Monthly Archives: April 2010

Interaction hierarchy of bird flocks mapped

By Yunkyu Sohn

An experimental study shows that even birds have hierarchical social orders among them. By using delayed correlation analysis on birds’ flight directions, the authors uncover a weighted directed influence network of the bird flock. The topological structure of the network validates the presence of distinct asymmetric hierarchy within the bird community. This result implies that the long-held proximal interaction topology assumption in group dynamics simulation research should be replaced by the implementation of highly nondemocratic network structures.

Follow the leader from Science News on Vimeo.

Social Learning

by Robert Bond

There is an article in the latest edition of Science on social learning. The authors show that social learning is a key to success in their model. The article is interesting by itself, but there are many ways that the work could be extended through examinations of social learning from network, biological, and/or neurological points of view. Here is the abstract:

Social learning (learning through observation or interaction with other individuals) is widespread in nature and is central to the remarkable success of humanity, yet it remains unclear why copying is profitable and how to copy most effectively. To address these questions, we organized a computer tournament in which entrants submitted strategies specifying how to use social learning and its asocial alternative (for example, trial-and-error learning) to acquire adaptive behavior in a complex environment. Most current theory predicts the emergence of mixed strategies that rely on some combination of the two types of learning. In the tournament, however, strategies that relied heavily on social learning were found to be remarkably successful, even when asocial information was no more costly than social information. Social learning proved advantageous because individuals frequently demonstrated the highest-payoff behavior in their repertoire, inadvertently filtering information for copiers. The winning strategy (discountmachine) relied nearly exclusively on social learning and weighted information according to the time since acquisition.

Number of Followers: Measuring the potential of social influence?

Measuring User Influence in Twitter: The Million Follower Fallacy
Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and Krishna Gummadi
In Proc. of International AAAI Conference on Weblogs and Social Media (ICWSM), May 2010*featured in the ReadWriteWeb blog link and picked up by the New York Times link*

Abstract

Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user’s influence on others—a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three mea- sures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spon- taneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.

Evolutionary Psychology and Literature

by Robert Bond

The New York Times has an article on how Literature professors are looking into how evolutionary psychology may be related to how we understand and enjoy literature. Fascinating stuff!