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With the election of Doug Ford to head the PC party, Ontario may have lost 
its smugness vis-a-vis the Trumpian US.

   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
    The earth to be spannd, connected by network -- Walt Whitman
        It's Always Something -- Roseanne Roseannadanna
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman  

The science of fake news

The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.

The science of fake news
David M. J. Lazer, Matthew A. Baum, Yochai Benkler, Adam J. Berinsky, Kelly M. Greenhill, Filippo Menczer, Miriam J. Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, Michael Schudson, Steven A. Sloman, Cass R. Sunstein, Emily A. Thorson, Duncan J. Watts, Jonathan L. Zittrain

Science  09 Mar 2018:
Vol. 359, Issue 6380, pp. 1094-1096
DOI: 10.1126/science.aao2998

Source: (

The spread of true and false news online

We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots
accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

The spread of true and false news online
Soroush Vosoughi, Deb Roy, Sinan Aral

Science  09 Mar 2018:
Vol. 359, Issue 6380, pp. 1146-1151
DOI: 10.1126/science.aap9559

Source: (

Who holds the power?

    What was the cause of Donald Trump's stunning victory over Hillary 
Clinton in the 2016 U.S. presidential election? Was it the peculiarities 
of the electoral college? Voter resistance to three-term rule by a single 
party? Anxiety about illegal immigration? As Niall Ferguson explains in 
The Square and the Tower, the answer lies largely in one word: networks.

Who holds the power?
Sean P. Cornelius
The Square and the Tower: Networks and Power, from the Freemasons to Facebook Niall Ferguson Penguin Press, 2018. 607 pp.
Science  09 Mar 2018:
Vol. 359, Issue 6380, pp. 1109
DOI: 10.1126/science.aar8692

Source: (

Autonomous agents modelling other agents: A comprehensive survey and open problems

    Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.

Autonomous agents modelling other agents: A comprehensive survey and open problems
Stefano V.Albrecht, PeterStone

Artificial Intelligence
Volume 258, May 2018, Pages 66-95

Source: (

Not-so-distant reading: A dynamic network approach to literature

    In this article we report about our efforts to develop and evaluate 
computational support tools for literary studies. We present a novel 
method and tool that allows interactive visual analytics of character 
occurrences in Victorian novels, and has been handed to humanities 
scholars and students for work with a number of novels from different 
authors. Our user study reveals insights about Victorian novels that are 
valuable for scholars in the digital humanities field, and informs UI as 
well as UX designers about how these domain experts interact with tools 
that leverage network science.

Not-so-distant reading: A dynamic network approach to literature

Markus Luczak-Roesch, Adam Grener, Emma Fenton

it - Information Technology
Published Online: 2018-02-28 | DOI:

Source: (

Network Medicine ˙˙18

Network science is having a huge impact in various aspects of 
health-related research. The advent of more comprehensive personalized 
data has fostered the emergence of precision medicine: an approach that 
considers individual genetic and physiological characteristics, lifestyle 
and environment in devising personalized therapies. Such an approach 
benefits greatly from big data and network approaches at multiple levels. 
First, integrating different molecular ˙˙interactomic˙˙ datasets from a 
single patient, from subcellular to organ-level, is a fundamental step 
towards understanding the mechanistic underpinnings of personalized health 
states. Moreover, there has been a continued improvement in the 
availability of health-related data, such as electronic health records 
detailing patient histories and providing accurate diseases statistics.

Network Medicine '18
Personalized Medicine in the Era of Big Data

Source: (

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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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