Clustering followers of influencers accounts based on likes and comments on Instagram Platform
Puji Winar Cahyo(1*), Muhammad Habibi(2)
(1) Department of Informatics, FTTI UNJANI, Yogyakarta
(2) Universitas Jenderal Achmad Yani Yogyakarta
(*) Corresponding Author
Abstract
The promotion of goods or services is now facilitated by the dissemination of information through Instagram. Dissemination of information is usually done by influencers or promotional accounts. The account used certainly has a lot of followers. Because of the large amount of follower data in that account, it can be grouped into the same characters. This is done to determine the potential for promotion using social media accounts. This study uses data from 2 popular accounts. The first account is an artist with the username ayutingting92. The second account is Infounjaya, the official promotion account from Jenderal Achmad Yani University, Yogyakarta. The results of grouping can divide follower data into two cluster groups with different interactions. The basic difference between the two groups is the number of likes and comments. The infounjaya account analysis results showed that of 4,906 followers, only 3,211 followers were actively involved in the interaction, 1,695 followers were passive followers who did not like or did not comment on the interaction. Meanwhile, the results of the ayutingting92 follower cluster show that out of 1 million sample data followers, only 13,591 followers were actively involved in the interaction of likes and comments, 986,409 were passive followers.
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[1] M. Irwansyah, “Kajian Humas Digital: Transformasi Dan Kontribusi Industri 4.0 Pada Stratejik Kehumasan,” J. Teknol. Inf. dan Komun., vol. 7, no. 1, pp. 27–36, 2018.
[2] E. Sivadas and R. P. Jindal, “Alternative measures of satisfaction and word of mouth,” J. Serv. Mark., vol. 31, no. 2, pp. 119–130, 2017.
[3] P. Katri, “Celebrity Endorsement of Meta-Analysis?,” West. J. Nurs. Res., vol. 31, no. 4, pp. 435–436, 2009.
[4] R. Ahmed, S. Seedani, M. Ahuja, and S. Paryani, “Impact of Celebrity Endorsement on Consumer Buying Behavior,” SSRN Electron. J. ·, 2015.
[5] P. L. Breves, N. Liebers, M. Abt, and A. Kunze, “The Perceived Fit between Instagram Influencers and the Endorsed Brand,” J. Advert. Res., vol. 59, no. 4, p. 440 LP-454, Dec. 2019.
[6] S. Kim, S. Yoo, J. Han, and M. Gerla, “How Are Social Influencers Connected in Instagram ?,” SocInfo 2017, vol. 2, pp. 257–264, 2017.
[7] M. De Veirman, V. Cauberghe, and L. Hudders, “Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude,” Int. J. Advert., vol. 36, no. 5, pp. 798–828, Sep. 2017.
[8] C. Abidin, “Visibility labour: Engaging with Influencers’ fashion brands and #OOTD advertorial campaigns on Instagram,” Media Int. Aust., vol. 161, no. 1, pp. 86–100, 2016.
[9] M. Habibi and P. W. Cahyo, “Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, pp. 399–408, 2019.
[10] I. Sen, A. Aggarwal, S. Mian, S. Singh, P. Kumaraguru, and A. Datta, “Worth its Weight in Likes: Towards Detecting Fake Likes on Instagram,” WebSci’18 10th ACM Conf. Web, pp. 205–209, 2018.
[11] R. Katarya and O. P. Verma, “An effective web page recommender system with fuzzy c-mean clustering,” pp. 21481–21496, 2017.
[12] V. N. Phu, N. Duy, D. Vo, T. Ngoc, T. Vo, T. Ngoc, and T. A. Nguyen, “Fuzzy C-means for english sentiment classification in a distributed system,” Appl. Intell., 2016.
[13] P. W. Cahyo and E. Winarko, “Model Monitoring Sebaran Penyakit Demam Berdarah di Indonesia Berdasarkan Analisis Pesan Twitter,” Universitas Gadjah Mada Yogyakarta, 2017.
[14] M. Habibi and Sumarsono, “Implementation of Cosine Similarity in an automatic classifier for comments,” vol. 3, no. 2, pp. 110–118, 2018.
[15] P. W. Cahyo, “Klasterisasi Tipe Pembelajar Sebagai Parameter Evaluasi Kualitas Pendidikan Di Perguruan Tinggi,” Teknomatika, vol. 11, no. 1, pp. 49–55, 2018.
[16] T. Wang, “A Flexible Possibilistic C-Template Shell Clustering Method with Adjustable Degree of Deformation,” in Fuzzy Systems (FUZZ-IEEE), 2016, pp. 1516–1522.
[17] K. P. Sinaga, J. Hsieh, J. B. M. Benjamin, and M.-S. Yang, “Modified Relational Mountain Clustering Method,” in Computing Sciences and Engineering (ICCSE), 2018, pp. 690–701.
[18] R. Winkler, F. Klawonn, and R. Kruse, “Fuzzy C-Means in High Dimensional Spaces Fuzzy c-means in high dimensional spaces,” Int. J. Fuzzy Syst. Appl., vol. 11, no. 1, 2010.
[19] Timothy J. Ross, Fuzzy Logic With Engineering Applications, Third Edit. United Kingdom: John Wiley & Sons Ltd, 2010.
[20] J. C. Bezdek, “FCM : THE FUZZY c-MEANS CLUSTERING ALGORITHM,” vol. 10, no. 2, pp. 191–203, 1984.
[21] M. Ren, P. Liu, Z. Wang, and J. Yi, “A Self-Adaptive Fuzzy ? -Means Algorithm for Determining the Optimal Number of Clusters,” Comput. Intell. Neurosci., vol. 2016, no. 1, 2016.
[22] Z. Hu, Y. Y. Bodyanskiy, and O. K. Tyshchenko, “A Cascade Deep Neuro-Fuzzy System for High- Dimensional Online Possibilistic Fuzzy Clustering,” in 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT), 2016, pp. 119–122.
[23] B. Choubin, K. Solaimani, H. M. Roshan, and A. Malekian, “Watershed classification by remote sensing indices : A fuzzy c-means clustering approach,” J. Mt. Sci., vol. 14, pp. 2053–2063, 2017.
DOI: https://doi.org/10.22146/ijccs.53028
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