Time-Series Clustering for Determining Behavioral-Based Brand Loyalty of Users Across Social Media

• Published in: IEEE transactions on computational social systems Vol. 10; no. 4; pp. 1 - 15
• Main Authors: Kafeza, Eleanna, Rompolas, Gerasimos, Kyriazidis, Sokratis, Makris, Christos
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Behavioral computing; Behavioral sciences; Brand loyalty; Business; Clustering; Clustering algorithms; cross-media study; Customers; Data collection; Data mining; Digital media; Graphical representations; Measurement; Media; shapelet clustering; social media analytics; Social networking (online); Social networks; Time series; time-series analysis; User behavior
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2022.3219781

In recent years social media data analytics allow enterprises to adopt a data driven approach to manage their processes. Although social media provide a plethora of data, still much work needs to be done to transform these data into services that businesses can use and make insightful decisions. In this work, we address one of the most important problems in business, the relationship with the customers and more precisely the identification of loyal customers. We use behavioral analytics to model and process customer actions. We extract user behavior based on our unified crawling approach and collect data from three different social media namely Reddit, Twitter and YouTube. We are extracting for each user three different behaviors namely communication, sentiment and product mix and convert them into a 3-D time-series. We use shapelet clustering to determine the loyal users. To verify our approach, we develop a set of metrics based on trust, commitment and engagement and we show that our approach results in differentiating the loyal users successfully. Moreover, we validate our results presenting a word cloud visualization. We extend our methodology introducing a semantic data transformation algorithm where we use the topic extraction, reducing the time-series to a more relevant one. Our experiments show that based on the verification metrics, our transformation increases the accuracy of the clustering results.