Jan 30, 2018

What If Amazon Ran Hospitals?

What if Dr. Alexa offered you the next appointment with your doctor in the Amazon Clinic? What if you could buy your prescription drugs in Amazon’s online pharmacy? What if you could get your...

Visit my blog to read the whole article and other news about the future of medicine!

from ScienceRoll http://my.onmedic.com/2EmLuMu

Jan 24, 2018

Comprehensive, open tutorial on using data analysis in social science research

Benjamin Mako Hill (previously) collaborated with colleagues involved in critical technology studies to write a textbook chapter analyzing the use of computational methods in social science and providing advice for social scientists who want to delve into data-based social science.

Their chapter is open access, and starts with a history of modern data-driven social science research, from its early days in social network analysis to the contemporary world of consumer analysis and public health research.

But the chapter primarily serves as a tutorial for using data methods with in your own social science research, with extensive code examples and practical advice.

You're invited to improve the paper and check your changes into the Github repo for it.

Data from social media platforms and online communities have fueled the growth of computational social science. In this chapter, we use computational analysis to characterize the state of research on social media and demonstrate the utility of such methods. First, we discuss how to obtain datasets from the APIs published by many social media platforms. Then, we perform some of the most widely used computational analyses on a dataset of social media scholarship we extract from the Scopus bibliographic database’s API. We apply three methods: network analysis, topic modeling using latent Dirichlet allocation, and statistical prediction using machine learning. For each technique, we explain the method and demonstrate how it can be used to draw insights from our dataset. Our analyses reveal overlapping scholarly communities studying social media. We find that early social me- dia research applied social network analysis and quantitative methods, but the most cited and influential work has come from marketing and medical research. We also find that publication venue and, to a lesser degree, textual features of papers explain the largest variation in incoming citations. We conclude with some consideration of the limitations of computational research and future directions.

Introducing Computational Methods to Social Media Scientists [Benjamin Mako Hill]

A Computational Analysis of Social Media Scholarship [Jeremy Foote, Aaron Shaw and Benjamin Mako Hill]

Software and data for "A Computational Analysis of Social Media Scholarship" [Jeremy Foote, Aaron Shaw and Benjamin Mako Hill]

(via 4 Short Links)

from Boing Boing http://my.onmedic.com/2Gb6R3R

Jan 15, 2018

Researchers develop a remote-controlled cancer immunotherapy system

A team of researchers has developed an ultrasound-based system that can non-invasively and remotely control genetic processes in live immune T cells so that they recognize and kill cancer cells. There is a critical need to non-invasively and remotely manipulate cells at a distance, particularly for translational applications in animals and humans, researchers said.

from eHealthNews.EU Portal / All News http://my.onmedic.com/2EKMnxF

Dec 29, 2017

Is Video Game Addiction Unscientific Bullshit?

The World Health Organization is on the verge of officially recognizing a phenomenon that researchers have been studying since the Super Nintendo era: video game addiction. Scientists and public health advocates who back the move say that compulsive video-game playing is a discrete disorder that can seriously damage a…


from Gizmodo: Top http://my.onmedic.com/2zMBrww

Dec 20, 2017

Twitter launches a new enterprise API to power customer service and chatbots

Twitter’s big news this week is its announcement that it’s now enforcing its new policies around hateful content and abuse, but today the company is rolling out something new for developers, as well: an enterprise-level API providing access to real-time activities like tweets, retweets, likes and follows.

The addition of the API is part of Twitter’s broader plan to revamp and expand its API platform, announced this April. Similar to how Twitter is now trying to make things right with its user base -who have too often been the victims of harassment, abuse, hate, and threats of violence – the company has been trying to reset relations with its developer community, too.

Over the years, Twitter has pulled the rug out from underneath developers’ feet too often. For example, it used to encourage third-party apps, then it began restricting them. It hosted developer conferences, then it killed them. And it once offered a suite of developer tools, only to turn around and sell them.

This year, Twitter aimed to turn things around by streamlining its API platform, while taking full advantage of its investment in Gnip. This included the launch of new APIs and endpoints for developers, as well as a published roadmap in an effort to boost transparency around its developer-focused efforts.

Today’s news of the new enterprise Account Activity API is a part of those promised changes.

Specifically, the API is designed to help developers build apps that can power customer service, chatbots and brand engagement on Twitter, the company says – an area Twitter has been increasingly invested in this year.

The existing Account Activity API lets developers pull the full set of activities related to an account, in real-time. The new enterprise version of this API is designed for those who need data for a larger number of accounts, plus multiple webhook URLs, reliability features like retries, and managed support.

In addition, Twitter says it’s expanding the beta for the standard API that delivers activities for up to 35 accounts. And on January 15th, typing indicators and read receipts for Direct Messages will be included as activities in the API, so developers can build more natural conversational experiences. (Meaning, customers will feel like they’re talking to a person, not a bot).

Alongside this launch, Twitter is launching a suite of developer tools for Direct Messages out of beta.

These features include Quick Replies, Welcome Messages, Buttons on messages, Custom Profiles, and Customer Feedback Cards. All have been previously announced, launched, and put to use by brands like Samsung, MTV, TBS, Wendy’s, and Patrón who used the tools with their chatbots. Other, like Tesco and Evernote, are using them for customer service.

The exit from the Direct Message beta will see some features removed, Twitter notes. This includes Location quick replies and location cards, text input quick replies, support indicators, response hours, and the prominent message button on profiles.

These latter features – like support hours and the big message button – seemed to have been launched in response to Facebook Messenger and its own advances as a platform for customer service. But there were some concerns among consumers that the message button the CS account’s profile served as a way to not address customer inquiries and concerns in public, in order to protect the brand’s image.

Twitter’s new APIs and DM toolset are available now.


Featured Image: Bryce Durbin/TechCrunch

from TechCrunch http://my.onmedic.com/2oRWjlA