Predicting Voting Behavior Using Digital Trace Data

A major concern arising from ubiquitous tracking of individuals’ online activity is that algorithms may be trained to predict personal sensitive information, even for users who do not wish to revea...

[1]  Koen W. De Bock,et al.  Predicting Website Audience Demographics forWeb Advertising Targeting Using Multi-Website Clickstream Data , 2010, Fundam. Informaticae.

[2]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[3]  J. G. Bethlehem,et al.  How representative are online panels? Problems of coverage and selection and possible solutions , 2011 .

[4]  Norbert Pohlmann,et al.  Measuring the Impact of the GDPR on Data Sharing in Ad Networks , 2018, AsiaCCS.

[5]  Aleecia M. McDonald,et al.  The Cost of Reading Privacy Policies , 2009 .

[6]  Prasant Mohapatra,et al.  Predicting user traits from a snapshot of apps installed on a smartphone , 2014, MOCO.

[7]  J. Friedman Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[8]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[9]  Antoine Pultier,et al.  Losing Control to Data-Hungry Apps: A Mixed-Methods Approach to Mobile App Privacy , 2018, Social Science Computer Review.

[10]  L. Festinger A Theory of Cognitive Dissonance , 1957 .

[11]  Justin M. Rao,et al.  Filter Bubbles, Echo Chambers, and Online News Consumption , 2016 .

[12]  Kieran Healy,et al.  Classification situations: Life-chances in the neoliberal era , 2013 .

[13]  Roxana Geambasu,et al.  Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence , 2015, CCS.

[14]  Jun Zhao,et al.  Third Party Tracking in the Mobile Ecosystem , 2018, WebSci.

[15]  Prasant Mohapatra,et al.  Your Installed Apps Reveal Your Gender and More! , 2015, MOCO.

[16]  Dan Murray,et al.  Inferring Demographic Attributes of Anonymus Internet Users , 1999, WEBKDD.

[17]  S. Presser CAN CHANGES IN CONTEXT REDUCE VOTE OVERPORTING IN SURVEYS , 1990 .

[18]  Yang Wang,et al.  Smart, useful, scary, creepy: perceptions of online behavioral advertising , 2012, SOUPS.

[19]  Aaron Alva,et al.  Cross-Device Tracking: Measurement and Disclosures , 2017, Proc. Priv. Enhancing Technol..

[20]  Daniela V. Dimitrova,et al.  The Effects of Digital Media on Political Knowledge and Participation in Election Campaigns , 2014, Commun. Res..

[21]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[22]  Steven M. Bellovin,et al.  A Privacy Analysis of Cross-device Tracking , 2017, USENIX Security Symposium.

[23]  Hua Li,et al.  Demographic prediction based on user's browsing behavior , 2007, WWW '07.

[24]  Joanne Hinds,et al.  What demographic attributes do our digital footprints reveal? A systematic review , 2018, PloS one.

[25]  Natasa Milic-Frayling,et al.  Network Analysis of Third Party Tracking: User Exposure to Tracking Cookies through Search , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[26]  Shelley Boulianne Does Internet Use Affect Engagement? A Meta-Analysis of Research , 2009 .

[27]  Derek Ruths,et al.  Classifying Political Orientation on Twitter: It's Not Easy! , 2013, ICWSM.

[28]  Hervais Simo Fhom,et al.  Big Data: Opportunities and Privacy Challenges , 2015, ArXiv.

[29]  Sanne Kruikemeier,et al.  Unraveling the effects of active and passive forms of political Internet use: Does it affect citizens’ political involvement? , 2014, New Media Soc..

[30]  Kieran Healy,et al.  Classification situations: Life-chances in the neoliberal era , 2013 .

[31]  S. Kruschinski,et al.  Restrictions on data-driven political micro-targeting in Germany , 2017 .

[32]  Edward W. Felten,et al.  Cookies That Give You Away: The Surveillance Implications of Web Tracking , 2015, WWW.

[33]  Victor C. M. Leung,et al.  Demographic Information Prediction: A Portrait of Smartphone Application Users , 2018, IEEE Transactions on Emerging Topics in Computing.

[34]  Narseo Vallina-Rodriguez,et al.  Tracking the Trackers: Towards Understanding the Mobile Advertising and Tracking Ecosystem , 2016, ArXiv.

[35]  Kurt Hornik,et al.  The Design and Analysis of Benchmark Experiments , 2005 .

[36]  K. Kenski,et al.  Connections Between Internet Use and Political Efficacy, Knowledge, and Participation , 2006 .

[37]  Daniel Gatica-Perez,et al.  Mining large-scale smartphone data for personality studies , 2013, Personal and Ubiquitous Computing.

[38]  Helen Nissenbaum,et al.  Privacy in Context - Technology, Policy, and the Integrity of Social Life , 2009 .

[39]  Jacob Ratkiewicz,et al.  Predicting the Political Alignment of Twitter Users , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[40]  Sotiris Ioannidis,et al.  Automated Measurements of Cross-Device Tracking , 2018, IOSec@RAID.

[41]  Arvind Narayanan,et al.  Online Tracking: A 1-million-site Measurement and Analysis , 2016, CCS.

[42]  Bill Fitzgerald,et al.  Tracking the Trackers , 2016 .

[43]  A. Buss,et al.  Personality Traits , 1973 .

[44]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[45]  Mary J. Culnan,et al.  Strategies for reducing online privacy risks: Why consumers read (or don't read) online privacy notices , 2004 .

[46]  Ericka Menchen-Trevino,et al.  The extent and nature of ideological selective exposure online: Combining survey responses with actual web log data from the 2013 Israeli Elections , 2016, New Media Soc..

[47]  Sotiris Ioannidis,et al.  Cross-Device Tracking: Systematic Method to Detect and Measure CDT , 2018, ArXiv.

[48]  M. Bühner,et al.  Personality Traits Predict Smartphone Usage , 2017 .

[49]  Walter Rudametkin,et al.  Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints , 2016, 2016 IEEE Symposium on Security and Privacy (SP).

[50]  Ingmar Weber,et al.  You Are What Apps You Use: Demographic Prediction Based on User's Apps , 2016, ICWSM.

[51]  Daniel Gayo-Avello,et al.  A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data , 2012, ArXiv.

[52]  David W. Nickerson,et al.  Political Campaigns and Big Data , 2014 .

[53]  Timothy Libert,et al.  Exposing the Hidden Web: An Analysis of Third-Party HTTP Requests on 1 Million Websites , 2015, ArXiv.

[54]  Tadayoshi Kohno,et al.  Internet Jones and the Raiders of the Lost Trackers: An Archaeological Study of Web Tracking from 1996 to 2016 , 2016, USENIX Security Symposium.

[55]  E. Peterson Echo Chambers and Partisan Polarization : Evidence from the 2016 Presidential Campaign , 2017 .

[56]  Blase Ur,et al.  Unpacking Perceptions of Data-Driven Inferences Underlying Online Targeting and Personalization , 2018, CHI.

[57]  Yong Zhang,et al.  Targeted Advertising Based on Browsing History , 2017, ArXiv.

[58]  Evangelos P. Markatos,et al.  Cookie Synchronization: Everything You Always Wanted to Know But Were Afraid to Ask , 2018, WWW.

[59]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.