Temporal pattern mining from user-generated content
作者机构：School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei230026AnhuiChina Department of Computing and MathematicsManchester Metropolitan UniversityManchesterM156BHUnited Kingdom Department of Computer Science and MathematicsLebanese American UniversityBeirutLebanon Woxsen School of BusinessWoxsen UniversityIndia
出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)
年 卷 期：2022年第8卷第6期
页 面：1027-1039页
核心收录：
学科分类：0810[工学-信息与通信工程] 08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(grant no.61573328)
主 题：Social media analysis Collaborative computing Social data Twitter data Temporal patterns mining Dynamic graphs
摘 要：Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content s metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content s metadata contains valuable information, which helps to understand the users collective behavior and can be beneficial for business and research. Dataset and codes are publicly available;the link is given in the dataset section.
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