Network extracted from User Talk pages of Venetian Wikipedia visualized with Gephi.
Wikipedia, the free online encyclopedia anyone can edit, is a live social experiment: millions of individuals volunteer their knowledge and time to collective create it. It is hence interesting trying to understand how they do it. While most of the attention concentrated on article pages, a less known share of activities happen on user talk pages, Wikipedia pages where a message can be left for the specific user. This public conversations can be studied from a Social Network Analysis perspective in order to highlight the structure of the “talk” network. In this paper we focus on this preliminary extraction step by proposing different algorithms. We then empirically validate the differences in the networks they generate on the Venetian Wikipedia with the real network of conversations extracted manually by coding every message left on all user talk pages. The comparisons show that both the algorithms and the manual process contain inaccuracies that are intrinsic in the freedom and unpredictability of Wikipedia growth. Nevertheless, a precise description of the involved issues allows to make informed decisions and to base empirical findings on reproducible evidence. Our goal is to lay the foundation for a solid computational sociology of wikis. For this reason we release the scripts encoding our algorithms as open source and also some datasets extracted out of Wikipedia conversations, in order to let other researchers replicate and improve our initial effort.
Just 2 social network pictures out of many in a PPT by Jim Moody (Duke Sociology, editor of Journal of Social Structure).
The first is a sociogram as invented by Jacob Moreno in 1930s: it depicts an attraction network in a Fourth Grade Class (Moreno was an incredible guy, for example he states that he was feeling he was God since the age of 5).
The second is a network of how vaseline (suggestions) spread. Vaseline Clinical Therapy has created its own word of mouth project, called Prescribe the Nation. The idea behind the project was to give Vaseline Clinical Therapy lotion to one Alaskan woman and to ask her to lather it on everyone she loved. In the end, 1,000 of her community’s 6,000 residents switched to Vaseline Clinical Therapy lotion (from vanksen blog).
Social networking of animals is fascinating. I would love to have more time to play with social networks of animals.
Today, thanks to an anonymous edit in the open wiki I created for collecting information about research on trust metrics, Trustlet.org, I re-found a paper I once added in the list of trust network datasets (subcategory “animals”): Matriarchs As Repositories of Social Knowledge in African Elephants.
I just re-skimmed through the abstract to re-find out that: despite widespread interest in the evolution of social intelligence, little is known about how wild animals acquire and store information about social companions or whether individuals possessing enhanced social knowledge derive biological fitness benefits. Using playback experiments on African elephants (Loxodonta africana), authors demonstrate that oldest elephants possess enhanced discriminatory abilities and this influences the social knowledge of the group as a whole. These superior abilities for social discrimination may result in higher per capita reproductive success for female groups led by older individuals. Our findings imply that the removal of older, more experienced individuals, which are often targets for hunters because of their large size, could have serious consequences for endangered populations of advanced social mammals such as elephants and whales.
The paper is cited by 166 other papers according to Google Scholar, by papers whose titles promise nothing but interesting readings: “Sperm whales: social evolution in the ocean” (cited by 128; according to Wikipedia, the name comes from the milky-white waxy substance, spermaceti, found in the animal’s head, due to its resemblance to semen, so nothing related to sex, I’m sorry), “Identifying the role that animals play in their social networks” (cited by 144), “The socioecology of elephants: analysis of the processes creating multitiered social structures”, “Quantifying the influence of sociality on population structure in bottlenose dolphins”, “Social relationships among adult female baboons (Papio cynocephalus) I. Variation in the strength of social bonds”, “Cognitive adaptations of social bonding in birds”, “Relatedness structure and kin-biased foraging in the greater horseshoe bat (Rhinolophus ferrumequinum)”.
Understanding social networks in Facebook or Wikipedia is so old school … I think I need to find funds for going to study social networks of animals, with a field study in Lesotho maybe! That would be extremely interesting! ;)
This great presentation tells you:
* how to use Netvizz, a Facebook application for exporting your Facebook social network or the network of a Facebook group in the form of a .gdf file
* and then how to import the .gdf file into gephi for analyzing and visualizing your network: you can select and parameter layout algorithms, change colors and sizes, etc.
During our weekly SoNet internal research meeting, my colleague Napo presented the paper “Predicting the Future With Social Media” by Sitaram Asur and Bernardo A. Huberman, archived on arXiv in March 2010. Using Twitter posts, they are able to forecast box-office revenues for movies, outperforming market-based predictors. They also do sentimental analysis on Twits by asking Mechanical Turk to tag few twits as positive, neutral, negative and then they train LingPipe to predict the positiveness of all the other millions of twits. Read it! Very interesting paper!
According to stats published by Facebook, Facebook has currently 400,000,000 active users. This would make it the third most populous country in the world, after China and India.
Do you bet it will overtake India’s population (1,166,900,000)? In how many months? (picture adapted from this image)
Attending the great conference Le reti socievoli (sociable nets) at Larica group of Univ Urbino.
Behind the speakers, the beamer shows live tag clouds of twitter posts (hashtag: #retisocievoli) by visibletweets (embedded below). Good example of audience live-participating to a conference!
I’ll make my first try to livetwitter a conf. Follow me at http://twitter.com/phauly.
By Debra Lauterbach; Hung Truong; Tanuj Shah; Lada A. Adamic Download as PDF
Abstract: Reputation mechanisms are essential for online transactions, where the parties have little prior experience with one another. This is especially true when transactions result in offline interactions. There are few situations requiring more trust than letting a stranger sleep in your home, or conversely, staying on someone else’s couch. Couchsurfing.com allows individuals to do just this. The global CouchSurfing network displays a high degree of reciprocal interaction and a large strongly connected component of individuals surfing the globe. This high degree of interaction and reciprocity among participants is enabled by a reputation system that allows individuals to vouch for one another. We find that the strength of a friendship tie is most predictive of whether an individual will vouch for another. However, vouches based on weak ties outnumber those between close friends. We discuss these and other factors that could inform a more robust reputation system.
Notes: Can an online social network build enough trust to allow strangers to sleep on each others’ couches?
The goal of this project is to integrate financial capital, human capital and social network analysis to formally and quantitively define and model social capital. Our interest is to find out what are the real dollar value of social networks.
Nine hundred and forty-eight dollars.
That’s the annual dollar value of each person in your email address book at work, according to a novel IBM study published in the Winter Information Systems Conference in February 2009.
IBM researchers, together with researchers in MIT, were looking to scientifically determine how valuable electronic social networks are, such as those in a group that primarily communicates electronically. Using mathematical formulas, honed by observing the email traffic and financial success of 2,600 anonymized far flung IBM consultants collaborating on thousands of projects during one year, researchers found that not all email relationships were equal.
In fact, they found that people with strong email ties with a manager, or had a more diverse circle of correspondents, enjoyed greater financial success than those who were more aloof. Teams with an even mix of genders also performed well financially. Individuals have more diverse networks and thus have more people who are reachable within 2 social steps (i.e., your friends’ friends’ friends.) is valuable. Too intensive communications to the same people have negative impact, perhaps because of the repetitive redundant information exchange.
Some researchers have suggested that human social networks may have a genetic basis. Using a sample of twins from the National Longitudinal Study of Adolescent Health, they found that in-degree (the number of times a person is named as a friend), transitivity (the probability that two friends are friends with one another), and betweenness centrality (the number of paths in the network that pass through a given person) are all significantly heritable. Existing models of network formation cannot account for this intrinsic node variation, so the researchers propose an alternative “Attract and Introduce” model that can explain heritability and many other features of human social networks.
 # ^ “Genes and the Friends You Make”. Wall Street Journal. January 27, 2009. http://online.wsj.com/article/SB123302040874118079.html.
 # ^ Fowler, J. H. (10 February 2009). “Model of Genetic Variation in Human Social Networks” (PDF). Proceedings of the National Academy of Sciences 106 (6): 1720–1724. doi:10.1073/pnas.0806746106. http://jhfowler.ucsd.edu/genes_and_social_networks.pdf