The study on social networks has its origins in social, educational and business communities. Academic interest in this field has been growing since the mid 20th century.  The recent increase in the number of Web users stimulates the interaction among people, data dissemination and exchange of information, and also increases significantly the available data, provided by people, their interaction, logs and web servers.  With big data sets the analysis can be more accurate and brings  also the opportunity to evaluate and develop new techniques for social network analysis and data identification and mining.  This has raised the interest of a wide range of fields - such as academia, politics, security, business, marketing, science - on social network analysis.

The Brazilian Workshop on Social Network Analysis and Mining (BraSNAM) brings together researchers and professionals interested on social network analysis and related fields, and will promote collaborations as well as exchanges of ideas and experiences.  

More specialized topics within BraSNAM include, but are not limited to:

  • Big Social Data
  • Social networks visualization
  • Flow and diffusion of information
  • Community evolution
  • Extraction and treatment of social data
  • Mining techniques
  • Influence detection and propagation
  • Trust and expertise identification
  • Linking prediction and simulation
  • Social information applied to recommender systems
  • Social information and retrieval systems
  • Contextualized analysis of social and information networks
  • Modeling of user behavior
  • Mobile and smart multimedia sensors
  • Monitoring social networks
  • Privacy in social networks
  • Detection of spam, misinformation and malicious activities
  • Crowdsourcing and crowdfunding
  • Social Networks and Software Ecosystems
  • Social networks, health and well-being
  • Analysis of covert networks and Dark Web
  • Economics in networks
  • Location-aware social networks and mobility
  • Ethical issues in data analysis
  • Social network analysis applied to Cognitive Computing
  • Cases and application to real life situations: Medicine, Science, Education, Marketing, Team Formation, Decision Making, Management and others.