Version 1
: Received: 6 June 2021 / Approved: 7 June 2021 / Online: 7 June 2021 (16:16:18 CEST)
Version 2
: Received: 23 November 2021 / Approved: 23 November 2021 / Online: 23 November 2021 (14:45:31 CET)
Version 3
: Received: 9 February 2022 / Approved: 17 February 2022 / Online: 17 February 2022 (13:15:23 CET)
Shoeibi, N.; Shoeibi, N.; Chamoso, P.; Alizadehsani, Z.; Corchado, J.M. A Hybrid Model for the Measurement of the Similarity between Twitter Profiles. Sustainability2022, 14, 4909.
Shoeibi, N.; Shoeibi, N.; Chamoso, P.; Alizadehsani, Z.; Corchado, J.M. A Hybrid Model for the Measurement of the Similarity between Twitter Profiles. Sustainability 2022, 14, 4909.
Shoeibi, N.; Shoeibi, N.; Chamoso, P.; Alizadehsani, Z.; Corchado, J.M. A Hybrid Model for the Measurement of the Similarity between Twitter Profiles. Sustainability2022, 14, 4909.
Shoeibi, N.; Shoeibi, N.; Chamoso, P.; Alizadehsani, Z.; Corchado, J.M. A Hybrid Model for the Measurement of the Similarity between Twitter Profiles. Sustainability 2022, 14, 4909.
Abstract
Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.
Keywords
Twitter; Social Media; Social Networking; Social Network Analytic; DistilBERT; Text Similarity; Natural Language Processing; Character Computing
Subject
Computer Science and Mathematics, Information Systems
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.
