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A Survey on Event Tracking in Social Media Data Streams

Big Data Mining and Analytics 2024年第1期7卷 217-243页

作者： Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang john panneerselvam Nick Antonopoulos School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu UniversityZhenjiang 212013China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Ocean College Jiangsu University of Science and TechnologyZhenjiang 212100China University Executive Office Edinburgh Napier UniversityEdinburghEH114BNUK

Social networks are inevitable parts of our daily life,where an unprecedented amount of complex data corresponding to a diverse range of applications are *** such,it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social *** tracking in social networks finds various applications,such as network security and societal governance,which involves analyzing data generated by user groups on social networks in real ***,as deep learning techniques continue to advance and make important breakthroughs in various fields,researchers are using this technology to progressively optimize the effectiveness of Event Detection(ED)and tracking *** this regard,this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social *** introduce mainstream event tracking methods,which involve three primary technical steps:ED,event propagation,and event ***,we introduce benchmark datasets and evaluation metrics for ED and tracking,which allow comparative analysis on the performance of mainstream ***,we present a comprehensive analysis of the main research findings and existing limitations in this field,as well as future research prospects and challenges.

关键词： Event Detection(ED) event propagation event evolution social networks

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ED-SWE:Event detection based on scoring and word embedding in online social networks for the internet of people

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Digital Communications and Networks 2021年第4期7卷 559-569页

作者： Xiang Sun Lu Liu Ayodeji Ayorinde john panneerselvam School of Informatics University of LeicesterUK School of Computer Science and Telecommunication Engineering Jiangsu UniversityJiangsuChina

Online social media networks are gaining attention worldwide,with an increasing number of people relying on them to connect,communicate and share their daily pertinent event-related *** detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of *** this paper,a novel Event Detection model based on Scoring and Word Embedding(ED-SWE)is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k *** proposed ED-SWE model can distill high-quality tweets,reduce the negative impact of the advent of spam,and identify latent events in the data streams ***,a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter *** order to further improve the performance of the Expectation-Maximization(EM)iteration algorithm,a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and ***,a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live *** conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.

关键词： Internet of people Hyperlink-induced topic search Event detection Online social networks

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EA-DFPSO:An intelligent energy-efficient scheduling algorithm for mobile edge networks

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Digital Communications and Networks 2022年第3期8卷 237-246页

作者： Yao Lu Lu Liu Jiayan Gu john panneerselvam Bo Yuan School of Informatics University of LeicesterUniversity RoadLeicesterLE17RHUnited Kingdom School of Computing and Engineering University of DerbyKedleston RoadDerbyDE221GBUnited Kingdom

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile *** edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission *** nodes are usually distributed in edge environments,enabling crucially efficient task scheduling among those nodes to achieve reduced processing ***,it is imperative to conserve edge server energy,enhancing their *** this end,this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization(EA-DFPSO)that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution *** proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for *** experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

关键词： Mobile edge computing Energy-aware systems Task scheduling Heuristic algorithms

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A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics

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Big Data Mining and Analytics 2022年第2期5卷 130-139页

作者： Jie Tong Leilei Shi Lu Liu john panneerselvam Zixuan Han the School of Computer Science and Telecommunication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu UniversityZhenjiang 212013China the School of Computing and Mathematical Sciences University of LeicesterLeicester LE17RHUK

Online social networks are increasingly connecting people around the *** maximization is a key area of research in online social networks,which identifies influential users during information *** of the existing influence maximization methods only consider the transmission of a single channel,but real-world networks mostly include multiple channels of information transmission with competitive *** problem of influence maximization in an environment involves selecting the seed node set for certain competitive information,so that it can avoid the influence of other information,and ultimately affect the largest set of nodes in the *** this paper,the influence calculation of nodes is achieved according to the local community discovery algorithm,which is based on community dispersion and the characteristics of dynamic community ***,considering two various competitive information dissemination cases as an example,a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known,and a novel influence maximization algorithm of node avoidance based on user interest is *** conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.

关键词： influence maximization competitive environment dynamic network

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