Thursday, December 12, 2013
Friday, December 6, 2013
Wednesday, December 4, 2013
Tuesday, November 19, 2013
"Political scientists often talk about dyads, by which we simply mean groups of two. In this case, a dyad refers to a pair of TPP countries. If we count up every instance that the United States appears in the same marker as, say, Australia, we can say that the U.S.-Australia dyad occurs with a certain frequency. If we did this for every possible dyad, we could compare the frequency of dyads and get a sense of how often countries’ negotiating positions overlap. The following chart displays the frequency of every possible dyad among the 12 TPP countries. For example, the U.S.-Australia dyad (AU-US) appears 83 times in the leaked text, and is the 43rd most frequent dyad. Note that the order doesn’t matter: a U.S.-Australia dyad is the same as an Australia-U.S. dyad."
Friday, November 15, 2013
While Yale sociologists find social networks in victims of fatal shootings, the Chicago PD takes SNA in order to predict a "heat list"of likely shooters to reach out to. Fascinating and a bit troubling.
Sunday, November 10, 2013
Tuesday, October 29, 2013
Monday, October 28, 2013
Sunday, October 27, 2013
Saturday, October 26, 2013
Check out the video from this event with Prof. Zeynep Tifekci talking about the Gezi Park protests in Turkey and the influence of social media and technology on the "boom and bust" protests around the world in recent years:
Friday, October 25, 2013
Maintenance of social networks of first-time offenders to aid their integration into society upon release
- Degree Centrality: to identify the number of connections different departments have among themselves and with the national chapters and vice versa.
- Closeness: to identify which departments have the most ties with chapters and vice versa.
- Betweeness: to understand how the flow of information is controlled.
- The connection amongst departments and chapters very often depends on the personal ties created by the employees and therefore the analysis would change if personnel leave.
- The information drawn from the survey could potentially be incomplete since we would be collecting data from departments that are composed by more than 1 person and it is difficult to ensure the participation of everyone.
The rise in the number of gerrymandered districts in the United States corresponds to an increasing tendency toward extremism and partisanship in American politics. The two trends are particularly disconcerting because they empower each other, resulting in Congressional sessions that are both inefficient and arguably ineffective. Public frustration has led to calls for fairer districting and more bipartisanship in Congress. One suggestion for producing more moderate politicians is to establish fairer districts – districts in which there will not be a “safe seat” in an election and candidates must appeal to swing voters to achieve a victory.
While research on the benefits of such highly competitive elections has proven inconclusive, the idea is intuitively appealing. Garnering support for fairer districting, however, will require more than intuitive sense. Consequently, this project seeks to contribute to the evolving body of research on the effects of “highly contested” elections through the employment of social network analysis. It will focus on the House of Representatives, where districts shape the election and politicians are constantly considering the next election cycle.
Objective & Research Question
Do highly contested elections produce Representatives who are more or less partisan than their House colleagues?
Because highly contested elections depend heavily on the choices of swing voters, it is hypothesized that Representatives from highly contested districts will want to “signal” that they not only share the ideological leanings of voters on both sides of the political spectrum but also support policies that produce outcomes all of their constituents desire. They will consequently form networks with stronger bonds across party lines than their “safe seat” peers.
The project will start with an analysis of a two-mode network representing affiliations between Representatives during House sessions (one network per session). Nodes will be analyzed using degree centrality, betweenness centrality, and eigenvector centrality.
Network links between Representatives can demonstrate the “strength” of connections between Representatives by highlighting how frequently they support each other or interact at a policy level. Analysis on these networks will seek to determine whether bipartisan Representatives (“highly contested” Representatives) are actually at the center of the House network or act rather as links between dense, centralized party networks.
The majority of the analysis will be conducted through network comparisons, attempting to draw conclusions from the differences in patterns and network structures. For example, the project will seek to compare the ego network of a highly contested Representative to the ego network of a “safe seat” Representative with the hope that the process will provide insight into their structure, density, and affiliations. And, in the event that enough data can be collected, the project will also attempt to compare a House network during a congressional session in which there were more highly contested seats with a House network during a congressional session in which there were fewer.
Data will be drawn from publically available information on Representatives themselves, House voting records, committee membership, and House bills (passed and unpassed).
Primary data will be collected for House networks, including: • Representatives’ connections through their voting records. • Representatives’ connections through the bills they sponsor – with whom and on what issues.
Attribute data will be collected for each Representative, including: • Gender • Party Affiliation (republican, democrat, other) • Election results (margin of victory) • Number of previous terms • Education • Age • Issues important to voting constituents during the election (jobs, healthcare, farm subsidies, etc) • Committee memberships
Limitations and other considerations
Data limitations –
Project will be conducted using only information that is publically accessible. House networks calculated through this project will therefore reflect nothing about the personal relationships between Representatives or any behind-the-scenes politicking that could very well indicate stronger bipartisanship than voting records and bill sponsorship could demonstrate.
Things to Consider –
It is going to be necessary to define “highly contested” in the context of elections so as to ensure that consistency is maintained in distinguishing highly contested Representatives over multiple House sessions. One way to define the phrase might be to establish a specific range of “margins of victory”; any Representatives who win their elections by a percentage of votes within the established margin will be considered a “highly contested” Representative. Additional questions include: What does “bipartisan” look like in a social network? Is it an equal number of connections among both parties or just stronger connections across party lines (in relative terms) than party peers? The project will also require clarifying how many Congressional sessions would be necessary for valid analysis.