Social Media Analytics: Current Trends and Future Prospects

Abstract

Social media analytics (SMA) has recently emerged as a dominant research field due to the usage of social media platforms, globally. The SMA usually involves the primary steps of data acquisition, preprocessing and analysis. Due to voluminous, heterogeneous, imprecise and noisy data, challenges at each step are inevitable. The need for unparalleled storage, computing power and efficiency have rendered traditional data modeling methods inapplicable. There is substantial literature available on the challenges and issues related to particular analysis approaches. However, there hardly exists any study on the steps of social media analytics. To enlighten this gap, this paper presents the current trends and proposes a novel framework for SMA. This framework provides a roadmap to the five stages of SMA, i.e., data acquisition, preprocessing, data representation, analysis and presentation along with its challenges at each stage. The potential of the state-of-the-art machine learning techniques such as deep learning and transfer learning for addressing issues of SMA are described. Further, the article puts emphasis on the future prospects of SMA in terms of knowledge discovery, recommendation and trust in social media.Srivastava, Sonam Singh, Mahesh Kumar Singh, Yogendra Narain