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    Content Assessment: The Power of V? Social Big Data: Techniques and Recent Applications

    Information - 90%
    Insight - 91%
    Relevance - 88%
    Objectivity - 89%
    Authority - 88%



    A short percentage-based assessment of the qualitative benefit of the post highlighting recent research on social big data and the six V model.

    Editor’s Note: From time to time, ComplexDiscovery highlights publicly available or privately purchasable announcements, content updates, and research from cyber, data, and legal discovery providers, research organizations, and ComplexDiscovery community members. While ComplexDiscovery regularly highlights this information, it does not assume any responsibility for content assertions.

    To submit recommendations for consideration and inclusion in ComplexDiscovery’s cyber, data, and legal discovery-centric service, product, or research announcements, contact us today.

    Industry Paper by Tina Tian

    Social Big Data: Techniques and Recent Applications

    Shared with permission under the Creative Commons Attribution-NonCommercial 4.0 International License, the following paper by author Tina Tian shares on considerations and challenges associated with managing social big data. This paper may be useful for cybersecurity, information governance, and legal discovery professionals as they seek to evaluate and understand large volumes of social media-generated electronically stored information in areas ranging from traditional audits, investigations, and litigation to the emerging requirement to evaluate large data sets for characteristics of disinformation campaigns.


    In the big data era, large volumes of social media data are generated at a high velocity, which we refer to as social big data. It is beyond the ability of traditional methods and algorithms to manage the massive amount of data in a tolerable elapsed time. In this paper, we present a comprehensive overview of the established big data techniques and new achievements on social big data management. The study also highlights a list of state-of-the-art applications based on data gathered from social networking platforms. At the end, we identify the key issues and challenges related to social big data analytics.

    Introduction Extract

    With the rapid development of computing and networking techniques, social media have experienced fast growth. Massive amounts of data have been generated in real-time. Social network sites, such as Twitter and Facebook, and other micro-blogging services, have provided a new kind of platform for information sharing. Web-users all over the world can directly engage in these networks and share their opinions and perspectives. The content data are presented in the forms of texts, links, images, videos, etc., ranging from daily life stories to the latest local and global news and events. The rich and continuously generated data provide tremendous value for users and organizations. The information gathered in the online communities and shared by their users constructs an important source of big data and provides a valuable contribution to decision making.

    Social big data is a collection of huge data sets with great diversity extracted from social networks. To better define big data and characterize its technological aspects, [D. Laney] proposes the three V model. The three Vs are Volume, Velocity, and Variety. Additionally, another three Vs have been added to extend the set of variables. They are Veracity, Variability, and Value. The latter three Vs have a stronger focus on addressing the big data challenges and managerial perspectives. The 6V model is further explained as follows.

    • Volume refers to the large amount of data that consume excessive storage.
    • Velocity represents the speed of data generation and the frequency of data delivery. Big data analysis requires a fast rate to keep up with the speed of data production.
    • Variety addresses the importance that big data are generated from a great variety of sources, which may contain both structured and unstructured data.
    • Veracity characterizes the quality of data (for example, uncertain and imprecise data) and the level of trust in different data sources.
    • Variability stresses the unpredictability of big data.
    • Value describes the transformation of information into insights that may create economic value for companies and organizations.

    As an emerging paradigm, big data refers to any set of data with enormous capacity that traditional methods would require a large amount of time to process. The sheer size of the dataset makes it practically impossible for typical database software tools to capture, store, manage and analyze. Therefore, the management of big data requires advanced and technology-based analytical approaches and sophisticated algorithms and techniques. In this paper, we provide an overview of the state-of-the-art methods and techniques to store and process social big data. A survey of recent achievements on social big data applications is being conducted, with a clear focus on applications published in the past few years. The paper also highlights the current challenges and security issues in social big data.

    Complete Paper: Social Big Data – Techniques and Recent Applications (PDF)

    IJCSS-1591 - Social Big Data- Techniques and Recent Applications

    Read the original paper.

    Reference: Tian, T., 2020. Social Big Data: Techniques and Recent Applications. [online] International Journal of Computer Science and Security (IJCSS), pp. 224-234. Available at:

    Additional Reading

    Source: ComplexDiscovery


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