Publications

Publication date: 
09/2016
Authors: 
Nils B Weidmann
Suso Benitez-Baleato
Philipp Hunziker
Eduard Glatz
Xenofontas Dimitropoulos

Abstract

Publication date: 
08/2016
Authors: 
Amy Krakowka
Chris Arney
Kathryn Coronges
Matthew Simonson

Introduction

Publication date: 
08/2016

Donald Trump has become well known for his shoot-from-the-hip tweeting style. Lots of insults, lots of rants and lots of energy. Data scientists who have examined all of Trump's tweets over time found he has some very clear Twitter strategies and tactics that, in many ways, have been working.

Keywords: 
Trump
Twitter
Publication date: 
07/2016
Authors: 
Stefano Balietti
Robert L. Goldstone
Dirk Helbing
Publication date: 
05/2016
Authors: 
Annie Waldherr
Et Al

Abstract In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue.

Keywords: 
big data
noise
hyperlink network
issue network
web crawler
document classification
Publication date: 
01/2016
Authors: 
Navid Dianati

Empirical networks of weighted dyadic relations often contain “noisy” edges that alter the global characteristics of the network and obfuscate the most important structures therein. Graph pruning is the process of identifying the most significant edges according to a generative null model and extracting the subgraph consisting of those edges.

Publication date: 
10/2015
Authors: 
Ancsa Hannak
David Lazer
Christo Wilson
Alan Mislove

To cope with the immense amount of content on the web, search engines often use complex algorithms to personalize search results for individual users. However, personalization of search results has led to worries about the Filter Bubble Effect, where the personalization algorithm decides that some useful information is irrelevant to the user, and thus prevents them from locating it.

Keywords: 
Search
Personalization
Geolocation
Internet Filter Bubble
Publication date: 
08/2015
Authors: 
Drew Margolin
Brian Keegan
Sasha Goodman
Yu-Ru Lin
David Lazer

The use of socio-technical data to predict elections is a growing research area. We argue that election prediction research suffers from under-specified theoretical models that do not properly distinguish between 'poll-like' and 'prediction market-like' mechanisms understand findings.

Keywords: 
election prediction
crowdsourcing
Wikipedia
politics
social media
communication studies

Publications by type

Journal Article