@article{abulaish_modeling_2018,
	title = {Modeling {Topic} {Evolution} in {Twitter}: {An} {Embedding}-{Based} {Approach}},
	volume = {6},
	issn = {2169-3536},
	shorttitle = {Modeling {Topic} {Evolution} in {Twitter}},
	doi = {10.1109/ACCESS.2018.2878494},
	abstract = {In last two decades, online social networks have grown vertically as well as horizontally. Due to various users activities in these networks, huge amount of data, mainly textual, is being generated that can be analyzed at different levels of granularity for various purposes, including behavior analysis, sentiment analysis, and predictive modeling. In this paper, we propose a word embedding-based approach to analyze users-centric tweets to observe their behavior evolution in terms of the topics discussed by them over a period of time. We also present a word embedding-based proximity measure to monitor temporal transitions between the topics using five topic evolution events - emergence, persistence, convergence, divergence, and extinction. The proximity between a pair of topics is defined as a function of the content and contextual similarity between their word distributions, wherein the contextual similarity is calculated using word embedding. The proposed approach is evaluated over three Twitter datasets in line with the existing state-of-the-art approaches in literature and the experimental results are encouraging.},
	journal = {IEEE Access},
	author = {Abulaish, Muhammad and Fazil, Mohd},
	year = {2018},
	keywords = {deep learning},
	pages = {64847--64857},
}

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