Prioritization of Benchmarking Framework for Social Media Content Based on Integration of AHP and Group TOPSIS: A Case Study of Instagram Travel

Prioritization of social media content is a difficult process due to the variability of data, different assessment criteria, criterion significance, and alternative members. In the present research, there is no precise approach for ranking social media content based on Multi-criteria decision making (MCDM). This study suggests a revolutionary approach to social media content prioritization based on the integration of AHP and TOPSIS. We present the benchmarking framework in this paper. The approach began with the extraction of the dataset, followed by the determination of the criteria and the construction of the TOPSIS decision matrix. 75 Instagram accounts were included in the data. In the meanwhile, AHP is utilized to determine the weighting based on expert judgments. A decision matrix is constructed using alternative criteria. The results indicate that expert weighing is appropriate, with a consistency rate of less than 10%. Additionally, the AHP and TOPSIS integration findings show that the prioritizing benchmark framework provides the similarity priority rating for the top 10 Instagram, based on the different individual expert weighting.