Version 1
: Received: 22 February 2021 / Approved: 24 February 2021 / Online: 24 February 2021 (16:35:09 CET)
How to cite:
Shooshtari, M.J.; Etemadfard, H.; Shad, R. Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran. Preprints2021, 2021020554. https://doi.org/10.20944/preprints202102.0554.v1
Shooshtari, M.J.; Etemadfard, H.; Shad, R. Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran. Preprints 2021, 2021020554. https://doi.org/10.20944/preprints202102.0554.v1
Shooshtari, M.J.; Etemadfard, H.; Shad, R. Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran. Preprints2021, 2021020554. https://doi.org/10.20944/preprints202102.0554.v1
APA Style
Shooshtari, M.J., Etemadfard, H., & Shad, R. (2021). Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran. Preprints. https://doi.org/10.20944/preprints202102.0554.v1
Chicago/Turabian Style
Shooshtari, M.J., Hossein Etemadfard and Rouzbeh Shad. 2021 "Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran" Preprints. https://doi.org/10.20944/preprints202102.0554.v1
Abstract
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 heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – 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 at vectorizing associated caption of each post, and two neural networks – multi-layer 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 citizens and tourists could not visit it and take photos to post on their Instagram accounts.
Keywords
Social media; Classification; Coronavirus; Word embedding; Artificial Intelligence
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
