Social Media Platforms (SMP) like Twitter and Facebook have become public influencing medium in today’s digital age which facilitate community interactions. These necessitates the monitoring of public participation in these SMP. This work performs such an study by examining the user engagement patterns in social media in Indonesia during the national elections (#Pemilu2024), Ramadan celebrations (#Ramadan2024), and climate change discussions (#ClimateChange). The data for the study was collected during a period of six months from (January to June 2024) using event-specific hashtags and keywords. To identify the user engagement patterns datamining and Natural Language Processing (NLP) tools were utilized. The findings show that the Twitter platform has higher user engagement in morning with news updates and visual updates at evening. The Facebook show user engagement in afternoon with videos and evening engagement with shared articles. The Sentiment Analysis (SA) and network analysis was performed over the dataset and the findings have shown that higher positive sentiments towards elections and Ramadan but argumentative towards climate change discussions. The Twitter show rapid communication effectiveness compared to Facebook. Further the youth prefer faster update and older expect detailed content sharing.
Keywords
Sentiment Analysis, Natural Language Processing, Twitter, Facebook, Social Media Platforms.
G. Bouvier and D. Machin, “Critical Discourse Analysis and the challenges and opportunities of social media,” Critical Discourse Studies and/in Communication, pp. 39–53, Nov. 2020, doi: 10.4324/9781003050353-3.
Z. Wang, C. S. Chong, L. Lan, Y. Yang, S. Beng Ho, and J. C. Tong, “Fine-grained sentiment analysis of social media with emotion sensing,” 2016 Future Technologies Conference (FTC), Dec. 2016, doi: 10.1109/ftc.2016.7821783.
K. S. Yogi, V. Dankan Gowda, D. Sindhu, H. Soni, S. Mukherjee, and G. C. Madhu, “Enhancing Accuracy in Social Media Sentiment Analysis through Comparative Studies using Machine Learning Techniques,” 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), pp. 1–6, Apr. 2024, doi: 10.1109/ickecs61492.2024.10616441.
M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P.-M. Cuenca-Jiménez, “A review on sentiment analysis from social media platforms,” Expert Systems with Applications, vol. 223, p. 119862, Aug. 2023, doi: 10.1016/j.eswa.2023.119862.
S. Alipour et al., “Cross-platform social dynamics: an analysis of ChatGPT and COVID-19 vaccine conversations,” Scientific Reports, vol. 14, no. 1, Feb. 2024, doi: 10.1038/s41598-024-53124-x.
Md. M. Alam, A. Lutfi, and A. Alsaad, “Antecedents and Consequences of Customers’ Engagement with Pro-Environmental Consumption-Related Content on Social Media,” Sustainability, vol. 15, no. 5, p. 3974, Feb. 2023, doi: 10.3390/su15053974.
C. R. Harris et al., “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, Sep. 2020, doi: 10.1038/s41586-020-2649-2.
H. Karamollaoglu, I. A. Dogru, M. Dorterler, A. Utku, and O. Yildiz, “Sentiment Analysis on Turkish Social Media Shares through Lexicon Based Approach,” 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sep. 2018, doi: 10.1109/ubmk.2018.8566481.
J. Tian and H. Zhou, “Sentiment Analysis Algorithm in Social Media Data of Cancer Patients,” 2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB), Apr. 2024, doi: 10.1109/iceib61477.2024.10602717.
T. Bikku, J. Jarugula, L. Kongala, N. D. Tummala, and N. Vardhani Donthiboina, “Exploring the Effectiveness of BERT for Sentiment Analysis on Large-Scale Social Media Data,” 2023 3rd International Conference on Intelligent Technologies (CONIT), Jun. 2023, doi: 10.1109/conit59222.2023.10205600.
R. Singh and P. Sharma, “An Overview of Social Media and Sentiment Analysis,” 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Oct. 2021, doi: 10.1109/iscon52037.2021.9702359.
V. Joseph, C. P. Lora, and N. T, “Exploring the Application of Natural Language Processing for Social Media Sentiment Analysis,” 2024 3rd International Conference for Innovation in Technology (INOCON), Mar. 2024, doi: 10.1109/inocon60754.2024.10511841.
V. Singh, H. V. Kaushik, and Reshma, “Social Media Sentiment Analysis Using Twitter Dataset,” 2024 Second International Conference on Data Science and Information System (ICDSIS), May 2024, doi: 10.1109/icdsis61070.2024.10594648.
M. V. S, P. Mastan Rao, and S. Babu, “Evaluating Social Responsible Attitudes and Opinions using Sentiment Analysis – An Indian Sentiment,” 2022 3rd International Conference on Computing, Analytics and Networks (ICAN), Nov. 2022, doi: 10.1109/ican56228.2022.10007315.
K. S. Yogi, V. Dankan Gowda, D. Sindhu, H. Soni, S. Mukherjee, and G. C. Madhu, “Enhancing Accuracy in Social Media Sentiment Analysis through Comparative Studies using Machine Learning Techniques,” 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), pp. 1–6, Apr. 2024, doi: 10.1109/ickecs61492.2024.10616441.
J. Tian and H. Zhou, “Sentiment Analysis Algorithm in Social Media Data of Cancer Patients,” 2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB), Apr. 2024, doi: 10.1109/iceib61477.2024.10602717.
V. Joseph, C. P. Lora, and N. T, “Exploring the Application of Natural Language Processing for Social Media Sentiment Analysis,” 2024 3rd International Conference for Innovation in Technology (INOCON), Mar. 2024, doi: 10.1109/inocon60754.2024.10511841.
V V. Singh, H. V. Kaushik, and Reshma, “Social Media Sentiment Analysis Using Twitter Dataset,” 2024 Second International Conference on Data Science and Information System (ICDSIS), May 2024, doi: 10.1109/icdsis61070.2024.10594648.
B. Mridula, A. H. Juliet, and N. Legapriyadharshini, “Deciphering Social Media Sentiment for Enhanced Analytical Accuracy: Leveraging Random Forest, KNN, and Naive Bayes,” 2024 10th International Conference on Communication and Signal Processing (ICCSP), Apr. 2024, doi: 10.1109/iccsp60870.2024.10543836.
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Lijun Tang
Lijun Tang
Faculty of Humanities and Social Sciences, Macao Polytechnic University, R. de
Luís Gonzaga Gomes, Macao, China.
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Cite this article
Jing Zhang and Lijun Tang, “Public Participation Monitoring: Social Media Data
Mining and Analysis of User Engagement Patterns”, Journal of Machine and Computing, pp. 971-979, October 2024. doi:10.53759/7669/jmc202404090.
