Social Media Data Analysis Trends and Methods

被引:0
作者
Rokaya, Mahmoud [1 ,2 ]
Al Azwari, Sanaa [1 ]
机构
[1] Taif Univ, Coll Comp & Informat Technol, Dept Informat Technol, POB 11099, Taif 21944, Saudi Arabia
[2] Tanta Univ, Fac Sci, Tanta, Egypt
来源
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2022年 / 22卷 / 09期
关键词
Social Media; Ensemble Learning; Security Risks; Identity Theft; Fraud; Malware; Adware; Bot; Phishing; Fake; DDoS; MULTIPLE CLASSIFIERS; ENSEMBLE; CLASSIFICATION; COMBINATION; MIXTURES;
D O I
10.22937/IJCSNS.2022.22.9.48
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Social media is a window for everyone, individuals, communities, and companies to spread ideas and promote trends and products. With these opportunities, challenges and problems related to security, privacy and rights arose. Also, the data accumulated from social media has become a fertile source for many analytics, inference, and experimentation with new technologies in the field of data science. In this chapter, emphasis will be given to methods of trend analysis, especially ensemble learning methods. Ensemble learning methods embrace the concept of cooperation between different learning methods rather than competition between them. Therefore, in this chapter, we will discuss the most important trends in ensemble learning and their applications in analysing social media data and anticipating the most important future trends.
引用
收藏
页码:358 / 368
页数:11
相关论文
共 73 条
[1]   Spam Email Detection Using Deep Learning Techniques [J].
AbdulNabi, Isra'a ;
Yaseen, Qussai .
12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2021, 184 :853-858
[2]   Fake News Detection Using Machine Learning Ensemble Methods [J].
Ahmad, Iftikhar ;
Yousaf, Muhammad ;
Yousaf, Suhail ;
Ahmad, Muhammad Ovais .
COMPLEXITY, 2020, 2020
[3]  
[Anonymous], 2015, The Marketing Review, DOI [DOI 10.1362/146934715X14441363377999, 10.1362/146934715X14441363377999]
[4]  
Badawy A, 2018, 2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), P258
[5]  
Banfield R. E., 2005, Information Fusion, V6, P49, DOI 10.1016/j.inffus.2004.04.005
[6]  
Barbier G, 2011, SOCIAL NETWORK DATA ANALYTICS, P327
[7]   CONSENSUS THEORETIC CLASSIFICATION METHODS [J].
BENEDIKTSSON, JA ;
SWAIN, PH .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1992, 22 (04) :688-704
[8]  
Bhandari1 Ravneet Singh, 2022, FIIB BUS REV, P1, DOI 10.1177%2F23197145221078106
[9]  
Bijalwan A, 2016, Perspect. Sci., V8, P502, DOI [10.1016/j.pisc.2016.05.008, DOI 10.1016/J.PISC.2016.05.008]
[10]  
Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1023/A:1018054314350