DOI : https://doi.org/10.18517/ijaseit.14.3.19402

Analyzing Abstention Discourse in Presidential Elections: Knowledge Discovery in X Using ML, LDA and SNA

Jayadi Butar Butar (1), Sofian Lusa (2), Sutia Handayani (3), Andi Akram Yusuf (4)
(1) Faculty of Computer Science, Universitas Indonesia, Depok, 16424, Indonesia
(2) Institut Pariwisata Trisakti, Jakarta, Indonesia
(3) Faculty of Computer Science, Universitas Indonesia, Depok, 16424, Indonesia
(4) Faculty of Computer Science, Universitas Indonesia, Depok, 16424, Indonesia
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How to cite (IJASEIT) :
Butar Butar, Jayadi, et al. “Analyzing Abstention Discourse in Presidential Elections: Knowledge Discovery in X Using ML, LDA and SNA”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 3, June 2024, pp. 1144-51, doi:10.18517/ijaseit.14.3.19402.
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As a social media platform, X (formerly Twitter) has become a massive source of real-time, unstructured data, providing valuable insights into people's opinions on various issues. One crucial social phenomenon that has attracted attention on social media is the discourse around abstention (commonly known as “golput” in Indonesia) in the context of the presidential election. Abstention refers to the deliberate act of refusing to vote. Understanding the patterns, preferences, and topics associated with abstention discourse can provide valuable knowledge for political analysis.  This study aims to discover knowledge based on patterns, sentiment polarization, and issues from unstructured X data to understand the discourse surrounding abstention in the 2024 Indonesian presidential election. The methodology involves collecting data from the X API, conducting Social Network Analysis (SNA) to analyze the social structure, preprocessing the data, and searching for the best sentiment analysis model through hyperparameter tuning on six Machine Learning (ML) models. Then, Latent Dirichlet Allocation (LDA) is employed with coherence score evaluation to identify topics related to the issue. The results indicate that 2,489 tweets discussing abstention were collected during the study period, exhibiting varied daily trends. SNA analysis reveals the formation of clusters within the dataset, alongside the identification of influential actors through three different centrality calculations. The sentiment analysis results show that the Logistic Regression (LR) model with count vectorizer is the best-performing model, with a predominance of positive sentiment polarity over negative sentiment. Evaluation of LDA using coherence scores indicates the presence of five topics related to abstention. This research contributes to knowledge discovery on the X platform by providing valuable insights into the discourse surrounding abstention in the Indonesian presidential election. These findings offer a deeper understanding of public opinion, political engagement, and election dynamics.

X. Shu and Y. Ye, “Knowledge Discovery: Methods from data mining and machine learning,” Soc. Sci. Res., vol. 110, no. April 2022, p. 102817, 2023, doi: 10.1016/j.ssresearch.2022.102817.

C. Zhang and J. Han, “Data Mining and Knowledge Discovery,” The Urban Book Series, pp. 797–814, 2021, doi: 10.1007/978-981-15-8983-6_42.

M. Aljabri, S. S. Aljameel, I. U. Khan, N. Aslam, S. M. B. Chrouf, and N. Alzahrani, “Machine Learning Model for Sentiment Analysis of COVID-19 Tweets,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 3, pp. 1206–1214, 2022, doi: 10.18517/ijaseit.12.3.14724.

K. L. Tsui, V. Chen, W. Jiang, F. Yang, and C. Kan, “Data Mining Methods and Applications,” Springer Handbooks, pp. 797–816, 2023, doi: 10.1007/978-1-4471-7503-2_38.

N. Pokhriyal, B. A. Valentino, and S. Vosoughi, “Quantifying participation biases on social media,” EPJ Data Sci., vol. 12, no. 1, 2023, doi: 10.1140/epjds/s13688-023-00405-6.

J. J. E. Macrohon, C. N. Villavicencio, X. A. Inbaraj, and J.-H. Jeng, “A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential Election,” Inf., vol. 13, no. 10, 2022, doi: 10.3390/info13100484.

S. Limboi and L. Diosan, “An unsupervised approach for Twitter Sentiment Analysis of USA 2020 Presidential Election,” 16th Int. Conf. Innov. Intell. Syst. Appl. INISTA 2022, pp. 1–6, 2022, doi:10.1109/INISTA55318.2022.9894264.

T. L. S. Pinto, B. M. Tabak, and D. O. Cajueiro, “How politics can influence the allocation of social program benefits: A case study of the Brazilian poverty reduction program Bolsa Família,” Econ. Anal. Policy, vol. 80, pp. 77–89, 2023, doi: 10.1016/j.eap.2023.07.009.

A. Vigna-Gómez, J. Murillo, M. Ramirez, A. Borbolla, I. Márquez, and P. K. Ray, “Design and analysis of tweet-based election models for the 2021 Mexican legislative election,” EPJ Data Sci., vol. 12, no. 1, 2023, doi: 10.1140/epjds/s13688-023-00401-w.

J. Downing and E. E. Brun, “‘I Think Therefore I Don’t Vote’: discourses on abstention, distrust and twitter politics in the 2017 French presidential election,” French Polit., vol. 20, no. 2, pp. 147–166, 2022, doi: 10.1057/s41253-021-00166-6.

T. Duile, “Challenging Hegemony: Nurhadi-Aldo and the 2019 Election in Indonesia,” J. Contemp. Asia, vol. 51, no. 4, pp. 537–563, 2021, doi: 10.1080/00472336.2020.1748896.

A. P. Logan, P. M. LaCasse, and B. J. Lunday, “Social network analysis of Twitter interactions: a directed multilayer network approach,” Soc. Netw. Anal. Min., vol. 13, no. 1, pp. 1–18, 2023, doi:10.1007/s13278-023-01063-2.

H. F. Karimi, Arini, S. U. Masruroh, and F. Mintarsih, “The Influence of Iteration Calculation Manipulation on Social Network Analysis Toward Twitter’s Users Against Hoax in Indonesia with Single Cluster Multi-Node Method Using Apache Hadoop Hortonworkstm Distribution,” 2018 6th Int. Conf. Cyber IT Serv. Manag. CITSM 2018, no. Citsm, pp. 1–6, 2019, doi:10.1109/CITSM.2018.8674374.

A. Chakraborty and N. Mukherjee, “Analysis and mining of an election-based network using large-scale twitter data: a retrospective study,” Soc. Netw. Anal. Min., vol. 13, no. 1, pp. 1–20, 2023, doi:10.1007/s13278-023-01081-0.

O. Monica, F. W. Wahida, and H. Fakhruroja, “The Relations between Influencers in Social Media and the Election Winning Party 2019,” Proceeding - 2019 Int. Conf. ICT Smart Soc. Innov. Transform. Towar. Smart Reg. ICISS 2019, pp. 0–4, 2019, doi:10.1109/ICISS48059.2019.8969801.

J. G. O’Reilly, “A proposal to strengthen Indonesian democracy,” Asian Polit. Policy, p. 12705, 2023, doi: 10.1111/aspp.12705.

M. Mohd et al., “poliWeet — Election prediction tool using tweets,” Software Impacts, vol. 17, p. 100542, Sep. 2023, doi:10.1016/j.simpa.2023.100542.

P. Chauhan, N. Sharma, and G. Sikka, “Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general election,” Social Network Analysis and Mining, vol. 13, no. 1, May 2023, doi: 10.1007/s13278-023-01087-8.

D. Kumar and F. Ahamad, “Opinion Extraction from Big Social Data Using Machine Learning Techniques: A Survey,” in AIP Conference Proceedings, 2023, vol. 2916, no. 1. doi: 10.1063/5.0179023.

H. Ali, H. Farman, H. Yar, Z. Khan, S. Habib, and A. Ammar, “Deep learning-based election results prediction using Twitter activity,” Soft Comput., vol. 26, no. 16, pp. 7535–7543, 2022, doi: 10.1007/s00500-021-06569-5.

Z. Dai and C. Higgs, “Social Network and Semantic Analysis of Roe v. Wade’s Reversal on Twitter,” Soc. Sci. Comput. Rev., no. 0123456789, 2023, doi: 10.1177/08944393231178602.

A. P. Logan, P. M. LaCasse, and B. J. Lunday, “Social network analysis of Twitter interactions: a directed multilayer network approach,” Soc. Netw. Anal. Min., vol. 13, no. 1, 2023, doi:10.1007/s13278-023-01063-2.

N. A. Diyana Suhaimi, S. Iffah Mohammad Salleh, S. A. Farhanah Abdul Hakim, P. Magalingam, N. B. Maarop, and M. Shanmugam, “Malaysian Politicians’ Connection Pattern on Twitter using SNA: A Case of Najib Razak,” 2021 Int. Congr. Adv. Technol. Eng. ICOTEN 2021, no. May 2018, pp. 1–10, 2021, doi:10.1109/ICOTEN52080.2021.9493501.

T. K. V. Sai, K. Lohith, M. P. Sai, K. Tejaswi, P. M. Ashok Kumar, and C. Karthikeyan, “Text Analysis on Twitter Data Using LSA and LDA,” 2023 Int. Conf. Comput. Commun. Informatics, ICCCI 2023, no. Iccci, pp. 1–6, 2023, doi: 10.1109/ICCCI56745.2023.10128417.

V. C. Hardita, R. Hammad, and A. Z. Amrullah, “Mandalika Modeling Topic on Social Media Using Latent Dirichlet Allocation,” ICCoSITE 2023 - Int. Conf. Comput. Sci. Inf. Technol. Eng. Digit. Transform. Strateg. Facing VUCA TUNA Era, pp. 1–5, 2023, doi:10.1109/ICCoSITE57641.2023.10127821.

K. Sethia, M. Saxena, M. Goyal, and R. K. Yadav, “Framework for Topic Modeling using BERT, LDA and K-Means,” 2022 2nd Int. Conf. Adv. Comput. Innov. Technol. Eng. ICACITE 2022, pp. 2204–2208, 2022, doi: 10.1109/ICACITE53722.2022.9823442.

E. Del Valle Martín and L. De La Fuente Valentín, “Sentiment analysis methods for politics and hate speech contents in Spanish language: a systematic review,” IEEE Lat. Am. Trans., vol. 21, no. 3, pp. 408–418, 2023, doi: 10.1109/TLA.2023.10068844.

M. Wyawahare, A. Dhanawade, M. Dhopade, S. Dharyekar, and A. Dhole, “Twitter sentiment analysis on political tweets,” AIP Conf. Proc., vol. 2981, no. 1, p. 20022, Dec. 2023, doi: 10.1063/5.0182743.

A. A. Lina, H. Khadidja, J. Imene, and M. Neila, “Analyzing US Airline Customer Sentiment on Twitter using Multinomial Logistic Regression and Feature Reduction,” Colloq. Inf. Sci. Technol. Cist, pp. 265–270, 2023, doi: 10.1109/CiSt56084.2023.10409979.

P. Assiroj, A. Kurnia, and S. Alam, “The performance of Naïve Bayes, support vector machine, and logistic regression on Indonesia immigration sentiment analysis,” Bull. Electr. Eng. Informatics, vol. 12, no. 6, pp. 3843–3852, 2023, doi: 10.11591/eei.v12i6.5688.

R. Chen and R. Dong, “The Relationship Between Twitter Sentiment and Stock Performance: A Decision Tree Approach,” Proceedings of the 56th Hawaii International Conference on System Sciences, 2023, doi: 10.24251/hicss.2023.592.

G. Thangarasu and K. R. Alla, “Detection of Cyberbullying Tweets in Twitter Media Using Random Forest Classification,” 13th IEEE Symp. Comput. Appl. Ind. Electron. ISCAIE 2023, pp. 113–117, 2023, doi: 10.1109/ISCAIE57739.2023.10165118.

E. Edgari, J. Thiojaya, and N. N. Qomariyah, “The Impact of Twitter Sentiment Analysis on Bitcoin Price during COVID-19 with XGBoost,” 5th Int. Conf. Comput. Informatics, ICCI 2022, pp. 337–342, 2022, doi: 10.1109/ICCI54321.2022.9756123.

Y. Setiawan, N. Maulidevi, and K. Surendro, “The Use of Dynamic n-Gram to Enhance TF-IDF Features Extraction for Bahasa Indonesia Cyberbullying Classification,” 2023, pp. 200–205. doi:10.1145/3587828.3587858.

K. Kukushkin, Y. Ryabov, and A. Borovkov, “Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling,” Data, vol. 7, no. 12, 2022, doi: 10.3390/data7120173.

B. V. Barde and A. M. Bainwad, “An overview of topic modeling methods and tools,” Proc. 2017 Int. Conf. Intell. Comput. Control Syst. ICICCS 2017, vol. 2018-Janua, pp. 745–750, 2017, doi:10.1109/ICCONS.2017.8250563.

M. Tanhapour, A. A. Safaei, and H. Shakibian, “Personal health record system based on social network analysis,” Multimedia Tools and Applications, vol. 81, no. 19, pp. 27601–27628, Mar. 2022, doi:10.1007/s11042-022-12910-3.

A. Gerber, “The Detection of Conversation Patterns in South African Political Tweets Through Social Network Analysis,” Commun. Comput. Inf. Sci., vol. 1551 CCIS, pp. 15–31, 2022, doi:10.1007/978-3-030-95070-5_2.

A. J. Oroh, Y. Bandung, and L. M. Zagi, “Detection of the Key Actor of Issues Spreading Based on Social Network Analysis in Twitter Social Media,” Proc. - 2021 IEEE Asia Pacific Conf. Wirel. Mobile, APWiMob 2021, pp. 206–212, 2021, doi:10.1109/APWiMob51111.2021.9435268.

R. Parvathi, Y. S. Asish, and V. Pattabiraman, “Analysis report for statistics in the Twitter network,” Deep Nat. Lang. Process. AI Appl. Ind. 5.0, pp. 50–58, 2021, doi: 10.4018/978-1-7998-7728-8.ch003.

I. Williams, “Contemporary Applications of Actor Network Theory,” Contemp. Appl. Actor Netw. Theory, pp. 1–283, 2020, doi:10.1007/978-981-15-7066-7.

J. Forestal, “Social Media, Social Control, and the Politics of Public Shaming,” Am. Polit. Sci. Rev., pp. 1–15, 2023, doi:10.1017/S0003055423001053.

S. Vosoughi, D. Roy, and S. Aral, “The spread of true and false news online,” Science (80-. )., vol. 359, no. 6380, pp. 1146–1151, Mar. 2018, doi: 10.1126/science.aap9559.

J. Weismueller, P. Harrigan, K. Coussement, and T. Tessitore, “What makes people share political content on social media? The role of emotion, authority and ideology,” Comput. Human Behav., vol. 129, no. December 2021, p. 107150, 2022, doi:10.1016/j.chb.2021.107150.

K. Clark et al., “Advancing the ethical use of digital data in human research: challenges and strategies to promote ethical practice,” Ethics Inf. Technol., vol. 21, no. 1, pp. 59–73, 2019, doi:10.1007/s10676-018-9490-4.

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