Data Analysis includes novel algorithmic and statistical techniques to reveal intelligence in social networks data. This includes: data models, recommender systems, data mining, machine learning, mining and analyzing social networks, time series; mining sequences, streams, text, graphs, spatio-temporal data.

Scalable analytics includes​​ highlighting theoretical foundations and algorithmic design for big data analytics. This includes: large-scale systems for text and graph analysis, parallel and distributed data mining and machine learning.

Data management​​ includes techniques for preprocess, store, retrieve, and query data . This includes: indexing and search algorithms, on-memory data management, storage management, query processing and optimization, network and graph data management, real-time databases, stream databases, cloud data Management, NoSQL
Visualization and User Interfaces​​: improve HCI experiences . This includes: Network data visualizations, visualizing statics and dynamic data

● Ethical and legal issues in social computing and social media
● Social modeling
● Social network analysis
● Sentiment Analysis
● Design and evaluation (scalable)-methodologies.
● Modeling and analyzing large-scale conversations and activities.
● User experience and behavior in social media
● Social issues in social computing and social media
● Social networks dynamics and applications
● Social network visualsation
● Voice-based social network analysis
● Artificial Intelligence (AI) in the analysis of data in social networks
● Cross-media analysis in social networks
● Privacy protection in social networks
● Risk analysis and data governance in social networks
● User profiling in social networks