Data Analytics and Data Mining for Social Media Minitrack

Social media is changing how we work and play. It is also changing way we access and consume media, stay in touch with family and friends, as well as how we communicate within our on-line communities. One of the things these activities share in common is that they generate a tremendous volume of data that can be analyzed and mined for both research and commercial purposes. This minitrack focuses on research that brings together social media (or social networks) and data analytics & data mining. We welcome quantitative, theoretical or applied papers whose approaches are within the scope of data analytics and data mining, and closely related areas (e.g., data warehousing, content mining, network analysis, structure mining, business intelligence and knowledge discovery).

Topics of interest include (but are not limited to):

  • Discovery, collection and extraction of Social Media data
  • Text- or image-based mining of Social Media content
  • Opinion mining, sentiment analysis and recommendation analysis
  • Cleaning, curation and provenance of data in social networks
  • Social Network Analysis; exploration of massive social networks
  • Identifying and profiling influential participants, subgroups and communities
  • Crowd or cloud computation on Social Media data
  • Predictive and forecasting analytics based on Social Media content
  • Trend analysis to identify emerging terms, topics and ideas
  • Visual analysis of web network structure, usage and content
  • Semantic representations of on-line content, link analysis and linkages
  • Social search, retrieval and ranking
  • Analysis of web-based collective intelligence
  • Performance and scalability of Social Media data management
  • Social innovation and effecting change through Social Media

Minitrack Co-Chairs:

David Yates (Primary Contact)
Bentley University

Jennifer Xu
Bentley University

Dominique Haughton
Bentley University

Xiangbin Yan
Harbin Institute of Technology, China

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