Researcher Jun Zhang and colleagues designed a social game to engage people in contributing to expertise profiling for themselves and their colleagues. In „Expertise­Tagging Game: Identifying Expertise Networks in organizations“ (2007) they describe how it works and a pilot study. Playing the game a user gets certain points when his or her input matches with other users’ input.

Thinking about our newly launched expertise finder system UCSF Profiles, a ‘game’ approach might be an interesting idea to explore. UCSF Profiles builds on a good set of data retrieved from Pubmed and already allows passive networks based on that information. However, active networking mechanisms will be helpful to retrieve ‘local knowledge’ that goes beyond scientific publications.

Here is how the game works: “A person’s expertise tags are presented in a masked tag cloud when the game starts. A user starts playing this game by typing a keyword in the text field following ‘John is a good person to talk to about (X) ‘ once a time. If a tag he sent matches a tag input by that individual or by other users who have played the game, the matched tag is revealed and the user earns some points based on how many other people have also tagged the same keyword.  The goal is to reveal all the masked words in the tag cloud. Top players are named as ‘top connectors’ in the landing page of the game site. Top players for a specific game are also listed in that game’s page as ‘who knows person (X) best’. A network visual-exploration interface  helps people discover the expertise networks around them.” The pilot study showed “that a lot of users were motivated by different fun factors, such as enjoyment of problem solving (revealing the tag cloud) and competition (being a top connector).”

By the way, some senior users expressed concerns of deploying a game into a corporate environment, so the authors  repositioned the ‘game’ as a “system for people to recognize their colleagues by recommending their expertise using tagging as well a means of self-expression by self-tagging and approving peers’ tags”.

And while we are at it, here is a second one: “The Dogear Game” (2007).  Individual players receive entertainment and learn about their colleagues’ bookmarks. The player’s colleagues, on the other hand, receive recommendations of websites and documents of potential interest to them. The numbers are impressive: The game was implemented as a plug-in to a corporate instant messaging client used by over 100,000 employees. Read on.

Researchers may disagree on the best social media or “crowdsourcing” approach. However, existing online communities that explore new social participation ideas provide a “vast laboratory” for testing theories about technology-mediated social interaction. Wikipedia, Twitter, Patients Like Me, Open Innovation Pavillion, Watch Jefferson County and Innocentive – today a great amount of data is becoming available, as stated in the recent article “Computational Social Science”. Now is the time to use these data and perform experiments at massive scale, wrote a team of researchers led by Ben Shneiderman, professor for computer science at the University of Maryland. They authored a white paper called “National Initiative for Social Participation” which suggests a “scientific foundation for thinking about technology-mediated social interaction”.

A core assumption is that there are “better and worse forms of social participation and social connectedness”. But what makes them better or worse? We have been thinking about this for a while, as we work towards the launch of UCSF’s research networking site mentioned in the recent post OpenSocial & Best Practices for Social Networking Websites.  Ben Shneiderman’s team argues that an organized research program could help to accelerate promising social action networks and answer fundamental questions about functionality, usability and sociability. For example: What motivates participants to come to a site, read, contribute and collaborate? What are best practices for moderation facilities? How do successful online communities handle the start-up paradox, recruit and socialize newcomers, encourage commitment and contribution from members? And how important are cooperation, competition, and trust for an individual’s participation? Not to mention legitimate dangers;  the white paper states to the point: “In the social networking ideal universe, anyone can connect with anyone else. In the ideal secure universe, there is insurance against that.”

Finding answers to these questions would offer the chance to “redesign and repurpose” social media technologies, improving “usability and sociability to better engage people with diverse motivations, experiences, perspectives, skills and knowledge”. Ben Shneiderman’s team envisions using the knowledge to “produce profound transformations in healthcare, community safety, disaster response, life-long learning, business innovation, energy sustainability, environmental protection, and other important national priorities.”

In this spirit, the National Science Foundation announced a $15-million Social-Computational Systems grant program to support research around social networking – a good opportunity to establish a stronger basis for evidence-driven discussions about online participation.