Social media sites (by some referred to as the web 2.0) allow their users to interact with each other, for example in collecting and sharing so-called user-generated content - these can be just bookmarks, but also blogs, images, and videos. Social media support co-creation: processes where customers (or users, if you prefer) do not just consume but play an active role in defining and shaping the end product. Famous examples include Six Degrees, LiveJournal, Digg, Epinions, Myspace, Flickr, YouTube, Linked-in, and Pinterest. Of course, today's internet giants Facebook and Twitter are key new developments. Finally, Wikipedia should not be overlooked - a major resource in many language technologies including information retrieval!
The second part of the lecture looks into the opportunities for information retrieval research. Social media platforms tend to provide access to user profiles, connections between users, the content these users publish or share, and how they react to each other's content through commenting and rating. Also, the large majority of social media platforms allow their users to categorize content by means of tags (or, in direct communication, through hash-tags), resulting in collaborative ways of information organization known as folksonomies. However, these social media also form a challenge for information retrieval research: the many platforms vary in functionalities, and we have only very little understanding of clearly desirable features like combining tag usage and ratings in content recommendation! A unifying approach based on random walks will be discussed to illustrate how we can answer some of these questions , but clearly the area has ample opportunity to leave your own marks.
In the final part of the lecture I will briefly touch upon an even wider range of opportunities, where data derived from social media form a key component to enable new research and insights. I will review a few important results from research centered on Wikipedia, facebook and twitter data, as well as a diverse range of new information sources including the geo- and temporal information derived from images and tweets, product reviews and comments on youtube videos, and how url shorteners may give a view on what is popular on the web.
 Maarten Clements, Arjen P. De Vries, and Marcel J. T. Reinders. 2010. The task-dependent effect of tags and ratings on social media access. ACM Trans. Inf. Syst. 28, 4, Article 21 (November 2010), 42 pages. http://doi.acm.org/10.1145/1852102.1852107