Conference: Search and Social Media 2010Posted: February 16, 2010 | Author: hilary | Filed under: academics, blog, Presentations | Tags: algorithms, conference, research, search | 2 Comments »
I recently attended the Third Annual Workshop on Search and Social Media, an academic workshop with very strong industry participation. The workshop was packed, and had some of the most informative and interesting panel discussions I’ve seen (not counting the one I spoke on!).
Daniel Tunkelang did a great job of writing up the specific presentations on his site and on the ACM blog, so I won’t attempt to re-create the presentations line by line at this late date. Rather, I’d like to highlight a few open problems and research questions that came out of the discussions that I hope to see developed in the next year.
Social search consists of a set of problems including (but hardly limited to) search of social content like status updates, real-time search, generating, labeling, and finding user-generated content, ‘long-tail’ events and interests, finding vs re-finding, and trend identification.
What data is available to social search? There are many kinds of social data, from e-mail (private) to blogs (public) and tweets (mostly public) — what is and should be searchable? How do we handle issues of privacy and identity management?
How do we compute relevance, taking into account freshness, accuracy, and degrees of social separation?
Will the architecture of these search engines look like the search engines we’re currently familiar with?
How do we evaluate accuracy and truthiness of social data?
How do we characterize social connections, around concepts like strong vs weak ties, and friend-of-a-friend vs friend-of-a-friend’s-friend? Can we converge on a single social graph representation?
How do we best filter social data to lead to accurate recommendations for content discovery? How do we accommodate the fact that as we move beyond static factual data, two people using the same query may be looking for very different results?
Finally, how do we deal with the chasm between the industry participants (who have LOTS of data) and the academic participants, who suffer from a lack of public (and publishable) data?