Can Latin American Voters see the Future?
Citizen Forecasting in Argentina
Abstract The present study examined whether Argentinian citizens could predict election results at the sub-national level. We targeted Argentinian Twitter users in seven provinces with polls using Twitter Ads. Argentinian Twitter users constitute a high-ability subgroup that possesses several characteristics that enhance citizen forecasting competence. The polls asked citizens to predict what party would win the first round of the upcoming presidential election in their province. We present a preliminary citizen forecast of the first round of the 2023 Argentinian presidential election. The forecast demonstrates three preliminary findings. First, citizens expect a competitive election in their respective provinces. Second, citizens in almost all the provinces expect an opposition victory. Finally, a high degree of uncertainty surrounds these predictions, with no party obtaining a greater than 50 percent probability of winning in any of the provinces.
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Arce, M., & Vera, S. (2022). Choosing the lesser evil: Forecasting presidential elections in Peru. Revista Latinoamericana De Opinión Pública, 11(1), 55-80. https://doi.org/10.14201/rlop.25805
Basyouni, S. (2021). Social networking sites and political knowledge: Factors that affect individuals’ knowledge acquisition from Facebook and Twitter. International Journal of Innovation, Creativity, and Change, 15(10), 259-275.
Beatriz Fernández, C., & Rodríguez-Virgili, J. (2019). Electors are from Facebook, political geeks are from Twitter: Political information consumption in Argentina, Spain, and
Venezuela. KOME, 7(1), 42-62. http://doi.org/10.17646/KOME.75698.62
Bélanger, E., & Trotter, D. (2017). Econometric approaches to forecasting. In Arzheimer, K., Evans, J. & Lewis-Beck, M. S. (Eds.), The SAGE Handbook of Electoral Behaviour (pp. 813-834). SAGE. https://doi.org/10.4135/9781473957978
Bertholini, F., Rennó, L., & Turgeon, M. (2022). Against all odds : Forecasting Brazilian elections in times of political disruption. Revista Latinoamericana de Opinión Pública, 11(1), 129-147. https://doi.org/10.14201/rlop.25882
Blais, A., & Bodet, M. A. (2006). How do voters form expectations about the parties’ chances of winning the election? Social Science Quarterly, 87(3), 477-493. https://doi.org/10.1111/j.1540-6237.2006.00392.x
Bolsen, T., Druckman, J. N., & Cook, F. L. (2014). The influence of partisan motivated reasoning on public opinion. Political Behavior, 36, 235-262. https://doi.org/10.1007/s11109-013-9238-0
Booth, A. (2022, Dec. 6). Argentina’s Cristina Fernández sentenced to six years in $1bn fraud case. The Guardian. https://www.theguardian.com/world/2022/dec/06/cristina-fernandez-de-kirchner-argentina-sentenced-prison-fraud-case
Boukes, M. (2019). Social network sites and acquiring current affairs knowledge: The impact of Twitter and Facebook usage on learning about the news. Journal of Information Technology & Politics, 16(1), 36-51. https://doi.org/10.1080/19331681.2019.1572568
Brusco, V., Nazareno, M., & Stokes, S. C. (2004). Vote buying in Argentina. Latin American Research Review, 39(2), 66-88. https://doi.org/10.1353/lar.2004.0022
Bunker, K. (2020). A two-stage model to forecast elections in new democracies. International Journal of Forecasting, 36(4), 1407-1419. https://doi.org/10.1016/j.ijforecast.2020.02.004
Cámara Nacional Electoral. (2023, October 30). Actas de escrutinio definitive – Generales 2023. Elecciones nacionales. https://www.electoral.gob.ar/nuevo/paginas/btn/actas_esc_generales2023.php
Campbell, J. E. (2000). The science of forecasting elections. In J. E. Campbell & J. C. Garand (Eds.), Before the vote: Forecasting American national elections (pp. 169-187). Sage Publications.
Campbell, J. E., & Lewis-Beck, M. S. (2008). US presidential election forecasting: An introduction. International Journal of Forecasting, 24(2), 189-192. https://doi.org/10.1016/j.ijforecast.2008.02.003
Castillo, J. G., Portillo, J. M., & Riojas, D. V. (2021). Fallaron las encuestas y los pronósticos en los resultados electorales de 2020 en Estados Unidos? Derecho Electoral, 31(31), 231-251. https://doi.org/10.35242/RDE_2021_31_12
Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of communication, 64(2), 317-332.
https://doi.org/10.1111/jcom.12084
Congosto, M. L., Fernández, M., & Esteban, M. E. (2011). Twitter y política: Información, opinion, y predicción? Cuadernos de Comunicación Evoca, 4. https://e-archivo.uc3m.es/handle/10016/21631
Cook, C. E., & Wasserman, D. (2014). Recalibrating ratings for a new normal. PS: Political Science & Politics, 47(2), 304-308. https://doi.org/10.1017/S1049096514000079
Dolan, K. A., & Holbrook, T. M. (2001). Knowing versus caring: The role of affect and cognition in political perceptions. Political Psychology, 22(1), 27-44.
Ganser, C., & Riordan, P. (2015). Vote expectations at the next level: Trying to predict vote shares in the 2013 German federal election by polling expectations. Electoral Studies, 40, 115-126. https://doi.org/10.1016/j.electstud.2015.08.001
Graefe, A. (2016). Forecasting proportional representation elections from non-representative expectation surveys. Electoral Studies, 42, 222-228. https://doi.org/10.1016/j.electstud.2016.03.001
Harrison, C. (2023, July 17). What are Argentina’s presidential primaries and who’s running? Americas Society Council of the Americas. https://www.as-coa.org/articles/what-are-argentinas-paso-presidential-primaries-and-whos-running
Herrera L. C., L., & Relmucao, J. J. (2022, April 21). Argentina 20 years after La Crisis del 2001. North American Congress on Latin America. https://nacla.org/argentina-20-years-after-la-crisis-del-2001.
Jennings, W., Lewis-Beck, M. S., & Wleizen, C. (2020). Election forecasting: Too far out? International Journal of Forecasting, 36(3), 949-962. https://doi.org/10.1016/j.ijforecast.2019.12.002
Leiter, D., Reilly, J., & Stegmaier, M. (2020). Echoing certainty in uncertain times: Network partisan agreement and the quality of citizen forecasts in the 2015 Canadian election. Electoral Studies, 63, Article 102115. https://doi.org/10.1016/j.electstud.2019.102115
Leiter, D., Murr, A., Rascon Ramirez, E., & Stegmaier, M. (2018). Social networks and election forecasting: The more friends the better. International Journal of Forecasting, 34(1), 235-248. https://doi.org/10.1016/j.ijforecast.2017.11.006
Lewis-Beck, M.S., & Bélanger, É. (2012). Election forecasting in neglected democracies: An introduction. International Journal of Forecasting, 28(4), 767-768. https://doi.org/10.1016/j.ijforecast.2012.04.006
Lewis-Beck, M. S., & Tien, C. (1999). Voters as forecasters: A micromodel of election prediction. International Journal of Forecasting, 15(2), 175-184. https://doi.org/10.1016/S0169-2070(98)00063-6
Luckner, S., Schroder, J., & Slamka, C. (2012). Prediction markets: Fundamentals, design, and applications. Gabler Verlag.
Misculin, N., Raszewski, E., & Grimberg, C. (2023, Aug. 14). Argentine far-right outsider Javier Milei posts shock win in primary election. Reuters. https://www.reuters.com/world/americas/argentina-set-primary-vote-with-ruling-peronists-fighting-survival-2023-08-13/
Mongrain, P. (2022). With a little help from my friends? The impact of social networks on citizens’ forecasting ability. European Journal of Political Research. Article 20221208. https://doi.org/10.1111/1475-6765.12576
Mongrain, P. (2021a). Did you see it coming? Explaining the accuracy of voter expectations for district and (sub)national election outcomes in multi-party systems. Electoral Studies, 71, Article 102317. https://doi.org/10.1016/j.electstud.2021.102317
Mongrain, P. (2021b). A technocratic view of election forecasting: Weighting citizens’ forecasts according to competence. International Journal of Public Opinion Research, 33(3), 713-723. https://doi.org/10.1093/ijpor/edab010
Murr, A., Stegmaier, M., & Lewis-Beck, M. S. (2021). Vote expectations versus vote intentions : Rival forecasting strategies. British Journal of Political Science, 51(1), 60-67. doi:10.1017/S0007123419000061
Murr, A. E., & Lewis-Beck, M. S. (2020). Citizen forecasting 2020: A state-by-state experiment. PS: Politics & Political Science, 54(1), 91-95. https://doi.org/10.1017/S1049096520001456
Murr, A. E. (2015). The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections. International Journal of Forecasting, 31(3), 916-929. https://doi.org/10.1016/j.ijforecast.2014.12.002
Murr, A. E. (2011). A decentralised election forecasting model that uses citizens’ local expectations. Electoral Studies, 30(4), 771-783. https://doi.org/10.1016/j.electstud.2011.07.005
Ratto, M. C. & Lewis-Beck, M. S., & Bélanger, É. (2022). Forecasting elections in Latin America: An overview. Revista Latinoamericana de Opinión Pública, 11(1), 5-13.
https://revistas.usal.es/cuatro/index.php/1852-9003/article/view/29171
Rodríguez, S., Allende-Cid, H., Palma, W., Alfaro, R., González, C., Elortegui, C. and Santander, P. (2018). Forecasting the Chilean electoral year: using Twitter to predict the presidential elections of 2017. In: Gabriele Meiselwitz (Ed.), Social Computing and Social Media: Technologies and Analytics, pp. 298-314. Springer.
Ronconi, L., & Zarazaga, R. (2019). Household-based clientelism: brokers’ allocation of temporary public works programs in Argentina. Studies in Comparative International Development, 54(3), 365–380. https://doi.org/10.1007/s12116-019-09280-7
Santander, P., Elortegui, C., Gonzalez, C., Allende-Cid, H., & Palma, W. (2017). Social networks, computational intelligence, and electoral prediction: The case of the presidential primaries of Chile in 2017. Cuadernos.info, 41(1), 41-56. https://doi.org/10.7764/cdi.41.1218
Satopää, V. A., Salikhov, M., Tetlock, P. E., & Mellers, B. (2023). Decomposing the effects of crowd-wisdom aggregators: The bias-information-noise (BIN) model. International Journal of Forecasting, 39. https://doi.org/10.1016/j.ijforecast.2021.12.010
Stiers, D., & Dassonneville, R. (2018). Affect versus cognition: Wishful thinking on election day. An analysis using exit poll data from Belgium. International Journal of Forecasting, 34(2), 199-215. https://doi.org/10.1016/j.ijforecast.2017.12.001
Temporão, M., Dufresne, Y., Savoie, J., & Van der Linden, C. (2019). Crowdsourcing the vote: New horizons in citizen forecasting. International Journal of Forecasting, 35(1), 1-10. https://doi.org/10.1016/j.ijforecast.2018.07.011
Tetlock, P. E. (2005). Expert political judgment. Princeton University Press.
Turgeon, M., & Rénno, L. (2012). Forecasting Brazilian presidential elections: Solving the small-N problem. International Journal of Forecasting, 28(4), 804-812. https://doi.org/10.1016/j.ijforecast.2012.04.003
Arce, M., & Vera, S. (2022). Choosing the lesser evil: Forecasting presidential elections in Peru. Revista Latinoamericana De Opinión Pública, 11(1), 55-80. https://doi.org/10.14201/rlop.25805
Basyouni, S. (2021). Social networking sites and political knowledge: Factors that affect individuals’ knowledge acquisition from Facebook and Twitter. International Journal of Innovation, Creativity, and Change, 15(10), 259-275.
Beatriz Fernández, C., & Rodríguez-Virgili, J. (2019). Electors are from Facebook, political geeks are from Twitter: Political information consumption in Argentina, Spain, and
Venezuela. KOME, 7(1), 42-62. http://doi.org/10.17646/KOME.75698.62
Bélanger, E., & Trotter, D. (2017). Econometric approaches to forecasting. In Arzheimer, K., Evans, J. & Lewis-Beck, M. S. (Eds.), The SAGE Handbook of Electoral Behaviour (pp. 813-834). SAGE. https://doi.org/10.4135/9781473957978
Bertholini, F., Rennó, L., & Turgeon, M. (2022). Against all odds : Forecasting Brazilian elections in times of political disruption. Revista Latinoamericana de Opinión Pública, 11(1), 129-147. https://doi.org/10.14201/rlop.25882
Blais, A., & Bodet, M. A. (2006). How do voters form expectations about the parties’ chances of winning the election? Social Science Quarterly, 87(3), 477-493. https://doi.org/10.1111/j.1540-6237.2006.00392.x
Bolsen, T., Druckman, J. N., & Cook, F. L. (2014). The influence of partisan motivated reasoning on public opinion. Political Behavior, 36, 235-262. https://doi.org/10.1007/s11109-013-9238-0
Booth, A. (2022, Dec. 6). Argentina’s Cristina Fernández sentenced to six years in $1bn fraud case. The Guardian. https://www.theguardian.com/world/2022/dec/06/cristina-fernandez-de-kirchner-argentina-sentenced-prison-fraud-case
Boukes, M. (2019). Social network sites and acquiring current affairs knowledge: The impact of Twitter and Facebook usage on learning about the news. Journal of Information Technology & Politics, 16(1), 36-51. https://doi.org/10.1080/19331681.2019.1572568
Brusco, V., Nazareno, M., & Stokes, S. C. (2004). Vote buying in Argentina. Latin American Research Review, 39(2), 66-88. https://doi.org/10.1353/lar.2004.0022
Bunker, K. (2020). A two-stage model to forecast elections in new democracies. International Journal of Forecasting, 36(4), 1407-1419. https://doi.org/10.1016/j.ijforecast.2020.02.004
Cámara Nacional Electoral. (2023, October 30). Actas de escrutinio definitive – Generales 2023. Elecciones nacionales. https://www.electoral.gob.ar/nuevo/paginas/btn/actas_esc_generales2023.php
Campbell, J. E. (2000). The science of forecasting elections. In J. E. Campbell & J. C. Garand (Eds.), Before the vote: Forecasting American national elections (pp. 169-187). Sage Publications.
Campbell, J. E., & Lewis-Beck, M. S. (2008). US presidential election forecasting: An introduction. International Journal of Forecasting, 24(2), 189-192. https://doi.org/10.1016/j.ijforecast.2008.02.003
Castillo, J. G., Portillo, J. M., & Riojas, D. V. (2021). Fallaron las encuestas y los pronósticos en los resultados electorales de 2020 en Estados Unidos? Derecho Electoral, 31(31), 231-251. https://doi.org/10.35242/RDE_2021_31_12
Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of communication, 64(2), 317-332.
https://doi.org/10.1111/jcom.12084
Congosto, M. L., Fernández, M., & Esteban, M. E. (2011). Twitter y política: Información, opinion, y predicción? Cuadernos de Comunicación Evoca, 4. https://e-archivo.uc3m.es/handle/10016/21631
Cook, C. E., & Wasserman, D. (2014). Recalibrating ratings for a new normal. PS: Political Science & Politics, 47(2), 304-308. https://doi.org/10.1017/S1049096514000079
Dolan, K. A., & Holbrook, T. M. (2001). Knowing versus caring: The role of affect and cognition in political perceptions. Political Psychology, 22(1), 27-44.
Ganser, C., & Riordan, P. (2015). Vote expectations at the next level: Trying to predict vote shares in the 2013 German federal election by polling expectations. Electoral Studies, 40, 115-126. https://doi.org/10.1016/j.electstud.2015.08.001
Graefe, A. (2016). Forecasting proportional representation elections from non-representative expectation surveys. Electoral Studies, 42, 222-228. https://doi.org/10.1016/j.electstud.2016.03.001
Harrison, C. (2023, July 17). What are Argentina’s presidential primaries and who’s running? Americas Society Council of the Americas. https://www.as-coa.org/articles/what-are-argentinas-paso-presidential-primaries-and-whos-running
Herrera L. C., L., & Relmucao, J. J. (2022, April 21). Argentina 20 years after La Crisis del 2001. North American Congress on Latin America. https://nacla.org/argentina-20-years-after-la-crisis-del-2001.
Jennings, W., Lewis-Beck, M. S., & Wleizen, C. (2020). Election forecasting: Too far out? International Journal of Forecasting, 36(3), 949-962. https://doi.org/10.1016/j.ijforecast.2019.12.002
Leiter, D., Reilly, J., & Stegmaier, M. (2020). Echoing certainty in uncertain times: Network partisan agreement and the quality of citizen forecasts in the 2015 Canadian election. Electoral Studies, 63, Article 102115. https://doi.org/10.1016/j.electstud.2019.102115
Leiter, D., Murr, A., Rascon Ramirez, E., & Stegmaier, M. (2018). Social networks and election forecasting: The more friends the better. International Journal of Forecasting, 34(1), 235-248. https://doi.org/10.1016/j.ijforecast.2017.11.006
Lewis-Beck, M.S., & Bélanger, É. (2012). Election forecasting in neglected democracies: An introduction. International Journal of Forecasting, 28(4), 767-768. https://doi.org/10.1016/j.ijforecast.2012.04.006
Lewis-Beck, M. S., & Tien, C. (1999). Voters as forecasters: A micromodel of election prediction. International Journal of Forecasting, 15(2), 175-184. https://doi.org/10.1016/S0169-2070(98)00063-6
Luckner, S., Schroder, J., & Slamka, C. (2012). Prediction markets: Fundamentals, design, and applications. Gabler Verlag.
Misculin, N., Raszewski, E., & Grimberg, C. (2023, Aug. 14). Argentine far-right outsider Javier Milei posts shock win in primary election. Reuters. https://www.reuters.com/world/americas/argentina-set-primary-vote-with-ruling-peronists-fighting-survival-2023-08-13/
Mongrain, P. (2022). With a little help from my friends? The impact of social networks on citizens’ forecasting ability. European Journal of Political Research. Article 20221208. https://doi.org/10.1111/1475-6765.12576
Mongrain, P. (2021a). Did you see it coming? Explaining the accuracy of voter expectations for district and (sub)national election outcomes in multi-party systems. Electoral Studies, 71, Article 102317. https://doi.org/10.1016/j.electstud.2021.102317
Mongrain, P. (2021b). A technocratic view of election forecasting: Weighting citizens’ forecasts according to competence. International Journal of Public Opinion Research, 33(3), 713-723. https://doi.org/10.1093/ijpor/edab010
Murr, A., Stegmaier, M., & Lewis-Beck, M. S. (2021). Vote expectations versus vote intentions : Rival forecasting strategies. British Journal of Political Science, 51(1), 60-67. doi:10.1017/S0007123419000061
Murr, A. E., & Lewis-Beck, M. S. (2020). Citizen forecasting 2020: A state-by-state experiment. PS: Politics & Political Science, 54(1), 91-95. https://doi.org/10.1017/S1049096520001456
Murr, A. E. (2015). The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections. International Journal of Forecasting, 31(3), 916-929. https://doi.org/10.1016/j.ijforecast.2014.12.002
Murr, A. E. (2011). A decentralised election forecasting model that uses citizens’ local expectations. Electoral Studies, 30(4), 771-783. https://doi.org/10.1016/j.electstud.2011.07.005
Ratto, M. C. & Lewis-Beck, M. S., & Bélanger, É. (2022). Forecasting elections in Latin America: An overview. Revista Latinoamericana de Opinión Pública, 11(1), 5-13.
https://revistas.usal.es/cuatro/index.php/1852-9003/article/view/29171
Rodríguez, S., Allende-Cid, H., Palma, W., Alfaro, R., González, C., Elortegui, C. and Santander, P. (2018). Forecasting the Chilean electoral year: using Twitter to predict the presidential elections of 2017. In: Gabriele Meiselwitz (Ed.), Social Computing and Social Media: Technologies and Analytics, pp. 298-314. Springer.
Ronconi, L., & Zarazaga, R. (2019). Household-based clientelism: brokers’ allocation of temporary public works programs in Argentina. Studies in Comparative International Development, 54(3), 365–380. https://doi.org/10.1007/s12116-019-09280-7
Santander, P., Elortegui, C., Gonzalez, C., Allende-Cid, H., & Palma, W. (2017). Social networks, computational intelligence, and electoral prediction: The case of the presidential primaries of Chile in 2017. Cuadernos.info, 41(1), 41-56. https://doi.org/10.7764/cdi.41.1218
Satopää, V. A., Salikhov, M., Tetlock, P. E., & Mellers, B. (2023). Decomposing the effects of crowd-wisdom aggregators: The bias-information-noise (BIN) model. International Journal of Forecasting, 39. https://doi.org/10.1016/j.ijforecast.2021.12.010
Stiers, D., & Dassonneville, R. (2018). Affect versus cognition: Wishful thinking on election day. An analysis using exit poll data from Belgium. International Journal of Forecasting, 34(2), 199-215. https://doi.org/10.1016/j.ijforecast.2017.12.001
Temporão, M., Dufresne, Y., Savoie, J., & Van der Linden, C. (2019). Crowdsourcing the vote: New horizons in citizen forecasting. International Journal of Forecasting, 35(1), 1-10. https://doi.org/10.1016/j.ijforecast.2018.07.011
Tetlock, P. E. (2005). Expert political judgment. Princeton University Press.
Turgeon, M., & Rénno, L. (2012). Forecasting Brazilian presidential elections: Solving the small-N problem. International Journal of Forecasting, 28(4), 804-812. https://doi.org/10.1016/j.ijforecast.2012.04.003
Thompson-Collart, B., Brie, E., & Dufresne, Y. (2024). Can Latin American Voters see the Future? Citizen Forecasting in Argentina. Revista Latinoamericana De Opinión Pública, 13, e31348. https://doi.org/10.14201/rlop.31348
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