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Social media platforms have been an undeniable part of our lifestyle for the past decade. Analyzing the information that is being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and to increase user satisfaction. To draw any further conclusions, first, it is necessary to know how to compare users. In this paper, a hybrid model is proposed to measure the degree of similarity between Twitter profiles by calculating features related to the users’ behavioral habits. For this, first, the timeline of each profile was extracted using the official TwitterAPI. Then, three aspects of a profile were deliberated in parallel. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping was utilized to compare the behavioral ratios of two profiles. Next, the audience network was extracted for each user, and to estimate the similarity of two sets, the Jaccard similarity was used. Finally, for the content similarity measurement, the tweets were preprocessed using the feature extraction method; TF-IDF and DistilBERT were employed for feature extraction and then compared using the cosine similarity method. The results showed that TF-IDF had slightly better performance; it was therefore selected for use in the model. When measuring the similarity level of different profiles, a Random Forest classification model was used, which was trained on 19,900 users, revealing a 0.97 accuracy in detecting similar profiles from different ones. As a step further, this convoluted similarity measurement can find users with very short distances, which are indicative of duplicate users.

Social media platforms are entirely an undeniable part of the lifestyle from the past decade. Analyzing the information being shared is a crucial step to understand humans behavior. Social media analysis is aiming to guarantee a better experience for the user and risen user satisfaction. But first, it is necessary to know how and from which aspects to compare users with each other. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official Twitter API. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for comparison of the behavioral ratios of two profiles. Next, Graph Network Analysis is used for monitoring the interactions of the user and its audience; for estimating the similarity of graphs, Jaccard similarity is used. Finally, for the Content similarity measurement, natural language processing techniques for preprocessing and TF-IDF for feature extraction are employed and then compared using the cosine similarity method. Results have presented the similarity level of different profiles. As the case study, people with the same interest show higher similarity. This way of comparison is helpful in many other areas. Also, it enables to find duplicate profiles; those are profiles with almost the same behavior and content.