2
How to cite this paper
APA: Firdaniza, F., Ruchjana, B., Chaerani, D & Radianti, J. (2022). Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users. International Journal of Data and Network Science, 6(3), 659-668.
Chicago/Turabian: Firdaniza, F., Ruchjana, B., Chaerani, D & Radianti, J. 2022. "Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users." International Journal of Data and Network Science 6, no. 3 (2022): 659-668.
AMA: Firdaniza, F., Ruchjana, B., Chaerani, D & Radianti, J. Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users. International Journal of Data and Network Science. 2022;6(3):659-668.
References
Alp, Z. Z., & Öğüdücü, Ş. G. (2019). Influence Factorization for identifying authorities in Twitter. Knowledge-Based Systems, 163, 944–954.
Bhowmick, A. (2019). Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks. IEEE Transactions on Computational Social Systems, 6(3), 441–455.
Fernando, S., Amador Díaz López, J., Şerban, O., Gómez-Romero, J., Molina-Solana, M., & Guo, Y. (2019). Towards a large-scale twitter observatory for political events. Future Generation Computer Systems, 110(3).
Firdaniza, F., Ruchjana, B. N., Chaerani, D., & Radianti, J. (2022). Information Diffusion Model in Twitter : A Systematic Literature Review. Information,13(1).
Firdaniza, Ruchjana, B. N., & Chaerani, D. (2021). Information diffusion model using continuous time Markov chain on social media. Journal of Physics: Conference Series, 1722(1).
Firdaus, S. N., Ding, C., & Sadeghian, A. (2018). Retweet: A popular information diffusion mechanism – A survey paper. Online Social Networks and Media, 6, 26–40.
Hamzehei, A., Jiang, S., Koutra, D., Wong, R., & Chen, F. (2017). Topic-based social influence measurement for social networks. Australasian Journal of Information Systems, 21.
Han, J., Kamber, M., and Pei,J., (2012) 'Data Mining: Concept and Techniques'. Elsevier,Inc.
Jiang, C., Chen, Y., & Liu, K. J. R. (2014). Evolutionary dynamics of information diffusion over social networks. IEEE Transactions on Signal Processing, 62(17), 4573–4586.
Kawamoto, T. (2013). A stochastic model of tweet diffusion on the Twitter network. Physica A: Statistical Mechanics and Its Applications, 392(16), 3470–3475. https://doi.org/10.1016/j.physa.2013.03.048
Kim, Y., & Seo, J. (2020). Detection of Rapidly Spreading Hashtags via Social Networks. IEEE Access, 8, 39847–39860.
Kumar, S., Saini, M., Goel, M., & Aggarwal, N. (2020). Modeling Information Diffusion in Online Social Networks Using SEI Epidemic Model. Procedia Computer Science, 171(2019), 672–678.
Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? Proceeding of the 19th International Conference on World wide web, 591-600.
Kwon, J. (2017). Effects of source influence and peer referrals on information diffusion in Twitter. In Industrial Management and Data Systems (Vol. 117, Issue 5, 896–909).
Lawrence, K.D., Klimberg, R.K. and Lawrence, S.M. (2009) Fundamentals of Forecasting Using Excel, Australian Health Service Alliance.
Li, J., Peng, W., Li, T., Sun, T., Li, Q., & Xu, J. (2014). Social network user influence sense-making and dynamics prediction. Expert Systems with Applications, 41, 5115-5124.
Mittal, D., Suthar, P., Patil, M., Pranaya, P. G. S., Rana, D. P., & Tidke, B. (2020). Social Network Influencer Rank Recommender Using Diverse Features from Topical Graph. Procedia Computer Science, 167(2019), 1861–1871.
Oo, M. M., & Lwin, M. T. (2020). Detecting Influential Users in a Trending Topic Community Using Link Analysis Approach. International Journal of Intelligent Engineering and Systems, 13(6), 178–188.
Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018). Influence analysis in social networks: A survey. Journal of Network and Computer Applications, 106, 17–32.
Qasem, Z., Jansen, M., Hecking, T., & Hoppe, H. U. (2017). Using attractiveness model for actors ranking in social media networks. Computational Social Networks, 4(1).
Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management, 1-27.
Ross, S. M. (2010). Introduction to Probability Models (10th ed.). Elsevier Inc.
Shuai, X., Ding, Y., Busemeyer, J., Chen, S., Sun, Y., & Tang, J. (2012). Modeling indirect influence on Twitter. International Journal on Semantic Web and Information Systems, 8(4), 20–36.
Simmie, D., Vigliotti, M. G., & Hankin, C. (2014). Ranking twitter influence by combining network centrality and influence observables in an evolutionary model. Journal of Complex Networks, 2(4), 495-517.
Song, X., Chi, Y., Hino, K., & Tseng, B. L. (2007). Information flow modeling based on diffusion rate for prediction and ranking. 16th International World Wide Web Conference, WWW2007, 191–200.
Tidke, B., Mehta, R., & Dhanani, J. (2020). Consensus-based aggregation for identification and ranking of top-k influential nodes. Neural Computing and Applications, 32(14), 10275–10301.
Varshney, D., Kumar, S., & Gupta, V. (2017). Predicting information diffusion probabilities in social networks: A Bayesian networks based approach. Knowledge-Based Systems, 133, 66–76.
Yoo, E., Rand, W., Eftekhar, M., & Rabinovich, E. (2016). Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises. Journal of Operations Management, 45, 123-133.
Zhang, L., Luo, M., & Boncella, R. J. (2020). Product information diffusion in a social network. Electronic Commerce Research, 20(1), 3–19.
Zheng, Z., Yang, H., Fu, Y., Fu, D., Podobnik, B., & Stanley, H. E. (2018). Factors influencing message dissemination through social media. Physical Review E, 97(6), 1–6.
Zhu, T., Wang, B., Wu, B., & Zhu, C. (2014). Maximizing the spread of influence ranking in social networks. Information Sciences, 278, 535–544.
https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/acces accessed on De-cember 31, 2021
Bhowmick, A. (2019). Temporal Sequence of Retweets Help to Detect Influential Nodes in Social Networks. IEEE Transactions on Computational Social Systems, 6(3), 441–455.
Fernando, S., Amador Díaz López, J., Şerban, O., Gómez-Romero, J., Molina-Solana, M., & Guo, Y. (2019). Towards a large-scale twitter observatory for political events. Future Generation Computer Systems, 110(3).
Firdaniza, F., Ruchjana, B. N., Chaerani, D., & Radianti, J. (2022). Information Diffusion Model in Twitter : A Systematic Literature Review. Information,13(1).
Firdaniza, Ruchjana, B. N., & Chaerani, D. (2021). Information diffusion model using continuous time Markov chain on social media. Journal of Physics: Conference Series, 1722(1).
Firdaus, S. N., Ding, C., & Sadeghian, A. (2018). Retweet: A popular information diffusion mechanism – A survey paper. Online Social Networks and Media, 6, 26–40.
Hamzehei, A., Jiang, S., Koutra, D., Wong, R., & Chen, F. (2017). Topic-based social influence measurement for social networks. Australasian Journal of Information Systems, 21.
Han, J., Kamber, M., and Pei,J., (2012) 'Data Mining: Concept and Techniques'. Elsevier,Inc.
Jiang, C., Chen, Y., & Liu, K. J. R. (2014). Evolutionary dynamics of information diffusion over social networks. IEEE Transactions on Signal Processing, 62(17), 4573–4586.
Kawamoto, T. (2013). A stochastic model of tweet diffusion on the Twitter network. Physica A: Statistical Mechanics and Its Applications, 392(16), 3470–3475. https://doi.org/10.1016/j.physa.2013.03.048
Kim, Y., & Seo, J. (2020). Detection of Rapidly Spreading Hashtags via Social Networks. IEEE Access, 8, 39847–39860.
Kumar, S., Saini, M., Goel, M., & Aggarwal, N. (2020). Modeling Information Diffusion in Online Social Networks Using SEI Epidemic Model. Procedia Computer Science, 171(2019), 672–678.
Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? Proceeding of the 19th International Conference on World wide web, 591-600.
Kwon, J. (2017). Effects of source influence and peer referrals on information diffusion in Twitter. In Industrial Management and Data Systems (Vol. 117, Issue 5, 896–909).
Lawrence, K.D., Klimberg, R.K. and Lawrence, S.M. (2009) Fundamentals of Forecasting Using Excel, Australian Health Service Alliance.
Li, J., Peng, W., Li, T., Sun, T., Li, Q., & Xu, J. (2014). Social network user influence sense-making and dynamics prediction. Expert Systems with Applications, 41, 5115-5124.
Mittal, D., Suthar, P., Patil, M., Pranaya, P. G. S., Rana, D. P., & Tidke, B. (2020). Social Network Influencer Rank Recommender Using Diverse Features from Topical Graph. Procedia Computer Science, 167(2019), 1861–1871.
Oo, M. M., & Lwin, M. T. (2020). Detecting Influential Users in a Trending Topic Community Using Link Analysis Approach. International Journal of Intelligent Engineering and Systems, 13(6), 178–188.
Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018). Influence analysis in social networks: A survey. Journal of Network and Computer Applications, 106, 17–32.
Qasem, Z., Jansen, M., Hecking, T., & Hoppe, H. U. (2017). Using attractiveness model for actors ranking in social media networks. Computational Social Networks, 4(1).
Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management, 1-27.
Ross, S. M. (2010). Introduction to Probability Models (10th ed.). Elsevier Inc.
Shuai, X., Ding, Y., Busemeyer, J., Chen, S., Sun, Y., & Tang, J. (2012). Modeling indirect influence on Twitter. International Journal on Semantic Web and Information Systems, 8(4), 20–36.
Simmie, D., Vigliotti, M. G., & Hankin, C. (2014). Ranking twitter influence by combining network centrality and influence observables in an evolutionary model. Journal of Complex Networks, 2(4), 495-517.
Song, X., Chi, Y., Hino, K., & Tseng, B. L. (2007). Information flow modeling based on diffusion rate for prediction and ranking. 16th International World Wide Web Conference, WWW2007, 191–200.
Tidke, B., Mehta, R., & Dhanani, J. (2020). Consensus-based aggregation for identification and ranking of top-k influential nodes. Neural Computing and Applications, 32(14), 10275–10301.
Varshney, D., Kumar, S., & Gupta, V. (2017). Predicting information diffusion probabilities in social networks: A Bayesian networks based approach. Knowledge-Based Systems, 133, 66–76.
Yoo, E., Rand, W., Eftekhar, M., & Rabinovich, E. (2016). Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises. Journal of Operations Management, 45, 123-133.
Zhang, L., Luo, M., & Boncella, R. J. (2020). Product information diffusion in a social network. Electronic Commerce Research, 20(1), 3–19.
Zheng, Z., Yang, H., Fu, Y., Fu, D., Podobnik, B., & Stanley, H. E. (2018). Factors influencing message dissemination through social media. Physical Review E, 97(6), 1–6.
Zhu, T., Wang, B., Wu, B., & Zhu, C. (2014). Maximizing the spread of influence ranking in social networks. Information Sciences, 278, 535–544.
https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/acces accessed on De-cember 31, 2021