HealthRally, a new San Francisco startup, allows you to reach personal wellness goals by getting friends and family to pledge cash to support you.

via Gigaom

Lars Backstrom, Eytan Bakshy, Jon Kleinberg, Thomas M. Lento, Itamar Rosenn. ICWSM 2011

Abstract:

An individual’s personal network — their set of social contacts — is a basic object of study in sociology. Studies of personal networks have focused on their size (the number of contacts) and their composition (in terms of categories such as kin and co-workers). Here we propose a new measure for the analysis of personal networks, based on the way in which an individual divides his or her attention across contacts. This allows us to contrast people who focus a large fraction of their interactions on a small set of close friends with people who disperse their attention more widely.

Using data from Facebook, we find that this balance of attention is a relatively stable property of an individual over time, and that it displays interesting variation across both different groups of people and different modes of interaction. In particular, activities based on communication involve a much higher focus of attention than activities based simply on observation, and these two types of modalities also exhibit different forms of variation in interaction patterns both within and across groups. Finally, we contrast the amount of attention paid by individuals to their most frequent contacts with the rate of change in the identities of these contacts, providing a measure of churn for this set.

via Michigan Interactive & Social Computing

libSNA is an open-source library for Social Network Analysis, licensed under the LGPL. This library is under active development by Abe Usher in hopes that it will serve as a catalyst for improving the field of Social Network Analysis. Features Easy to use Python API Flexible data import options Scalable Built-in reports Built-in data export capabilities Open source - easily extended Fast processing time (efficient use of graph algorithms)

(via Pew Research Center’s Internet & American Life Project) 

Patients and caregivers know things — about themselves, about each other, about treatments — and they want to share what they know to help other people. Technology helps to surface and organize that knowledge to make it useful for as many people as possible.

Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie, and Wei-Ying Ma. 2011. Recommending friends and locations based on individual location history. ACM Trans. Web 5, 1, Article 5 (February 2011), 44 pages. DOI=10.1145/1921591.1921596 http://doi.acm.org/10.1145/1921591.1921596

Abstract
The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply, to some extent, users’ interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction and report on a personalized friend and location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system, a particular individual’s visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories and recommend to each user a group of potential friends in a GIS community. Third, we estimate an individual’s interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual’s location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people’s outdoor movements, 2) the visited popularity of a geospatial region, and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, namely similarity-by-count, cosine similarity, and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.

1. using facebook
2. friends who watch tv
3. well-educated parents and grandparents
4. cheerleading
5. homophobia
6. joining sorority
7. playing sports
8. friends with ED
9. genetics