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17 results on '"Polimis, Kivan"'

1. Combining social media and survey data to nowcast migrant stocks in the United States

Author

Alexander, Monica, Polimis, Kivan, and Zagheni, Emilio

Subjects
Statistics - Applications
Abstract

Measuring and forecasting migration patterns, and how they change over time, has important implications for understanding broader population trends, for designing policy effectively and for allocating resources. However, data on migration and mobility are often lacking, and those that do exist are not available in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more traditional data sources. Facebook, for example, can be thought of as a large digital census that is regularly updated. However, its users are not representative of the underlying population. This paper proposes a statistical framework to combine social media data with traditional survey data to produce timely `nowcasts' of migrant stocks by state in the United States. The model incorporates bias adjustment of the Facebook data, and a pooled principal component time series approach, to account for correlations across age, time and space. We illustrate the results for migrants from Mexico, India and Germany, and show that the model outperforms alternatives that rely solely on either social media or survey data.

Published
2020
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2. Measuring the predictability of life outcomes with a scientific mass collaboration

Author

Salganik, Matthew J, Lundberg, Ian, Kindel, Alexander T, Ahearn, Caitlin E, Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M, Brand, Jennie E, Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J, Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C, Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E, Roberts, Claudia V, Stanescu, Diana M, Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H, Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B, Argyle, Lisa P, Baer-Bositis, Livia, Büchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagné, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P, Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M, Hummel, Lisa M, Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, EH, Leizman, Ben, Liu, Naijia, Möser, Malte, Mack, Andrew E, Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M, Porter, Ethan, Porter, Kristin E, Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D, Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K, Woon, Wei Lee, Wu, James, Wu, Catherine, and Yang, Kengran

Subjects
Pediatric, Behavioral and Social Science, Generic health relevance, Adolescent, Child, Child, Preschool, Cohort Studies, Family, Female, Humans, Infant, Life, Machine Learning, Male, Predictive Value of Tests, Social Sciences, life course, prediction, machine learning, mass collaboration
Abstract

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Published
2020

7. The impact of Hurricane Maria on out-migration from Puerto Rico: Evidence from Facebook data

Author

Zagheni, Emilio, Alexander, Monica, and Polimis, Kivan

Subjects
bepress|Social and Behavioral Sciences|Social Statistics, bepress|Social and Behavioral Sciences, SocArXiv|Arts and Humanities, SocArXiv|Social and Behavioral Sciences, bepress|Arts and Humanities, geographic locations, SocArXiv|Social and Behavioral Sciences|Social Statistics
Abstract

Natural disasters such as hurricanes can cause substantial population out-migration. However, the magnitude of population movements is difficult to estimate using only traditional sources of migration data. We utilize data obtained from Facebook's advertising platform to estimate out-migration from Puerto Rico in the months after Hurricane Maria. We find evidence to indicate a 17.0% increase in the number of Puerto Rican migrants present in the US over the period October 2017 to January 2018. States with the biggest increases were Florida, New York and Pennsylvania, and there were disproportionately larger increases in the 15-30 age groups and for men compared to women. Additionally, we find evidence of subsequent return migration to Puerto Rico over the period January 2018 to March 2018. These results illustrate the power of complementing social media and traditional data to monitor demographic indicators over time, particularly after a shock, such as a natural disaster, to understand large changes in population characteristics.

Published
2019
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8. Humans in the Loop: Incorporating Expert and Crowd-sourced Knowledge for Predictions Using Survey Data

Author

Filippova, Anna, Gilroy, Connor, Kashyap, Ridhi, Kirchner, Antje, Morgan, Allison C, Polimis, Kivan, Usmani, Adaner, Wang, Tong, Filippova, Anna, Gilroy, Connor, Kashyap, Ridhi, Kirchner, Antje, Morgan, Allison C, Polimis, Kivan, Usmani, Adaner, and Wang, Tong

Abstract

Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a “human-in-the-loop” approach in the Fragile Families Challenge. The authors use surveys to elicit knowledge from experts and laypeople about the importance of different variables to different outcomes. This strategy offers the option to subset the data before prediction or to incorporate human knowledge as scores in prediction models, or both together. The authors find that human intervention is not obviously helpful. Human-informed subsetting reduces predictive performance, and considered alone, approaches incorporating scores perform marginally worse than approaches that do not. However, incorporating human knowledge may still improve predictive performance, and future research should consider new ways of doing so.

Published
2019

12. Junk in the trunk: evaluating the relationship between body-mass index and interracial dating for white adolescent females

Author

Polimis, Kivan

Abstract

Increasing racial diversity in the United States coupled with changing cultural norms toward interracial dating fuels the growth in interracial couples. The purpose of this paper is to examine the factors that influence heterosexual interracial adolescent dating, specifically the link between an adolescent's body mass index (BMI) and the likelihood of that individual entering an interracial relationship. I hypothesize that white adolescent women with BMI levels that align with the body type preferences of black men will date interracially at higher rates than their peers with BMI levels that are not consistent with preferences common among black men. Using data from the National Longitudinal Survey of Adolescent Health (Add Health), I estimate the effects of BMI on the likelihood of an individual entering an interracial relationship through binomial logistic regression analysis. I find that for white female adolescents, as BMI increases the likelihood of dating black men increases as well.

Published
2012
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14. "Adolescent Experiences and Adult Neighborhood Attainment".

Author

Marsh, Kris, Polimis, Kivan, and Crowder, Kyle

Abstract

This study combines data from the 1968-2009 waves of the Panel Study of Income Dynamics (PSID) with tract-level decennial census data to explore hypotheses regarding the role of adolescent neighborhood experiences in shaping racial differences in adult residential attainment for non-Hispanic black and white householders. Theories of racial differences in adult residential location decisions implicate a combination of economic constraints, neighborhood preferences, discrimination and knowledge of residential options. Using a life course perspective, the authors examine how these theories explain differences in adult residential contexts and how adolescent residential histories develop throughout adulthood and mold adult neighborhood context. Our findings indicate that a substantial share of the variation in adult neighborhood racial context can be explained by adolescent neighborhood experience although several sociodemographic, life course and socio-economic characteristics are important moderating factors. We discuss the development of racial differences in residential trajectories and the implications for these patterns on residential segregation. We conclude that focusing on current-time conditions in research on residential segregation misses a key source of racial differences in residential attainment--differences in adolescent neighborhood context. [ABSTRACT FROM AUTHOR]

Published
2013

15. Measuring the predictability of life outcomes with a scientific mass collaboration

Author

Salganik, Matthew J., Lundberg, Ian, Kindel, Alexander T., Ahearn, Caitlin E., Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M., Brand, Jennie E., Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J., Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C., Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E., Roberts, Claudia V., Stanescu, Diana M., Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H., Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B., Argyle, Lisa P., Baer-Bositis, Livia, Büchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagné, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P., Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M., Hummel, Lisa M., Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, E. H., Leizman, Ben, Liu, Naijia, Möser, Malte, Mack, Andrew E., Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M., Porter, Ethan, Porter, Kristin E., Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D., Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K., Woon, Wei Lee, Wu, James, Wu, Catherine, Yang, Kengran, Yin, Jingwen, Zhao, Bingyu, Zhu, Chenyun, Brooks-Gunn, Jeanne, Engelhardt, Barbara E., Hardt, Moritz, Knox, Dean, Levy, Karen, Narayanan, Arvind, Stewart, Brandon M., Watts, Duncan J., McLanahan, Sara, Salganik, Matthew J., Lundberg, Ian, Kindel, Alexander T., Ahearn, Caitlin E., Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M., Brand, Jennie E., Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J., Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C., Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E., Roberts, Claudia V., Stanescu, Diana M., Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H., Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B., Argyle, Lisa P., Baer-Bositis, Livia, Büchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagné, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P., Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M., Hummel, Lisa M., Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, E. H., Leizman, Ben, Liu, Naijia, Möser, Malte, Mack, Andrew E., Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M., Porter, Ethan, Porter, Kristin E., Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D., Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K., Woon, Wei Lee, Wu, James, Wu, Catherine, Yang, Kengran, Yin, Jingwen, Zhao, Bingyu, Zhu, Chenyun, Brooks-Gunn, Jeanne, Engelhardt, Barbara E., Hardt, Moritz, Knox, Dean, Levy, Karen, Narayanan, Arvind, Stewart, Brandon M., Watts, Duncan J., and McLanahan, Sara

Abstract

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

16. Junk in the Trunk: Evaluating the Relationship between BMI and Interracial Dating for White Adolescent Females.

Author

Polimis, Kivan

Subjects
BODY mass index, ANTHROPOMETRY, HUMAN body composition, LOGISTIC regression analysis
Abstract

Growing racial diversity in the United States coupled with changing cultural norms toward interracial dating has produced more interracial couples (Root 2001). Past research on interracial dating is largely concerned with adult dating behavior and has focused on investigating the linkages between racial attitudes and the propensity to date interracially. In the larger dating literature, researchers have documented the role of body type preferences as a contributing factor to relationship formation but the role of body type preferences has garnered little attention in interracial dating literature. Furthermore, adolescents maturing in this more accepting society have started to date interracially as well and little research has focused on the factors that influence interracial dating for adolescents. This paper examines the factors that influence interracial adolescent dating, specifically the link between an adolescent's body type as measured by Body Mass Index (henceforth "BMI") and the likelihood of that individual entering an interracial relationship. I predict that white adolescent girls with levels of BMI that align with the body type preferences of minority men will enter into interracial relationships at higher rates than their peers with levels of BMI that are not consistent with minority male preferences. Using data from the National Longitudinal Survey of Adolescent Health (Add Health), I estimate the effects of BMI on the likelihood of an individual entering an interracial relationship through binomial logistic regression analysis. I find that for white female adolescents, as BMI increases the likelihood of being involved in an interracial relationship increases. [ABSTRACT FROM AUTHOR]

Published
2011

17. Humans in the Loop: Incorporating Expert and Crowd-Sourced Knowledge for Predictions Using Survey Data.

Author

Filippova A, Gilroy C, Kashyap R, Kirchner A, Morgan AC, Polimis K, Usmani A, and Wang T

Abstract

Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a "human-in-the-loop" approach in the Fragile Families Challenge. The authors use surveys to elicit knowledge from experts and laypeople about the importance of different variables to different outcomes. This strategy offers the option to subset the data before prediction or to incorporate human knowledge as scores in prediction models, or both together. The authors find that human intervention is not obviously helpful. Human-informed subsetting reduces predictive performance, and considered alone, approaches incorporating scores perform marginally worse than approaches that do not. However, incorporating human knowledge may still improve predictive performance, and future research should consider new ways of doing so.

Published
2019
Full Text
View/download PDF

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