Category Archives: social networks

What Does a Political Networks Conference Do? (On Twitter)

Last week I mapped the Twitter follow network of those attending the Political Networks Conference in Boulder, CO.  I gathered this data prior to the first day of the conference, so I decided to take a second look now that the conference is over.

Political Networks Twitter Follow Network – Post Conference (Click image for larger version.)

How does the post-conference graph differ from the pre-conference graph?

  • The first notable difference is the addition of 18 new nodes.  The size of the network grew by over 50% as new Twitter users decided to follow @PolNetworks during the conference.
  • Another difference is the addition of 215 new follow edges.  With only 181 edges in the original graph, the number of follow relationships in the network grew more than 100%.
  • The account with the biggest increase in followers was @krmckelv.  Karissa went from only 1 follower to 8.  (She has since changed her Twitter handle to @karissamckelvey.)

Now let’s limit the post-conference graph to a subgraph containing only those nodes that were also in the pre-conference graph.  How has this component changed?

Graph Density: Increased 0.03 from 0.15 to 0.18.

Graph Transitivity:  Decreased 0.03 from 0.56 to 0.53.

Graph Efficiency:  Decreased 0.02 from 0.87 to 0.85.

So new follow edges were added (density), but in a way that more incomplete than complete triangles were formed (transitivity).  Not surprisingly, the new ties were redundant in connecting the graph, lowering efficiency.

Here are some individual-level statistics for the interested.  It was great to get to know many of you at the conference, and I’ll see you at the next one!

Account In Degree Out Degree Eig. Centrality
JaciKettler 10 20 0.3
smotus 35 18 0.27
kwcollins 20 19 0.27
burtmonroe 15 17 0.26
therriaultphd 20 18 0.24
BrendanNyhan 31 17 0.24
JohnCluverius 8 13 0.22
RebeccaHannagan 4 12 0.21
ianpcook 7 12 0.2
Student 27 14 0.2
jlove1982 7 11 0.2
KyleLSaunders 9 13 0.19
krmckelv 12 17 0.19
JeffGulati 9 12 0.18
richardmskinner 8 10 0.15
James_H_Fowler 8 11 0.15
prisonrodeo 16 9 0.15
archimedino 5 10 0.14
davekarpf 7 8 0.13
NateMJensen 5 8 0.13
jon_m_rob 0 8 0.13
sissenberg 8 7 0.12
3876 7 8 0.12
hsquared47 3 7 0.12
marioguerrero 1 7 0.12
FHQ 17 7 0.11
heathbrown 4 8 0.11
First_Street 1 7 0.1
98percentright 5 6 0.1
davidlazer 17 5 0.07
GeoffLorenz 0 5 0.07
jasonjones_jjj 4 6 0.06
matthewhitt 2 4 0.06
DocPolitics 1 4 0.06
JoeLenski 2 4 0.05
maizeandblue 1 4 0.05
allenlinton2 2 3 0.04
DryHeathen 0 4 0.04
ajungherr 0 4 0.04
slimbock 0 2 0.03
cassyld 2 2 0.02
PolNetworks 52 1 0.02
rmbond15 2 3 0.02
DominikBatorski 0 2 0.01
vatrafilm 0 1 0
janschulz 0 1 0
dogaker 0 1 0
stefanjwojcik 1 1 0
KenneyMkenney 0 1 0
ophastings 0 1 0
jbrittaq 0 1 0
jboxstef 1 1 0
Hirschi5 0 1 0

Download edge list as .xlsx: polnetworks2_edge_list

Acquisition of Social Network Structure

by Jason J. Jones
Scale Free Graph

On Tuesday at the HNG meeting I’ll be discussing three experiments I’ve conducted on the acquisition of social network structure. For a preview, you can read my submission to CogSci 2011 which discusses one of the experiments.

New Papers in the Physics and Society section at arxiv.org

By Chris Fariss

Evolution of Coordination in Social Networks: A Numerical Study

Coordination games are important to explain efficient and desirable social behavior. Here we study these games by extensive numerical simulation on networked social structures using an evolutionary approach. We show that local network effects may promote selection of efficient equilibria in both pure and general coordination games and may explain social polarization. These results are put into perspective with respect to known theoretical results. The main insight we obtain is that clustering, and especially community structure in social networks has a positive role in promoting socially efficient outcomes.

More below:

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Truthy tweets (in real time)

by Robert Bond

Some researchers at the University of Indiana have created a website that lets users track politics-related tweets in real time. The site includes trends over time, network visualizations, among other things. This is a great example of the kinds of things one could do research on using the (freely available) data from Twitter’s API. While I don’t have the programming skills to do anything useful with the API, I can get some data out of the API using the R package for Twitter.

Gladwell on social media

by Robert Bond

Malcolm Gladwell has a new piece in The New Yorker on social media and activism. I have linked to it here.

In the article Gladwell makes a lot of assertions about how people use social media and what it is/isn’t useful for doing. Most of these assertions are not based on research; rather, they seem to be based on what he assumes about how Facebook and Twitter are used. This is a great article to get some hypotheses about how people actually use these types of media!

Congressional Speech Corpus (including references to other members of Congress)

By Jason J. Jones

I ran across this corpus of Congressional speech that may be useful to some in the group.  Here is a brief description:

This data includes speeches as individual documents, together with:

  • automatically-derived labels for whether the speaker supported or opposed the legislation discussed in the debate the speech appears in, allowing for experiments with this kind of sentiment analysis
  • indications of which “debate” each speech comes from, allowing for consideration of conversational structure
  • indications of by-name references between speakers, and the scores that our agreement/disagreement classifier(s) automatically assigned to such references, allowing for experiments on agreement classification if one assigns “true” labels from the support/oppose labels assigned to the pair of speakers in question
  • the edge weights and other information we derived to create the graphs we used for our experiments upon this data, facilitating implementation of alternative graph-based methods upon the graphs we constructed

The third bullet seems like it would be of particular interest.

In my data mining class we are not using this corpus, unfortunately.  But, if you want to know which words most likely indicate an unfavorable movie review, I should have a classifier that will tell you by next week.

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.