Data Analytics Applications for Streaming Data From Social Media: What to Predict?

Area of Science:

• Computer Science
• Information Science
• Computational Social Science

Background:

• Social media platforms offer vast, dynamic datasets for research.
• Analyzing this data, particularly text-based content, presents unique opportunities and challenges.
• Microblogging services are a significant source of real-time, user-generated information.

Purpose of the Study:

• To review and categorize research questions addressable through the analysis of microblogging data.
• To focus on text-based analytical approaches due to their prevalence in scientific literature.
• To provide a taxonomy of problems studied using streaming data from scientific literature.

Main Methods:

• Literature review focusing on studies analyzing microblogging data.
• Categorization of research questions based on text-based analytical approaches.
• Development of a taxonomy for studied problems in this domain.

Main Results:

• Identification of diverse research questions addressable via microblogging data analysis.
• Emphasis on the dominance of text-based methods in current research.
• A structured taxonomy classifying the types of problems investigated.

Conclusions:

• Text-based analysis of microblogging data is a rich area for scientific inquiry.
• The proposed taxonomy aids in understanding the landscape of research in this field.
• Further research can leverage these insights to explore new questions and methodologies.