Use of text mining to compare quality and accreditation content generated on social media by Peruvian and Chilean universities

This study analyzes quality and accreditation content generated on online social media by Chilean and Peruvian universities. Keywords regarding university external communication strategies are compared between two types of universities (accredited and unaccredited). Data are collected from Twitter and Facebook by applying text mining techniques to count the most frequently used keywords. The random forest algorithm is applied to perform a binary classification. The results show that the terms most used by universities were “quality,” “service,” and “management.” The results obtained from the classifier are in agreement with the results obtained by text mining, where the amount of publications related to quality and accreditation do not correlate with university rankings. It is concluded that universities should revise their content strategies on social media to achieve greater differentiation to secure their classification in university rankings.