This study provides a framework for the comparative assessment of the key industry aspects of competitiveness among logistics services and the logistics systems of enterprises in the informational environment. Frequently, the relationships between a consumer and a company created by means of the informational environment determine how the enterprise positions itself in the market. For instance, the evaluation of a company’s representation in the information field is an essential aspect of determining the company’s competitiveness. The study suggests a set of special metrics for measuring the representation of digital components and other aspects of an enterprise’s digital image via data gathering and analysis of the most encountered tokens. The proposed automated analysis algorithm allows companies to examine their image in the digital environment and implement effective decisions. The functionality of the algorithm fosters data collection, helping to form the desired image of the company. Tokens of several thematic groups on social media are collected during the process, and the most significant of them that are valuable for the competitiveness of the enterprise are extracted. The outcome can be used for the tracking of the dynamics of key parameters of an enterprise’s image and for conducting a comparative analysis of the digital image of its competitors.

A large amount of text data flows through the web every day in the context of various comments and reactions of users to social network content. Such information tokens can act as quality metrics for content, a group, or an industry. This information can be valuable for decision makers and content managers of higher education institutions (HEIs) as well as researchers studying the marketing activities of HEIs. Topic modelling, as one of the methods for analysing textual information, can help practitioners and researchers create analytical models that allow them to make important management decisions based on audience reactions to content. As part of this task, the goal of this research is to develop a topic model based on LDA to uncover key topics of posts in 15 university groups on the "VK" social network of five leading universities located in Saint Petersburg, Russia. For each university, posts from three groups were collected: "Overhead," "Main" and "Enrolment." In total, 247,633 posts were collected over the 2018–2023 period, resulting in more than 6 million tokens. During the application of the methodology, large clusters of posts on social networks were identified. Using analytics of reaction metrics and feedback from subscribers of university groups, recommendations were made for managing community content, and a convenient decision-making matrix was developed based on the bubble "Comments–Likes" diagram. These results can be used by decision makers to improve university brands and increase engagement in social networks.