In The Clash of Civilizations, Samuel Huntington argued that the primary axis of global conflict was no longer ideological or economic but cultural and religious, and that this division would characterize the "battle lines of the future.
Our analysis shows that email flows are consistent with Huntington's thesis. In addition to location in Huntington's "civilizations," our results also attest to the importance of both cultural and economic factors in the patterning of inter-country communication ties.
Superpositions of social networks, such as communication, friendship, or trade networks, are called multiplex networks, forming the structural backbone of human societies. Novel datasets now allow quantification and exploration of multiplex networks.
On the individual level females perform better economically and are less risk-taking than males. Males reciprocate friendship requests from females faster than vice versa and hesitate to reciprocate hostile actions of females.
On the network level females have more communication partners, who are less connected than partners of males. We find a strong homophily effect for females and higher clustering coefficients of females in trade and attack networks. Cooperative links between males are under-represented, reflecting competition for resources among males.
These results confirm quantitatively that females and males manage their social networks in substantially different ways.
Twitter is a social network that network scientists refer to as an asymmetric network -- the links are directional, they drawn with arrows. Links between people on Twitter show direction of intent. The arrows are drawn from source to target.
Looking at a social graph from Twitter we can tell a lot by following the arrows...
who is aware of whom/what?
whom/what is getting attention?
who is involved in conversations on specific topics?
who is central, and who is peripheral to the discussions?
15m.zip contains this “readme.txt” plus two files with information about Twitter messages that were collected in the period April-May 2011. These messages are related to the political events occurred at that time in Spain, and only messages in Spanish are considered.
Georgetown University's peer-reviewed Journal of Communication, Culture & Technology (CCT)
The researchers used such linguistic measures as how an individual uses the first person singular pronoun (e.g. “I”, “I’ve”, “me”, “mine”), which has been shown to increase as a speaker interacts with someone of higher status. The researchers found that the technical skill-based roles, such as those of programmers and analysts, elicited higher levels of respect than those of more managerial roles
A lot of community managers just go with their gut on this one, or use proxy metrics like signups, posts per day, klout scores, retweets or some other metric that is fairly hollow, but there are better ways.
This post is the first one from the category “show me your network and I’ll tell you a story”. My goal here is to show how one can grow a business by leveraging publicly available data, mostly from...
Once we have our interest graph we can run some computations and understand:
who are our potential clients/attendeeswho can help us spread the word about the event and reach non-random audiencewho are the most influential users in our target market in general and who are the stars from the local scene
Modern technologies not only provide a variety of communication modes, e.g., texting, cellphone conversation, and online instant messaging, but they also provide detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts.
Here, we study the inter-call durations of the 100,000 most-active cellphone users of a Chinese mobile phone operator. We confirm that the inter-call durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the inter-call durations follow a power-law distribution for only 3460 individuals (3.46%). The inter-call durations for the majority (73.34%) follow a Weibull distribution.