The Living Thing / Notebooks :

Applied psephology

/images/voting_system.jpg

On the practicalities of weaponised voter modeling in elections, for the purpose of controlling how they vote with special reference to Australian elections. Marketing psychology for governments, and for those who wish to have control of governments.

For now, a scattered collection of links, some of which should probably be filed under filter bubbles.

For the rest, I’m interested in polls; how they can be made more predictive and so on.

TODO: mention problems of ecological inference, Simpson’s paradoxes in electoral demographics, “Symbolic data analysis” for census data etc. Finish up with practical tips.

How it’s being done

Theoretical wrinkles

Refs

AcOP10
Acemoglu, D., Ozdaglar, A., & ParandehGheibi, A. (2010) Spread of (mis)information in social networks. Games and Economic Behavior, 70(2), 194–227. DOI.
ACKM05
Achlioptas, D., Clauset, A., Kempe, D., & Moore, C. (2005) On the Bias of Traceroute Sampling: Or, Power-law Degree Distributions in Regular Graphs. In Proceedings of the Thirty-seventh Annual ACM Symposium on Theory of Computing (pp. 694–703). New York, NY, USA: ACM DOI.
Bail16
Bail, C. A.(2016) Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. Proceedings of the National Academy of Sciences, 201607151. DOI.
BaPe16
Bareinboim, E., & Pearl, J. (2016) Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences, 113(27), 7345–7352. DOI.
BaTP14
Bareinboim, E., Tian, J., & Pearl, J. (2014) Recovering from Selection Bias in Causal and Statistical Inference. In AAAI (pp. 2410–2416).
BFJK12
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H.(2012) A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298. DOI.
BGKR15
Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L.(2015) Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 9(1), 247–274. DOI.
BrKS16
Broockman, D. E., Kalla, J., & Sekhon, J. S.(2016) The Design of Field Experiments With Survey Outcomes: A Framework for Selecting More Efficient, Robust, and Ethical Designs (SSRN Scholarly Paper No. ID 2742869). . Rochester, NY: Social Science Research Network
BGHH13
Bullock, J. G., Gerber, A. S., Hill, S. J., & Huber, G. A.(2013) Partisan Bias in Factual Beliefs about Politics (Working Paper No. 19080). . National Bureau of Economic Research
CBDL00
Cheng, J., Bernstein, M., Danescu-Niculescu-Mizil, C., & Leskovec, J. (n.d.) Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions.
CrSo82
Crawford, V. P., & Sobel, J. (1982) Strategic Information Transmission. Econometrica: Journal of the Econometric Society, 50(6), 1431–1451. DOI.
Degr74
Degroot, M. H.(1974) Reaching a Consensus. Journal of the American Statistical Association, 69(345), 118–121. DOI.
Deni16
Denizet-lewis, B. (2016, April 7) How Do You Change Voters’ Minds? Have a Conversation. The New York Times.
FeWi15
Feinberg, M., & Willer, R. (2015) From Gulf to Bridge When Do Moral Arguments Facilitate Political Influence?. Personality and Social Psychology Bulletin, 41(12), 1665–1681. DOI.
GoMW10
Goel, S., Mason, W., & Watts, D. J.(2010) Real and perceived attitude agreement in social networks. Journal of Personality and Social Psychology, 99(4), 611–621. DOI.
Gran83
Granovetter, M. (1983) The strength of weak ties: A network theory revisited. Sociological Theory, 1(1), 201–233.
Gran73
Granovetter, M. S.(1973) The Strength of Weak Ties. The American Journal of Sociology, 78(6), 1360–1380. DOI.
KiPR00
King, G., Pan, J., & Roberts, M. E.(10000) How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument. American Political Science Review.
Kulk16
Kulkarni, V. (2016) Temporal Evolution of Social Innovation: What Matters?. SIAM Journal on Applied Dynamical Systems, 1485–1500. DOI.
Lyon11
Lyons, R. (2011) The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis. Statistics, Politics, and Policy, 2(1). DOI.
NoNy11
Noel, H., & Nyhan, B. (2011) The “unfriending” problem: The consequences of homophily in friendship retention for causal estimates of social influence. Social Networks, 33(3), 211–218. DOI.
RuWa06
Rubin, D. B., & Waterman, R. P.(2006) Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology. Statistical Science, 21(2), 206–222. DOI.
ShMc16
Shalizi, C. R., & McFowland III, E. (2016) Controlling for Latent Homophily in Social Networks through Inferring Latent Locations. arXiv:1607.06565 [Physics, Stat].
ShTh11
Shalizi, C. R., & Thomas, A. C.(2011) Homophily and Contagion Are Generically Confounded in Observational Social Network Studies. Sociological Methods & Research, 40(2), 211–239. DOI.
TNDL16
Tan, C., Niculae, V., Danescu-Niculescu-Mizil, C., & Lee, L. (2016) Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions. In Proceedings of the 25th International Conference on World Wide Web (pp. 613–624). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee DOI.
RAWF16
van de Rijt, A., Akin, I., Willer, R., & Feinberg, M. (2016) Success-Breeds-Success in Collective Political Behavior: Evidence from a Field Experiment. Sociological Science, 3, 940–950. DOI.
WaDo07
Watts, D. J., & Dodds, P. S.(2007) Influentials, Networks, and Public Opinion Formation. Journal of Consumer Research, 34(4), 441–458. DOI.
YPHS16
Yadav, P., Prunelli, L., Hoff, A., Steinbach, M., Westra, B., Kumar, V., & Simon, G. (2016) Causal Inference in Observational Data. arXiv:1611.04660 [Cs, Stat].
YLSS11
Yang, S.-H., Long, B., Smola, A., Sadagopan, N., Zheng, Z., & Zha, H. (2011) Like Like Alike: Joint Friendship and Interest Propagation in Social Networks. In Proceedings of the 20th International Conference on World Wide Web (pp. 537–546). New York, NY, USA: ACM DOI.
ZURG17
Zarezade, A., Upadhyay, U., Rabiee, H. R., & Gomez-Rodriguez, M. (2017) RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (pp. 51–60). New York, NY, USA: ACM Press DOI.