Evaluation of COVID-19 Spread Effect on the Commercial Instagram Posts using ANN: A Case Study on Holy Shrine, Mashhad, Iran

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the covid-19 spread. this study presents a heuristicapproach to classify commercial instagram posts (cips) and explores how the businesses around the holy shrine were impacted by the pandemic. two datasets of instagram posts (one gathered data from march 14th to april 10th, 2020, when holy shrine and nearby shops were closed, and one extracted data from the same period in 2019), two-word embedding models – aimed atvectorizing associated caption of each post, and two neural networks – multilayer perceptron and convolutional neural network – were employed to classify cips in 2019. among the scenarios defined for the 2019 cips classification, the results revealed that the combination of mlp and cbow achieved the best performance, which was then used for the 2020 cips classification. it is found out that the fraction of cips to total instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. moreover, the portion of non-commercial instagram posts (ncips) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the holy shrine was closed, mashhad residents and tourists could not visit it and take photos to post on their instagram accounts