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A Data Analysis of iTunes’ Top Charts Algorithm | dataworks
http://data.betaworks.com/data-analysis-itunes-top-charts-algorithm
A Data Analysis of iTunes’ Top Charts Algorithm. Jan 20, 2015 0 comments. A few days ago I published an in depth analysis of Apple’s iTunes top free chart algorithm, boosting, rank manipulation and algorithmic glitching – on medium.com. Here’s the overview:. Encoded within the iTunes app store algorithm is the power to make or break an app. If you get on its good side, you do really well, and if not, you lose. If these volatile days are deliberate, shouldn’t we be informed? These are people who pour coun...
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Israel, Gaza, War & Data | dataworks
http://data.betaworks.com/israel-gaza-war-data
Israel, Gaza, War & Data. By suman deb roy. Aug 26, 2014 0 comments. Chief scientist at betaworks wrote a recent post. That was featured by NPR, BBC and several other media. Gilad shows that Social Media is being actively used in the art of personalizing propaganda, and while war rages on the ground in Gaza and across Israeli skies, there’s an all-out information war unraveling in social networked spaces. Here are some highlights:. 8230; on social networks:. 8230; on algorithmic filtering:. 8230;and a be...
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No #MarchMadness Hate on Twitter? | dataworks
http://data.betaworks.com/no-marchmadness-hate-on-twitter
No #MarchMadness Hate on Twitter? Apr 4, 2014 0 comments. Last week the Digg editors came to us with an idea. Wouldn’t it be great if we could do a joint data post on March Madness, but instead of the usual – looking at the account with the most likes, or retweets – we would take a very different approach. Their idea was to find the most disliked team on social media, and map that out. March madness is a love / hate relationship. I hate it right now. Mdash; Dylan Lamb (@dylanlamb ) March 30, 2014. 8220;F...
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The Digital Flames of Ferguson | dataworks
http://data.betaworks.com/the-digital-flames-of-ferguson
The Digital Flames of Ferguson. By suman deb roy. Aug 26, 2014 0 comments. I wrote a recent post. Articulating the fragmentation around media coverage in Ferguson and the impact of social media in spreading news about events occurring in Ferguson, eventually gaining national attention. Using Twitter trending topics data, I show. How the country’s attention gradually turned to the events transpiring in Ferguson,. The driving factors and what it tells us about. Certain latent kinetics of the Social Web.
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suman deb roy | dataworks
http://data.betaworks.com/author/suman
Retrospective Event Detection in News. By suman deb roy. Aug 25, 2015 0 comments. This is a project developed by Chris Hidey. Who spent this summer interning with us at the betaworks data science team, focusing on natural language understanding and event detection in news streams. In Chris’ words:. The project I worked on this summer was to develop a method that algorithmically generates timelines around a given news subject. A “subject” can be any topic or event, such as. Or the ongoing news coverage of.
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Digg Reader Visualized: what millions of RSS feeds look like | dataworks
http://data.betaworks.com/digg-reader-visualized-rss-feeds
Digg Reader Visualized: what millions of RSS feeds look like. Jul 1, 2013 0 comments. The mass adoption of RSS as a publishing/subscription protocol, means that it is a great way to bypass the growing number of walled gardens created by social networks, where homophily is dominant. Alternatively, with feed subscription we see much more cross-domain bridging happening. Within the English content region, the popular feeds split into three main types:. Is pretty high up there. 2) To the right, we see a dens...
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gilad | dataworks
http://data.betaworks.com/author/gilad
Beyond Trending Topics: identifying important conversations in communities. Sep 1, 2015 0 comments. One of the newest companies to launch out of betaworks, helps identify, follow and reach communities on Twitter. While there’s a great visual dashboard that gives us a way to look at what’s bubbling up from within communities, it is still hard to evaluate which items appear on a regular basis, and which are more unique. For example, in the. Or more specifically, deviates from the expected behavior? Https:/...
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How ‘Instapaper Daily’ remembers the most interesting stories | dataworks
http://data.betaworks.com/how-instapaper-daily-remembers-the-most-interesting-stories
How ‘Instapaper Daily’ remembers the most interesting stories. By suman deb roy. Mar 11, 2014 0 comments. Our attempt to alleviate the first problem was in creating InstaRank. We introduced Instapaper Daily. It culminates into a fascinating dataset for exploring story topics and topic longevity along each timeline. The following analysis uses the last three months of timeline data. However, if the title word space is too sparse, we employ a DBpedia. Whereas ‘The Hunger Games’ will map to topic ‘. 8216;)&...
data.betaworks.com
Will your news trend on Facebook? Driving factors behind Facebook trending topics | dataworks
http://data.betaworks.com/will-your-news-trend-on-facebook-driving-factors-behind-facebook-trending-topics
Will your news trend on Facebook? Driving factors behind Facebook trending topics. By suman deb roy. Oct 12, 2014 0 comments. Earlier this year, Facebook announced the launch of trending topics. On its newsfeed page. Like Twitter trends, which reflect the attention landscape in the Twittersphere. Massive pro-democracy protests took place in Hong Kong last week. More than half a million individuals (most of whom were students) decided to occupy Central. The heart of Hong Kong. I chronologically. When enou...
data.betaworks.com
media | dataworks
http://data.betaworks.com/category/media
Retrospective Event Detection in News. By suman deb roy. Aug 25, 2015 0 comments. This is a project developed by Chris Hidey. Who spent this summer interning with us at the betaworks data science team, focusing on natural language understanding and event detection in news streams. In Chris’ words:. The project I worked on this summer was to develop a method that algorithmically generates timelines around a given news subject. A “subject” can be any topic or event, such as. Or the ongoing news coverage of.
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