The Living Thing / Notebooks : Applied psephology

On the practicalities of weaponised voter modeling in elections, for the purpose of controlling how they vote esp Australian elections.

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

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


Acemoglu, D., Ozdaglar, A., & ParandehGheibi, A. (2010) Spread of (mis)information in social networks. Games and Economic Behavior, 70(2), 194–227. DOI.
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.
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.
Bareinboim, E., & Pearl, J. (2016) Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences, 113(27), 7345–7352. DOI.
Bareinboim, E., Tian, J., & Pearl, J. (2014) Recovering from Selection Bias in Causal and Statistical Inference. In AAAI (pp. 2410–2416).
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.
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.
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
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
Cheng, J., Bernstein, M., Danescu-Niculescu-Mizil, C., & Leskovec, J. (n.d.) Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions.
Crawford, V. P., & Sobel, J. (1982) Strategic Information Transmission. Econometrica: Journal of the Econometric Society, 50(6), 1431–1451. DOI.
Degroot, M. H.(1974) Reaching a Consensus. Journal of the American Statistical Association, 69(345), 118–121. DOI.
Denizet-lewis, B. (2016, April 7) How Do You Change Voters’ Minds? Have a Conversation. The New York Times.
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.
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.
Granovetter, M. (1983) The strength of weak ties: A network theory revisited. Sociological Theory, 1(1), 201–233.
Granovetter, M. S.(1973) The Strength of Weak Ties. The American Journal of Sociology, 78(6), 1360–1380. DOI.
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.
Kulkarni, V. (2016) Temporal Evolution of Social Innovation: What Matters?. SIAM Journal on Applied Dynamical Systems, 1485–1500. DOI.
Lyons, R. (2011) The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis. Statistics, Politics, and Policy, 2(1). DOI.
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.
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.
Shalizi, C. R., & McFowland III, E. (2016) Controlling for Latent Homophily in Social Networks through Inferring Latent Locations. arXiv:1607.06565 [Physics, Stat].
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.
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.
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.
Watts, D. J., & Dodds, P. S.(2007) Influentials, Networks, and Public Opinion Formation. Journal of Consumer Research, 34(4), 441–458. DOI.
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].
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.
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.