DATA SCIENCE TECHNIQUES AND ALGORITHMS FOR LITERATURE REVIEWS AND META-ANALYSES
The objective of this CFP is to identify how new methodologies based on the development of technology help to carry out reviews of the literature and meta-analyses for communication especially focused on, although not limited to, online consumer behavior.
Technological development is favoring the review of existing methodologies. In this way, bibliometric analysis, literature reviews and meta-analyses methodologies can now be extended thanks to the use of algorithms for data extraction and analysis of large amounts of data. Algorithms can have varied functionalities; for example, Topic modeling is a semi-automatic algorithm that allows identifying clusters that are called topics. In this way, an area of analysis can be understood semi-automatically thanks to the use of algorithms.
Therefore, authors are invited to send articles that use:
- Algorithms for the extraction and analysis of bibliometric data.
- Big data of online consumer behavior or other areas of communication.
- Bibliometric analyses of online consumer behavior or other areas of communication.
- Meta-analyses supported by large amounts of data on online consumer behavior or other areas of communication.
- Meta-analyses that use algorithms for data analysis.
- Articles on good practices in the use of artificial intelligence and machine learning to carry out literature reviews.
- Development of new techniques and algorithms applicable to literature reviews.
- Case studies on literature reviews.
- Other multidisciplinary approaches that are applicable to literature reviews in the area of online consumer behavior or communication.
This CFP is linked, but not limited, to CBO2022 (Consumer Behavior Online) International Conference.
Open submission: September 1th, 2022
Close submission: December 15th, 2022
Acceptance on a rolling basis.