Social Computing (TSC)


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Social Media



ACM Transactions on Social Computing (TSC) seeks to publish work that covers the full spectrum of social computing including theoretical, empirical, systems, and design research contributions. The editorial perspective is that social computing is fundamentally about computing systems and techniques in which users interact, directly or indirectly, with what they believe to be other users or other users’ contributions.  TSC welcomes research employing a wide range of methods to advance the tools, techniques, understanding, and practice of social computing, including:  theoretical, algorithmic, empirical, experimental, qualitative, quantitative, ethnographic, design, and engineering research.  Social computing will continue to be shaped by foundational algorithmic, econometric, psychological, sociological, and social science research and these broad based perspectives will continue to have a profound influence on how social computing systems are designed, built and how they grow.

TSC particularly solicits research that designs, implements or studies systems that mediate social interactions among users, or that develops or studies theory or techniques for application in those systems. Examples of such social computing systems include, but are not limited to: instant messaging, blogs, wikis, social networks, social tagging, social recommenders, collaborative editors and shared repositories.


To illustrate the scope, we provide examples of research covered within TSC:


  • Foundational algorithmic analyses that account for human and machine data and runtime complexity.
  • The influence of scale; how differing scales of human and machine participation changes the designs and adoptions of systems.


  • Motivations for contributing to and participating in social computing systems, both intrinsic and extrinsic.
  • Communications patterns in online communication forums.
  • Learning in social computing settings.
  • Ethnographic studies of social computing in situ.


  • Social impacts of social computing use, such as Internet addiction or spread of misinformation, leading to recommendations to mitigate harms and enhance benefits.
  • Ethical and policy issues in social computing.


  • Tools that help users understand the individual and collective roles of participants in social computing systems.
  • Algorithms for personalization within a social computing context, including recommender systems and social matchmaking systems.
  • Security and privacy mechanisms—both formal and interactive—related to social computing data and systems.
  • Social aspects of blockchain technologies
  • The roles of artificial agents (aka bots) in social computing spaces, including the design, creation and management of those agents relative to social interactions within a social computing system


  • System architectures and infrastructure for developing social computing platforms.
  • Micro-tasking systems based on techniques for decomposing complex activities into recomposable tasks that can be completed by mixtures of people and machines.
  • Crowdsourcing, collaborative content creation, productive social gaming or citizen science systems that include mechanisms to aggregate individual contributions for a collective goal.


  • Algorithms and approaches for extracting knowledge from social computing usage data and artifacts.
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