  A novel Twitter context aided content caching (TAC) framework is proposed for
enhancing the caching efficiency by taking advantage of the legibility and
massive volume of Twitter data. For the purpose of promoting the caching
efficiency, three machine learning models are proposed to predict latent events
and events popularity, utilizing collect Twitter data with geo-tags and
geographic information of the adjacent base stations (BSs). Firstly, we propose
a latent Dirichlet allocation (LDA) model for latent events forecasting taking
advantage of the superiority of the LDA model in natural language processing
(NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding
approach and LSTM with continuous skip-gram-Geo-aware embedding approach for
the events popularity forecasting. Lastly, we associate the predicted latent
events and the popularity of the events with the caching strategy. Extensive
practical experiments demonstrate that: (1) The proposed TAC framework
outperforms the conventional caching framework and is capable of being employed
in practical applications thanks to the associating ability with public
interests. (2) The proposed LDA approach conserves superiority for natural
language processing (NLP) in Twitter data. (3) The perplexity of the proposed
skip-gram-based LSTM is lower compared with the conventional LDA approach. (4)
Evaluation of the model demonstrates that the hit rates of tweets of the model
vary from 50% to 65% and the hit rate of the caching contents is up to
approximately 75\% with smaller caching space compared to conventional
algorithms.
