On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking

By Loulwa S. Al-Sumait, Daniel Barbará, Carlotta Domeniconi. 2008 Eighth IEEE International Conference on Data Mining, 2008

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Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics" (2012, methodology): “Another possible use of our model, if applied in an online form [1], would be to better understand the dynamics of check ins due to groups of users being in cities for various events.”
Information diffusion in online social networks: a survey (2013, background): “[1] propose an online topic model, more precisely, a non-Markov on-line LDA Gibbs sampler topic model, called OLDA.”
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “s, topics [76] T) 2005 Sampling A T) ery [73] model(DTM) 2006 algorithm A process topics [77] T) 2006 Sampling A time. [78] AM 2006 Sampling A method correlations [79] A 2007 sampling A algorithm ork [80] A) 2008 Sampling A method Detection [36] A 2008 A multi-aspect [66] A 2009 Sampling A collectio”
Applications of Topic Models (2017, background): “There has been some work on using topic models to detect emerging events by searching for changes in topic probability [AlSumait et al., 2008].”
Topic Modeling Using Latent Dirichlet allocation (2021, unknown): “AlSumait et al. proposed an extension for tracking the evolution of topics over time, known as On-line LDA [3].”
BTM: Topic Modeling over Short Texts (2014, methodology): “We now compare the running time and memory requirement of the Gibbs sampling algorithm of BTM with LDA.”