467-024-50918-5

[3]        Wong, A., Ho, S., Olusanya, O., Antonini, M. V., & Lyness, D. (2021). The use of social media and online communications in times of pandemic COVID-19. Journal of the Intensive Care Society, 22(3), 255–260. https://doi.org/10.1177/1751143720966280.

[4]         Guille, A., Hacid, H., Favre, C., &Zighed, D. A. (2013). Information Diffusion in Online Social Networks: A Survey. SIGMOD Record, 42(2), 17-28. https://doi.org/10.1145/2503792.2503797

[5]         Kurka, D. B. (2015). Online social networks: Knowledge extraction from information diffusion and analysis of spatio-temporal phenomena (Dissertation). Retrieved from [Institution repository link]

[6]         Sima, D., Fountain, T., & Karasuk, P. (n.d.). Advanced Computer Architecture: A Design Space Approach (1st ed.). Pearson.

[7]         Cole, W. D. (n.d.). An Information Diffusion Approach for Detecting Emotional Contagion in Online Social Networks (Master’s thesis, Arizona State University). Retrieved from [Institution repository link]

[8]         Tenenbaum, A. S. (n.d.). Computer Networks (1st ed.). Pearson.

[9]        Sermpezis, P., & Spyropoulos, T. (2013). Information diffusion in heterogeneous networks: The configuration model approach. In IEEE INFOCOM 2013 (pp. 3261-3266). IEEE. https://doi.org/10.1109/INFCOM.2013.6566986

[10]     Kumar, K. P. K. (2015). Information Diffusion Modeling to Counter Semantic Attacks in Online Social Networks (PhD thesis). Retrieved from http://hdl.handle.net/10603/123660

[11]    Sun, Q., Li, Y., Hu, H., & Cheng, S. (2019).A model for competing information diffusion in social networks. IEEE Access, 7, 67916-67922. https://doi.org/10.1109/ACCESS.2019.2918812

[12]    Arnaboldi, V., Conti, M., Passarella, A., & Dunbar, R. (2017).Online social networks and information diffusion: The role of ego networks. Online Social Networks and Media, 1, 44-55. https://doi.org/10.1016/j.osnem.2017.04.001

[13]    Wang, F., Wang, H., Xu, K., Wu, J., & Jia, X. (2013). Characterizing information diffusion in online social networks with linear diffusive model. In Proceedings - 2013 IEEE 33rd International Conference on Distributed Computing Systems, ICDCS 2013 (pp. 307-316). IEEE. https://doi.org/10.1109/ICDCS.2013.14

[14]    arXiv:1802.01729

[15]    Behera, P. C. (2020). Data mining technique for tracking of information diffusion in online social network. International Journal of Latest Research in Science and Technology, 5.

[16]    Han, J., Kamber, M., & Pei, J. (n.d.).Data Mining (3rd ed.).

[17]    Erlandsson, F., Br?dka, P., Borg, A., & Johnson, H. (2016). Finding influential users in social media using association rule learning. Entropy, 18(5), 164. https://doi.org/10.3390/e18050164

[18]    Wang, F., Wang, H., & Xu, K. (n.d.). Dynamic Mathematical Modeling of Information Diffusion in Online Social Networks. Arizona State University. Retrieved from [Arizona State University link].

[19]    Cazabet, R., Amblard, F., & Hanachi, C. (2010). Detection of overlapping communities in dynamical social networks. In 2010 IEEE International Conference on Social Computing (pp. 309-314). IEEE. https://doi.org/10.1109/SocialCom.2010.51

[20]    Wu, S. (n.d.).The Dynamics of Information Diffusion on Online Social Networks (PhD thesis).

[21]    Guille, A., Hacid, H., Favre, C., &Zighed, D. A. (2013). Information diffusion in online social networks: A survey. SIGMOD Record, 42(2), 17–28. https://doi.org/10.1145/2503792.2503797

[22]    Wang, C. (2014). Jumping over the network threshold: Information diffusion on information sharing websites. PhD Thesis, City University of Hong Kong. Retrieved from https://scholars.cityu.edu.hk/en/theses/theses(6f918870-1270-4232-bb3c-e66ce4bb4a05).html

[23]    Das, A., Gollapudi, S., &Kiciman, E. (2014). Effect of persuasion on information diffusion in social networks.

[24]    Silva, A., Guimar?es, S., Meira Jr., W., & Zaki, M. (2013).ProfileRank: Finding relevant content and influential users based on information diffusion. https://doi.org/10.1145/2501025.2501033

[25]    Matsubara, Y., Sakurai, Y., Prakash, B. A., Li, L., &Faloutsos, C. (2012).Rise and fall patterns of information diffusion: Model and implications. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 6–14). https://doi.org/10.1145/2339530.2339537

[26]    Yang, Y., Tang, J., Leung, C. W., Sun, Y., Chen, Q., Li, J., & Yang, Q. (2015). RAIN: Social role-aware information diffusion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI'15) (pp. 367–373).

[27]    Liu, L., Qu, B., Chen, B., Hanjalic, A., & Wang, H. (2017). Modeling of information diffusion on social networks with applications to WeChat. arXiv:1704.03261

[28]    Ren, X., & Zhang, Y. (2016). Predicting information diffusion in social networks with users’ social roles and topic interests. In Ma, S. et al. (Eds.), Information RetrievalTechnology. AIRS 2016. Lecture Notes in Computer Science, vol. 9994 (pp. 343–354). Springer. https://doi.org/10.1007/978-3-319-48051-0_30

[29]    Susarla, A., Oh, J. H., & Tan, Y. (2012).Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research, 23(1), 23-41. https://doi.org/10.1287/isre.1110.0404

[30]    Taxida, L., Fischer, P. M., De Nies, T., Mannens, E., Verborgh, R., & Van de Walle, R. (2015).Modeling information diffusion in social media as provenance in W3C Prov. ACM Transactions on the Web.

[31]    Farajtabar, M., Gomez-Rodriguez, M., Wang, Y., Li, S., Zha, H., & Song, L. (2015). Co-evolutionary dynamics of information diffusion and network structure. In Proceedings of the 24th International Conference on World Wide Web (pp. 619–620). ACM.

[32]    Dhamal, S., Prabuchandran, K. J., &Narahari, Y. (2015).A multi-phase approach for improving information diffusion in social networks. In The 14th International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2015), May 4–8, 2015, Istanbul, Turkey.

[33]    Huang, J.-P., Wang, C.-Y., & Wei, H.-Y. (2011). Strategic information diffusion through online social networks. In Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL '11). ACM. https://doi.org/10.1145/2093698.2093786

[34]    Taxidou, I., & Fischer, P. M. (2014). Online analysis of information diffusion in Twitter. In Proceedings of the 23rd International Conference on World Wide Web (WWW '14 Companion) (pp. 1313–1318). ACM. https://doi.org/10.1145/2567948.2580050

[35]    Jiang, Y., & Jiang, J. C. (2015). Diffusion in social networks: A multi-agent perspective. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(2), 198–213. https://doi.org/10.1109/TSMC.2014.2339198

[36]    Weng, L. (n.d.).Information Diffusion on Online Social Networks (PhD thesis, Indiana University). Retrieved from http://hdl.handle.net/2027.42/89838

[37]    Matsubara, Y., Sakurai, Y., Prakash, B. A., Li, L., &Faloutsos, C. (2017).Nonlinear dynamics of information diffusion in social networks. ACM Transactions on the Web, 11(2), Article 11, 40 pages. https://doi.org/10.1145/3057741

[38]    Wang, F., Wang, H., & Xu, K. (n.d.). Dynamic Mathematical Modeling of Information Diffusion in Online Social Networks. Arizona State University. Retrieved from https://www2.cs.arizona.edu/~bzhang/CCW2012/slides/xu.pdf

[39]    Farajtabar, M., Wang, Y., Rodriguez, M., Li, S., Zha, H., & Song, L. (2015). COEVOLVE: A joint point process model for information diffusion and network co-evolution. Journal of Machine Learning Research, 18, 1305–1353.

[40]    Yang, C., Sun, M., Liu, H., Han, S., Liu, Z., & Luan, H. (2018). Neural Diffusion Model for Microscopic Cascade Prediction. arXiv:1812.08933

[41]    Gatti, M. A. de C., Appel, A. P., dos Santos, C. N., Pinhanez, C. S., Cavalin, P. R., & Neto, S. B. (2013).A simulation-based approach to analyze information diffusion in microblogging online social network. In 2013 Winter Simulations Conference (WSC) (pp. 1685-1696). IEEE. https://doi.org/10.1109/WSC.2013.6721550

[42]    Kim, H., &Yoneki, E. (2012). Influential neighbours selection for information diffusion in online social networks. In 2012 21st International Conference on Computer Communications and Networks (ICCCN) (pp. 1-7). IEEE. https://doi.org/10.1109/ICCCN.2012.6289230

[43]    Hu, Y., Song, R. J., & Chen, M. (2017). Modeling for information diffusion in online social networks via hydrodynamics. IEEE Access, 5, 128-135. https://doi.org/10.1109/ACCESS.2016.2605009

[44]    Davoudi, A., & Chatterjee, M. (2016). Prediction of information diffusion in social networks using dynamic carrying capacity. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 2466–2469). IEEE. https://doi.org/10.1109/BigData.2016.7840883

[45]    Saito, K., Kimura, M., Ohara, K., &Motoda, H. (2013). Detecting changes in information diffusion patterns over social networks. ACM Transactions on Intelligent Systems and Technology, 4(3), Article 55. https://doi.org/10.1145/2499862

[46]    Wang, Z., Yang, Y., Pei, J., & Chen, E. (2016). Activity Maximization by Effective Information Diffusion in Social Networks. IEEE Transactions on Knowledge and Data Engineering, PP. https://doi.org/10.1109/TKDE.2017.2740284.

[47]    Kim, H., &Yoneki, E. (2012). Influential Neighbours Selection for Information Diffusion in Online Social Networks. 2012 21st International Conference on Computer Communications and Networks, ICCCN 2012 - Proceedings, 1–7. https://doi.org/10.1109/ICCCN.2012.6289230.

[48]    Wang, Q. (2016). Towards Understanding Information Diffusion about Infrastructure, An Empirical Study of Twitter Data. Retrieved from https://www.irbnet.de/daten/iconda/CIB_DC29666.pdf.

[49]    Farajtabar, M., Wang, Y., Gomez-Rodriguez, M., Li, S., Zha, H., & Song, L. (2017). COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution. Journal of Machine Learning Research, 18, 1305–1353.

[50]    Du, N., Song, L., Woo, H., &Zha, H. (2013).Uncover Topic-Sensitive Information Diffusion Networks. AISTATS.

[51]    Bakshy, E. (2011). Information Diffusion and Social Influence in Online Networks. PhD Thesis, University of Michigan. Retrieved from http://hdl.handle.net/2027.42/89838.

[52]    Jalali, M. S., Ashouri rad, A., Herrera-Restrepo, O., & Zhang, H. (2016).Information Diffusion through Social Networks: The Case of an Online Petition. Expert Systems with Applications, 187–197. https://doi.org/10.1016/j.eswa.2015.09.014.

[53]    Jiang, C., Chen, Y., & Liu, K. J. R. (2014). Modeling Information Diffusion Dynamics over Social Networks. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 1095–1099. https://doi.org/10.1109/ICASSP.2014.6853766.

[54]    Mahdi, M. K., &Almanory, H. N. (2019). Modeling the Information Diffusion Overlapped Nodes Using SFA-ICBDMA. IJRTE. https://doi.org/10.35940/ijrte.B1710.078219.

[55]    Wang, F., Wang, H., Xu, K., Wu, J., & Jia, X. (2013). Characterizing Information Diffusion in Online Social Networks with Linear Diffusive Model. In 2013 IEEE 33rd International Conference on Distributed Computing Systems (pp. 307–316). IEEE. https://doi.org/10.1109/ICDCS.2013.105.

[56]    Tanwar, P., & Priyanka. (2019). Spam Diffusion in Social Networking Media Using Dirichlet Allocation. IJITEE. Retrieved from https://www.ijitee.org/wp-content/uploads/papers/v8i12/I7898078919.pdf.

[57]    Bright, J., Margetts, H., Hale, S., &Yasseri, T. The Use of Social Media for Research and Analysis: A Feasibility Study. Oxford Institute Press. Retrieved from https://www.bl.uk/collection-items/use-of-social-media-for-research-and-analysis-a-feasibility-study#.

[58]    Obregon, J., Song, M., & Jung, J.-Y. (2019).InfoFlow: Mining Information Flow Based on User Community in Social Networking Services. IEEE Access, 7, 48024–48036.

[59]    Jain, L., &Katarya, R. (2019). Discover Opinion Leader in Online Social Network Using Firefly Algorithm. Expert Systems with Applications, 122, 1–15.

[60]    Dai, J., Wang, B., Sheng, J., Sun, Z., Khawaja, F. R., Ullah, A., Dejene, D. A., & Duan, G. (2019). Identifying Influential Nodes in Complex Networks Based on Local Neighbor Contribution. IEEE Access, 7, 131719–131731.

[61]    Pothineni, D., Mishra, P., & Rasheed, A. (2012). Social Thermodynamics: Modeling Communication Dynamics in Social Networks. In The First International Conference on Future Generation Communication Technologies (pp. 76–82). IEEE.

[62]    Alemany, J., Del Val, E., Alberola, J. M., & Garc?a-Fornés, A. (2019). Metrics for Privacy Assessment when Sharing Information in Online Social Networks. IEEE Access, 7, 143631–143645.

[63]    Al-Azim, N. A. R., Gharib, T. F., Afify, Y., & Hamdy, M. (2020). Influence Propagation: Interest Groups and Node Ranking Models. Physica A: Statistical Mechanics and its Applications, 124247.

[64]    Carey, A. (200). Global Information Workforce Study. Retrieved from www.idc.com.

[65]    Gregg, M., Watkins, S., Mays, G., Ries, C., Bandes, R. M., & Franklin, B. (2006). Hack the Stack: Using Snort and Ethereal to Master the 8 Layers of an Insecure Network. Elsevier.

[66]    Horowitz, E., Sahni, S., & Rajasekaran, S. (2008). Fundamentals of Computer Algorithms (2nd ed.).

[67]    Rosen, K. H., &Krithivasan, K.Discrete Mathematics and Its Applications (McGraw-Hill).

[68]    Anh, N., Son, D., Thu, H., Kuznetsov, S., & Vinh, N. T. Q. (2018). A Method for Determining Information Diffusion Cascades on Social Networks. Eastern-European Journal of Enterprise Technologies, 6, 61–69. https://doi.org/10.15587/1729-4061.2018.150295.

[69]    Ganai, A.H., Hashmy, R. & Khanday, H.A. Finding Information Diffusion’s Seed Nodes in Online Social Networks Using a Special Degree Centrality. SN COMPUT. SCI. 5, 333 (2024). https://doi.org/10.1007/s42979-024-02683-x

[70]    Ganai, A.H., Hashmy, R., Khanday, H.A. et al. IDT-Cascade: a novel information dissemination tree model for influential cascade detection in online social networks. Int. j. inf. tecnol. (2025). https://doi.org/10.1007/s41870-025-02573-2