Social media big data analytics: A survey
By Norjihan Binti Abdul Ghani, Suraya Hamid, Ibrahim Abaker Targio Hashem, Ejaz Ahmed. Computers in Human Behavior, 2019
359 citations recorded by the source corpus.
Abstract Big data analytics has recently emerged as an important research area due to the popularity of the Internet and the advent of the Web 2.0 technologies. Moreover, the proliferation and adoption of social media applications have provided extensive opportunities and challenges for researchers and practitioners. The massive amount of data generated by users using social media platforms is the result of the integration of their background details and daily activities. This enormous volume of generated data known as “big data” has been intensively researched recently. A review of the recent works is presented to obtain a broad perspective of the social media big data analytics research topic. We classify the literature based on important aspects. This study also compares possible big data analytics techniques and their quality attributes. Moreover, we provide a discussion on the applications of social media big data analytics by highlighting the state-of-the-art techniques, methods, and the quality attributes of various studies. Open research challenges in big data analytics are described as well.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/8cc8f762a52936f0d6baddb4f1bd5c06f1062605
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Citations in the corpus
- Artificial intelligence in the creative industries: a review (2020, methodology): “l data in order to predict preferences [248]. Contextualizing social-media conversations can also help advertisers understand how consumers feel about products and to detect fraudulent ad impressions [249]. This can be achieved using natural language processing (NLP) methods [65]. Recently, an AI-ba”