The DOI matches its Crossref record; 119 of the 119 references it cites were found in a registry or an index.
Social media analytics – Challenges in topic discovery, data collection, and data preparation
Open accessAbstract
Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data is being analysed in many different disciplines. The social media analytics process involves four distinct steps, data discovery, collection, preparation, and analysis. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on the stages of data discovery, collection, and preparation. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that the volume of data was most often cited as a challenge by researchers. In contrast, other categories have received less attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analyse social media data.
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Cited by
Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics
Saura, J. R. (2021) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2020.08.001 · Cited by 473
Sivarajah, U., Irani, Z., Gupta, S., & Mahroof, K. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.04.005 · Cited by 332
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Machine learning algorithms for social media analysis: A survey
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Metadata
- Authors
- Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C.
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 39
- Pages
- 156-168
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2780195716
- Cited by
- 803
- References
- 119
- FWCI
- 305.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
