Key research themes
1. What are the core challenges and methodologies in collecting and preparing social media data for effective analytics?
This research area addresses the preliminary yet critical stages of social media analytics: discovering relevant topics, collecting voluminous and heterogeneous data, and preparing such data for analysis. These foundational steps are essential because the quality and structure of data significantly affect any subsequent analysis and insights. The focus on challenges like data volume, diversity, noise, and data structuring reflects the complexity in harnessing social media data for intelligence.
2. How can social media intelligence methods support security and public safety through analysis of online behaviors and networks?
This research theme explores how social media data contributes to intelligence and security domains, including countering violent extremism, detecting coordinated influence campaigns, and national security monitoring. The methodologies mix computational social science, network analysis, and machine learning to detect behavioral patterns, sentiment, and coordination indicative of threats or influence operations. This area stresses the human-centric analytical perspective combined with technical capabilities to generate actionable intelligence from massive, dynamic online data.
3. In what ways does Artificial Intelligence transform the extraction and application of intelligence from social media for business, emotional, and educational contexts?
This research focuses on the role of AI in analyzing social media data to enhance business intelligence, understand emotional intelligence discourses, and support educational uses. AI techniques such as natural language processing, machine learning, and deep learning are applied to derive consumer insights, emotional states, user classification, and support learning processes. The theme highlights the transformative potential and challenges of AI-driven analytics in diverse social media intelligence applications.