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9. References Blei, D., A.Y. Ng, and M. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–1022. Available at: (accessed May 2016). Blei, D. and J. Lafferty. 2006. “Dynamic Topic Models.” Proceedings of the 23rd International Conference on Machine Learning, 113–120, Pittsburgh, Pennsylvania, U.S.A., June 25 – 29, 2006. Doi: . Blei, D. and J. Lafferty. 2007. “A Correlated Topic Model of Science.” Annals of Applied Statistics 1

aa8-600b-4f32-b110-d02fbf7fd379, 25 June, 2015. [5] BBC. BBC forgotten list “sets precedent”., 26 June, 2015. [6] Bert-Jaap Koops. Forgetting footprints, shunning shadows: A critical analysis of the “Right to be Forgotten” in big data practice. SCRIPTed, 8(3):229-256, Dec. 2011. [7] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. the Journal of machine Learning research, 3:993-1022, 2003. [8] R. L. Bolton. The right to be forgotten: Forced amnesia in a technological age. John Marshall Journal of

works on microblog influence are abundant. However, research on the influence of microblog in specific fields, such as public health emergencies, is relatively insufficient. This study attempts to propose a microblog influence prediction model for public health emergencies, which is composed of user, time, and content features and which uses the random forest method ( Breiman, 2001 ) and the Best Match 25-based latent Dirichlet allocation model (LDA-BM25) ( Li, 2013 ). As this model is constructed specifically for public health emergencies, it highlights the features

References 1. Sun, L., Y. Liu, Q.-A. Zeng, F. Xiong. A Novel Rumor Diffusion Model Considering the Effect of Truth in Online Social Media. - International Journal of Modern Physics, Vol. 26, 2015, No 7, pp. 1-20. 2. He, Y., J. Tan. Study on Sina Micro-Blog Personalized Recommendation Based on Semantic Network. - Expert Systems with Applications, Vol. 42, 2015, pp. 4797-4804. 3. Zhou, X., S. Wua, C. Chen, G. Chen. Real-Time Recommendation for Microblogs. - Information Sciences, Vol. 279, 2014, pp. 301-325. 4. Blei, D. M., A. Y. Ng, M. I. Jordan. Latent Dirichlet

Aquacultural Animals FFRAA 1,876 Production Workers, All Other PW 1,750 Landscaping and Groundskeeping Workers LGW 1,988 3.2 Method Many researchers have utilized Latent Dirichlet Allocation (LDA) to identify topics from social media data ( Alkhodair et al., 2018 ; Asghari, Sierra-Sosa, & Elmaghraby, 2020 ; Giannetti, 2018 ). LDA is a three-level hierarchical Bayesian model ( Blei, Ng, & Jordan, 2003 ), it is an unsupervised machine learning technique used to create a representation of documents by topic, where each topic consisted of a set of words. In this study, we

. Zhang et al. (2015) H7N9 Weibo Analysis on the number of Weibo posts and the number of new confirmed cases There was a positive correlation between discussion and disease outbreak level, and Weibo served as a good medium to promote communications of public health. Li et al. (2020) COVID-19 Weibo Machine learning algorithms Weibo posts were classified into seven categories of situational information. Useful text features should be helpful in building an emergence response system. 2.2 Topic Modeling Using LDA Latent Dirichlet Allocation (LDA) assumes that a document is

analysis of the level of localism and journalistic professionalism in the Norwegian local media system. The analysis is based on structural analysis as well as a mix of descriptive and predictive statistical analyses on a corpus of 847,487 digital news articles collected from 156 online newspapers in 2015–2017, using Latent Dirichlet Allocation (LDA) topic modelling. The extent to which these assumptions are supported in turn enables a discussion of how local media system features contribute to media systems theory. In the following, we first discuss the relevant

topics serve as the auxiliary knowledge to regulate the topic learning process in SSCF. On two real-world datasets in two languages, experimental results show that the proposed SSCF consistently achieves better classification accuracy than state-of-the-art dataless baselines in terms of F 1 . We also observe that SSCF can even achieve superior performance to supervised classifiers supervised latent dirichlet allocation (sLDA) and support vector machine (SVM) on some specific tasks. To summarize, the main contributions of this paper are: We propose and formalize a new

matched. Since Google Trends data can be provided weekly and PubMed data are released monthly, we convert all weekly data to monthly by taking a four-week moving average. For every selected topic discussed above, we obtain Google Trends time-series data from January 2004 to January 2013. 2.2 Methodology The overall framework of the methodology is shown in Figure 2 , including generating topics from the obesity corpus using the latent Dirichlet allocation (LDA) algorithm ( Blei, Ng, & Jordan, 2003 ), obtaining time series of keyword search trends in Google Trends

systems, 18, 147, 2006. [5] Blei, D. M., Ng, A. Y., Jordan, M. I., Latent Dirichlet allocation , Journal of Machine Learning Research, 3, 993-1022, 2003. [6] Rai, A., Artificial Intelligence for Emotion Recognition , Journal of Artificial Intelligence Research & Advances, 1(2), 24-30, 2014. [7] Rai, A., Sakkaravarthi Ramanathan, Kannan, R. J., Quasi Opportunistic Supercomputing for Geospatial Socially Networked Mobile Devices , Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), IEEE 25th International Conference, 2016. [8] Rai, A