Please enable JavaScript.
Coggle requires JavaScript to display documents.
Computational Social Science (Universtities (Oxford :silhouettes:…
Computational
Social
Science
Private
Microsoft research :silhouettes:
Duncan Watts :red_flag:
:pencil2:
-social supercollider - have a better picture of individuals and behaviour
Hannah Wallach
#
:confetti_ball:
:pencil2:2018 opinion CSS=/soc science+computer
Google :silhouettes:
Jeremy Ginsberg
:pencil2:predicting the flu
2009
Chris Welty
#
Sage Publishing :silhouettes:
-
Ocean
Facebook :silhouettes:
Lada Adamic
#
:confetti_ball:
Solomon Messing
IBM :silhouettes:
Alexandra Olteanu :confetti_ball:
Universtities
UC Berkeley :silhouettes:
Joshua Blumenstock :red_flag:
:pencil2:
predicting social indicators
with available data
-poverty in Africa with mobile phone metadata
Sebastian Benthall
(philosophy of CSS)
Princeton:silhouettes:
Matt Salganik
:red_flag:
(sociology)
:pencil2:Bit by bit
Northeastern Uni :silhouettes:
David Lazer
:red_flag:
:pencil2:Computational Social Science
article in Nature
2009
Agnes Horvat :confetti_ball:
Cornell University :silhouettes:
CSS
group
Steven Strogatz
(math)
Michael Macy:red_flag:
David Mimmo
Lillian Lee :confetti_ball:
CalTech :silhouettes:
Michael Alvarez
:red_flag:
Computational Social Science :pencil2:
book 2016
MIT:silhouettes:
Alex Pentland
:red_flag:
#
(founder MIT Media Lab)
Imperial (UK) :silhouettes:
-ML initiative
Seth Flaxman
(math)
Yves-Alexandre de Montjoye
(computing)
#
Stanford :silhouettes:
Literary lab
Mark Algee-Hewitt
Franco Moretti
IRiSS - center for computational social science
Jeff Hancock
Karen S Cook :confetti_ball:
sociology
New York University :silhouettes:
Paul DiMaggio
-sociology :silhouette:
:pencil2:
applying computation to text analysis
Josh Tucker
University of Bristol :silhouettes:
Intelligent Systems Lab
Colin Campbell
Nello Cristianini
Science Po :silhouettes:
Bruno Latour
:red_flag:
(philosophy and sociology)
retired
Harvard :silhouettes:
Gary King
:red_flag:
#
perusall
thresher
The OpenScholar
UVA Netherlands:silhouettes:
Damian Trilling
wrote Python for SS book
Vrije Unive Amsterdam :silhouettes:
CrowdTruth
collective intelligence
Lora Aroyo :confetti_ball:
Oana Inel :confetti_ball:
Nebraska :silhouettes:
Stephen Ramsay
(digital humanities)
:pencil2: towards and algorithmic criticism
University of Pennsylvania :silhouettes:
Sandra Gonzalez-Bailon :confetti_ball:
#
Emily Falk :confetti_ball:
University of Minho PT :silhouette:
Inês Amaral :confetti_ball:
Warwick :silhouettes:
Suzy Moat :confetti_ball:
CHANUKI SERESINHE :confetti_ball:
Oxford :silhouettes:
Oxford Internet Institute
Helen Margetts :confetti_ball:
Taha Yasseri
SÍLVIA MAJÓ-VÁZQUEZ :confetti_ball:
Janet Pierrehumbert :confetti_ball:
Melinda Mills
LSE :silhouettes:
Milena Tsvetkova :confetti_ball:
GOKHAN CIFLIKLI
Ken Benoit
UC Davis :silhouettes:
Cuihua Shen :confetti_ball:
Leibnitz Institute for the Social Sciences :silhouettes:
GESIS
Claudia Wagner :confetti_ball:
Katrin Weller :confetti_ball:
Pittsburg :silhouettes:
Janyce Wiebe :confetti_ball:
Diane Litman :confetti_ball:
Santa Fe Institute :silhouettes:
Turing Institute
Essex
James Allen Robertson
data analysis
text analysis
statistical and supervised
Topic modelling
counting co-occurrences/Latent Dirichlet Allocation
bag of words
sentiment based on word frequencies
n-grams
NER
Parts of Speech
rhetorical analysis
EMERGING
causal relation extraction
linguistic - handcrafted patterns (manual); most common is lexical clues
logical rules: also using deep semantic reasoning to infer additional rules
machine learning (supervised, un and semi)
Manual
reading/annotation
unsupervised ML
NLP
distant reading
#
event extraction
summarization
network analysis
social cotagion
clustered networks
multilayer networks
information transfer
interactions over time
:warning:
challenges
ethical: How data is collected
-individual privacy vs 'social supercollider'
more careful use of statistical tests
-confusion around p-values
social media usage biases
crowdsourcing biases
few CSS papers published in social science journals
at least half social scientists
don't have the skills
We need better guidelines for curating textual data
pre-analysis
#
bias towards already digitised content
branches
computational social
choice
-aggregation of preferences
of multiple agents
computational journalism
social network analysis
computational cognition
predictive analytics -business intelligence
collective intelligence
social cyber-security
computational finance
evolution of paradigms
data collection
collect data
-recruit participants
-crowdsource participants
-design hypothesis and testing
citizen science
pop-up [experiments]
(
https://www.frontiersin.org/articles/10.3389/fphy.2015.00093/full
)
reuse/repurpose data
Large available datasets:
-journals & newspapers API
-Google Ngram viewer
-Twitter and other API
-network data
-SEO data
etc
generate data
by simulation
:star:
advantages
large scale analysis
understanding of opinions and biases
understand dynamics of
social movements
scalable
predict poverty with mobile phone metadata
Blumenstock paper; best example of low cost prediction
Funding & Councils
DARPA :silhouettes:
Adam Russell
NSF :silhouettes:
Sloan :silhouettes:
Josh Greenberg
Consiglio Nazionale delle Ricerche :silhouettes:
Giulia Andrighetto :confetti_ball:
EC Joint Res Centre :silhouettes:
Alexandra Balahur :confetti_ball:
other groups/initiatives
CSS London
Ic2s2
community and conferece
Social Science One
(facebook)
LinkedIn Economic Graph Research Program
cool projects, tools and other examples
Scholia
Internet Archive
virtual reading
room
CrowdTruth
visualisation of Tolkien books:
http://lotrproject.com/statistics/books/
emotions in 20th books:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059030
pronoun frequencies by gender over time :
http://languagelog.ldc.upenn.edu/nll/?p=4126
Google nGram viewer
nelatoolkit.science
Bloomsbury.ai
(acquired by Facebook)
simulations
agent based simulations
system level simulations
