Defining the big social data paradigm through a systematic literature review approach

被引:5
作者
Solazzo, Gianluca [1 ]
Elia, Gianluca [1 ]
Passiante, Giuseppina [1 ]
机构
[1] Univ Salento, Dept Engn Innovat, Lecce, Italy
关键词
Research agenda; Systematic literature review; Big data; Big social data; DATA ANALYTICS; PUBLIC-HEALTH; MEDIA DATA; KNOWLEDGE MANAGEMENT; OPEN INNOVATION; FACEBOOK PAGES; SET ANALYSIS; FUZZY-LOGIC; FRAMEWORK; IMPACT;
D O I
10.1108/JKM-10-2020-0801
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Purpose This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources. Design/methodology/approach The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques. Findings The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective. Research limitations/implications The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field. Practical implications Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories. Originality/value The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.
引用
收藏
页码:1853 / 1887
页数:35
相关论文
共 312 条
  • [1] CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor
    Abu-Salih, Bilal
    Wongthongtham, Pornpit
    Chan, Kit Yan
    Zhu, Dengya
    [J]. JOURNAL OF INFORMATION SCIENCE, 2019, 45 (02) : 259 - 280
  • [2] Big data applications in operations/supply-chain management: A literature review
    Addo-Tenkorang, Richard
    Helo, Petri T.
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 101 : 528 - 543
  • [3] Korea's social dynamics towards power supply and air pollution caused by electric vehicle diffusion
    Ahn, Sang-Jin
    Kim, Leo
    Kwon, Okyu
    [J]. JOURNAL OF CLEANER PRODUCTION, 2018, 205 : 1042 - 1068
  • [4] Crowdsourced Mobile Sensing for Smarter City Life
    Aihara, Kenro
    Imura, Hajime
    Takasu, Atsuhiro
    Tanaka, Yuzuru
    Adachi, Jun
    [J]. 2014 IEEE 7TH INTERNATIONAL CONFERENCE ON SERVICE-ORIENTED COMPUTING AND APPLICATIONS (SOCA), 2014, : 334 - 337
  • [5] On Feasibility of Crowdsourced Mobile Sensing for Smarter City Life
    Aihara, Kenro
    Bin, Piao
    Imura, Hajime
    Takasu, Atsuhiro
    Tanaka, Yuzuru
    [J]. DISTRIBUTED, AMBIENT AND PERVASIVE INTERACTIONS, (DAPI 2016), 2016, 9749 : 395 - 404
  • [6] How does Social Media Analytics Create Value?
    Akter, Shahriar
    Bhattacharyya, Mithu
    Wamba, Samuel Fosso
    Aditya, Sutapa
    [J]. JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING, 2016, 28 (03) : 1 - 9
  • [7] User profiling for big social media data using standing ovation model
    Al-Qurishi, Muhammad
    Alhuzami, Saad
    AlRubaian, Majed
    Hossain, M. Shamim
    Alamri, Atif
    Rahman, Md. Abdur
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (09) : 11179 - 11201
  • [8] Critical Success Factors for Big Data: A Systematic Literature Review
    Al-Sai, Zaher Ali
    Abdullah, Rosni
    Husin, Mohd Heikal
    [J]. IEEE ACCESS, 2020, 8 : 118940 - 118956
  • [9] AlGaradi M.A, 2018, 2018 INT C COMPUTING, P1, DOI DOI 10.1109/ICOMET.2018.8346351
  • [10] Big Social Data as a Service: A Service Composition Framework for Social Information Service Analysis
    Ali, Kashif
    Hamilton, Margaret
    Thevathayan, Charles
    Zhang, Xiuzhen
    [J]. WEB SERVICES - ICWS 2018, 2018, 10966 : 487 - 503