Identify User Behavior based on Tweet Type on Twitter Platform using Agglomerative Hierarchical Clustering

Authors

  • Prawiro Weninggalih Telkom University, Bandung
  • Yuliant Sibaroni Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v6i3.4342

Keywords:

Twitter, User Behavior, Clustering, Agglomerative Hierarchical Clustering

Abstract

Information dissemination can occur through any media, including social media. One of the social media that has become a forum for disseminating information is Twitter. Through user-uploaded tweets, not a few comments are positive (praise/support) or negative (blasphemy), depending on the tweet. This study chooses politics as a discussion. Data crawling was carried out to obtain a dataset and raise the topic of Joko Widodo as a President of Indonesia, whose work is considered poor by the public, so they want him to resign immediately. This makes it interesting because we can identify user behavior from tweets about the topic. The choice of this topic was based on a lot of users who discussed it, so it was trending on Twitter. Preprocessing stage aims to eliminate missing values. After that, it then goes through the feature extraction process. The agglomerative Hierarchical Clustering Algorithm of the clustering method is applied in this research. This algorithm can directly set how many clusters to facilitate the clustering process. The result obtained 3 clusters with different user behavior. Negative user behavior is found in cluster 1, while positive user behavior is found in cluster 2.

References

L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, “Target-dependent Twitter Sentiment Classification,†pp. 151–160, 2011, doi: 10.5555/2002472.

V. Effendy, A. Novantirani, and M. K. Sabariah, “Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Methodâ€.

A. Gupta, A. Joshi dan P. Kumaraguru, “Identifying and Characterizing User Communities on Twitter during Crisis Events,†pp. 23-26, 2012.

Z. Zengin Alp and Ş. Gündüz Öğüdücü, “Identifying topical influencers on twitter based on user behavior and network topology,†Knowledge-Based Systems, vol. 141, pp. 211–221, Feb. 2018, doi: 10.1016/J.KNOSYS.2017.11.021.

D. W. Wardani dan Y. Wardhani, “Detecting Spammers on Twitter by Identifying User Behavior and Tweet-Based Features,†UTeM Open Journal System, vol. 10, pp. 81-84, 2018.

S. He, H. Wang dan Z. H. Jiang, “Identifying User Behavior on Twitter Based on Multi-scale Entropy,†IEEE, pp. 381-384, 2014.

A. Mogadala dan V. Varma, “Twitter User Behavior Understanding with Mood Transition Prediction,†pp. 31-34, 2012.

Z. Xu and Q. Yang, “Analyzing user retweet behavior on twitter,†Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, pp. 46–50, 2012, doi: 10.1109/ASONAM.2012.18.

G. Wang, X. Zhang, S. Tang, H. Zheng, and B. Y. Zhao, “Unsupervised clickstream clustering for user behavior analysis,†Conference on Human Factors in Computing Systems - Proceedings, pp. 225–236, May 2016, doi: 10.1145/2858036.2858107.

M. Maia, J. Almeida, and V. Almeida, “Identifying user behavior in online social networks,†Proceedings of the 1st Workshop on Social Network Systems, SocialNets’08 - Affiliated with EuroSys 2008, pp. 13–18, 2008, doi: 10.1145/1435497.1435498.

F. Amato et al., “Recognizing human behaviours in online social networks,†Computers and Security, vol. 74, pp. 355–370, May 2018, doi: 10.1016/J.COSE.2017.06.002.

“Identifying Biased Users in Online Social Networks to Enhance the Accuracy of Sentiment Analysis: A User Behavior-Based Approach | Request PDF.†https://www.researchgate.net/publication/351575532_Identifying_Biased_Users_in_Online_Social_Networks_to_Enhance_the_Accuracy_of_Sentiment_Analysis_A_User_Behavior-Based_Approach (accessed Jun. 21, 2022).

H. Gao, R. Zhou, C. Cheng, X. Sun dan R. Xin, “Understanding User Behavior on Social Network During COVID-19: Twitter,†International Core Journal of Engineering, vol. 6, no. 11, pp. 342-450, 2020.

A. Go, R. Bhayani dan L. Huang, “Twitter Sentiment Classification using Distant Supervision,†2009.

K. Deng, L. Xing, L. Zheng, H. Wu, P. Xie dan F. Gao, “A User Identification Algorithm Based on User Behavior Analysis in Social Networks,†IEEE Access, vol. 7, pp. 47114-47123, 2019.

U. Dutta, R. Hanscom, J. S. Zhang, R. Han, T. Lehman, Q. Lv dan S. Mishra, “Analyzing Twitter Users’ Behavior Before and After Contact by Russia’s Internet Research Agency,†Proc. ACM Hum.-Comput. Interact., vol. 5, 2021.

T. Tang, M. Hämäläinen, A. Virolainen, and J. Makkonen, “Understanding user behavior in a local social media platform by social network analysis,†Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, MindTrek 2011, pp. 183–188, 2011, doi: 10.1145/2181037.2181067.

“The 5 Clustering Algorithms Data Scientists Need to Know | by George Seif | Towards Data Science.†https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 (accessed Jun. 21, 2022).

D. E. Cahyani and I. Patasik, “Performance comparison of tf-idf and word2vec models for emotion text classification,†Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2780–2788, Oct. 2021, doi: 10.11591/EEI.V10I5.3157.

S. Mardianti, M. Z. Naf'an dan I. Hidayatulloh, “EKSTRAKSI TF-IDF N-GRAM DARI KOMENTAR PELANGGAN PRODUK SMARTPHONE PADA WEBSITE E-COMMERCE,†ResearchGate, pp. 79-84, 2018.

“SIMULASI JEJARING JALAN KOTA PONTIANAK DENGAN BETWEENESS CENTRALITY DAN DEGREE CENTRALITY | Pratama | Jurnal TIN Universitas Tanjungpura.†https://jurnal.untan.ac.id/index.php/jtinUNTAN/article/view/23752 (accessed Jun. 21, 2022).

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Published

2022-07-25

Issue

Vol. 6 No. 3 (2022): Juli 2022

Section

Articles

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