Abstract
Social networks have become an important part of human life. There have been recently several studies on using Latent Dirichlet Allocation (LDA) to analyze text corpora extracted from social platforms to discover underlying patterns of user data. However, when we wish to discover the major contents of a social network (e.g., Facebook) on a large scale, the available approaches need to collect and process published data of every person on the social network. This is against privacy rights as well as time and resource consuming. This paper tackles this problem by focusing on fan pages, a class of special accounts on Facebook that have much more impact than those of regular individuals. We proposed a vector representation for Facebook fan pages by using a combination of LDA-based topic distributions and interaction indices of their posts. The interaction index of each post is computed based on the number of reactions and comments, and works as the weight of that post in making of the topic distribution of a fan page. The proposed representation shows its effectiveness in fan page topic mining and clustering tasks when experimented on a collection of Vietnamese Facebook fan pages. The inclusion of interaction indices of the posts increases the fan page clustering performance by 9.0% on Silhouette score in the case of optimal number of clusters when using K-means clustering algorithm. These results will help us to build a system that can track trending contents on Facebook without acquiring the individual user’s data.
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References
Datareportal, “Social Media Users by Platform,” 2019. Available: https://datareportal.com/social-media-users. Accessed 19 Nov 2019
Yau, C.-K., Porter, A., Newman, N., Suominen, A.: Clustering scientific documents with topic modeling. Scientometrics 100, 767–786 (2014)
Kim, S.-W., Gil, J.-M.: Research paper classification systems based on TF-IDF and LDA schemes. Hum. centric Comput. Inf. Sci. 9(1), 1–21 (2019). https://doi.org/10.1186/s13673-019-0192-7
Pengtao, X., Eric, P.X.: Integrating document clustering and topic modeling. In: Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, AUAI Press, Virginia, United States, pp. 694–703 (2013)
Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I.: A hybrid framework for news clustering based on the DBSCAN-martingale and LDA. MLDM 2016. LNCS (LNAI), vol. 9729, pp. 170–184. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41920-6_13
Eliana, S., Camilo, M., Raimundo, A.: Topic modeling of Twitter conversations (2018)
Zhou, T., Haiy, Z.: A text mining research based on LDA topic modelling. In: International Conference on Computer Science, Engineering and Information Technology, pp. 201–210 (2016)
Jiamthapthaksin, R.: Thai text topic modeling system for discovering group interests of Facebook young adult users. In: 2016 2nd International Conference on Science in Information Technology (ICSITech), pp. 91–96. IEEE 2016
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Darling, W.M.: A theoretical and practical implementation tutorial on topic modeling and gibbs sampling. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies, pp. 642–647 (2011)
Social Bakers Vietnamese Statistics. Available: https://www.socialbakers.com/statistics/facebook/pages/total/vietnam. Accessed 19 Nov 2019
pyvi toolkit. Available: https://pypi.org/project/pyvi/. Accessed 19 Nov 2019
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rousseeuw, P.J.: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Moody, C.E.: Mixing dirichlet topic models and word embeddings to make lda2vec. arXiv arXiv:1605.02019 (2016)
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This research is funded by the University of Danang – University of Science and Technology under grant number T2017-02-93.
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Phan, V.H., Ninh, D.K., Ninh, C.K. (2020). An Effective Vector Representation of Facebook Fan Pages and Its Applications. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_55
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DOI: https://doi.org/10.1007/978-3-030-63119-2_55
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