27.9K views | +1 today
antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy
Your new post is loading...
Your new post is loading...
Scooped by luiy

Want to be Retweeted? Large Scale Analytics on #Factors Impacting Retweet in #Twitter Network | #datascience

No comment yet.
Rescooped by luiy from Shifting Minds & Communities

The World Religions Tree I #dataviz #anthropology #diffusion

The World Religions Tree I #dataviz #anthropology #diffusion | e-Xploration | Scoop.it

Dynamic infographic on world religions (don't be intimidated by the page being in Russian... The graphic is not).

Via Seth Dixon, Glenis Joyce
Abby Laybourn's curator insight, December 10, 2014 1:25 PM

Although this was kind of hard to read it was interesting to see how different religions are related and where they stem from. 

Marita Viitanen's curator insight, January 31, 2015 6:48 PM

Tämä puu jotakuinkin hämmentää...

Emma Conde's curator insight, May 26, 2015 9:16 PM

Unit 1 Geography: Its nature and perspectives

Although the article relating to this diagram is in Russian, the diagram is not, and I found it to be a very interesting visual to not only show world religions developing on a time scale, but also because it does a very good job of showing just how many little divisions of each religion they are, and how they are all intertwined. Zooming in on the diagram, you are able to see each divide, each new branch, and each date for hundreds of sets of information.


This illustrates the theme of identification of major world religions because it simply shows the mass amounts of tiny divisions that occur in the major world religions in a simple format. This is very helpful because this would be pages of writing if you tried to write it all out. 

Scooped by luiy

Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet Rate in Twitter Network | #datascience

Presented at SocialCom 2010 conference Aug 21st 2010
luiy's insight:

Retweeting is the key mechanism for information
diffusion in Twitter. It emerged as a simple yet powerful way of
disseminating information in the Twitter social network. Even
though a lot of information is shared in Twitter, little is known
yet about how and why certain information spreads more widely
than others. In this paper, we examine a number of features that
might affect retweetability of tweets. We gathered content and
contextual features from 74M tweets and used this data set to
identify factors that are significantly associated with retweet rate.
We also built a predictive retweet model. We found that, amongst
content features, URLs and hashtags have strong relationships
with retweetability. Amongst contextual features, the number of
followers and followees as well as the age of the account seem to
affect retweetability, while, interestingly, the number of past
tweets does not predict retweetability of a user’s tweet. We
believe that this research would inform the design of sensemaking 

and analytics tools for social media streams.


Article in :  http://www-users.cs.umn.edu/~echi/papers/2010-socialcom/2010-06-25-retweetability-cameraready-v3.pdf

No comment yet.