linkstream2 microblog

http://www.storybench.org/get-twitter-data-rtweet-r/

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🧠

from
https://twitter.com/cmkhealthatwork/status/1073903636982444032

https://twitter.com/YaleData/status/1043196384403443712

https://twitter.com/EvoMRI/status/960633150115282944
Here’s an overview of the GPCR literature as indexed on Wikidata: https://tools.wmflabs.org/scholia/topic/Q38173

https://www.theverge.com/2014/9/3/6101407/twitter-ceo-finally-explains-why-random-favorites-are-inserted-into

.@fionabrinkman @BioMickWatson @iddux Spurious Correlations
(http://tylervigen.com/spurious-correlations) related to Stat Frankenstein (https://twitter.com/markgerstein/status/689478730343837696)

Banjo Raises $100 Million to Detect World Events in Real Time http://blogs.wsj.com/digits/2015/05/06/banjo-raises-100-million-to-detect-world-events-in-real-time Will their global "crystal ball" notice this tweet?

A tool to easily edit DNA transforms research, holds potential for medicine – The Boston Globe
https://www.bostonglobe.com/metro/2014/11/30/tool-easily-edit-dna-transforms-research-holds-potential-for-medicine/X7srGGFardarsWBfCqEL2H/story.html

The technique has become so ubiquitous that it has entered the casual vernacular of science as a verb; people talking about “CRISPRing” genes they want to tweak or delete.

Social media fingerprints of unemployment, from detecting network components in tweet mining arxiv.org/abs/1411.3140 +
http://www.technologyreview.com/view/532746/twitter-exhaust-reveals-patterns-of-unemployment

Lots of press for an arxiv paper, viz:
Twitter “Exhaust” Reveals Patterns of Unemployment | MIT Technology Review

QT:{{”

So the team analysed the rate at which messages were exchanged between regions using a standard community detection algorithm. This revealed 340 independent areas of economic activity, which largely coincide with other measures of geographic and economic distribution. “This result shows that the mobility detected from geolocated tweets and the communities obtained are a good description of economical areas,” they say.

Finally, they looked at the unemployment figures in each of these regions and then mined their database for correlations with twitter activity.

“}}

http://arxiv.org/abs/1309.6001