This provides an R implementation for the different poststratification models described in the Web Conference (WWW) 2019 paper titled Demographic Inference and Representative Population Estimates from Multilingual Social Media Data.

To correct for sampling biases in using social media data for downstream applications, this implementation provides interpretable multilevel regression methods to predict the population of a group (e.g., country or nuts3 region) using non-representative social media counts. The poststratification models were evaluated on publicly available ground-truth census data and inferred joint population counts from Twitter using [M3-inference tool] (https://github.com/euagendas/m3inference)

Using the best performing poststratification model, we provide a code to compute the inclusion probability of an individual from a group with given demographics (e.g., age: 18-29, gender: Female) to be on a given social media platform.

The Python notebooks are used to preprocess the dataset required for the debiasing code in R. They are:

• nuts3_data_prep.ipynb : this preprocessing is when M3 inference distinguishes between organizational and non-organizational Twitter accounts
• nuts3_data_prep.orgs-as-humans.ipynb : this preprocessing is when M3 inference treats organizational accounts as humans/personal accounts

The debiasing models using R syntax are:

formular <- 'census ~ twitter + (twitter+0|country)'

formular <- 'census ~ gender_F + gender_M + (0+gender_F |country) + (0+gender_M |country)'

formular <- 'census ~ age_0017 + age_1829 + age_3039 + age_4099 + (0+age_0017 |country) + (0+age_1829 |country) + (0+age_3039 |country) + (0+age_4099 |country)'

formular <- 'census ~ tw_0017F + tw_1829F + tw_3039F + tw_4099F + tw_0017M + tw_1829M + tw_3039M + tw_4099M + (0+tw_0017F |country) + (0+tw_1829F |country) + (0+tw_3039F |country) + (0+tw_4099F |country) + (0+tw_0017M |country) + (0+tw_1829M |country) + (0+tw_3039M |country) + (0+tw_4099M |country) '

formular <- 'census ~ twitter + age+gender + (0+twitter |country) + (0+age+gender|country)'

We evaluate the debiasing models using mean abssolute percentage error in the following leave-one-group cross-validation settings:

• leave one region out: debias_twitter_leave_one_region_CV.R
• leave one country out (i.e., leave out all regions from a given country) : debias_twitter_leave_one_country_CV.R
• and leave one stratum out (e.g.,leave out only females aged 18-29): debias_twitter_leave_one_stratum_CV.R

For the leave-one-region out cross-validation, we provided two implementation:

• Treating organizational accounts as humans: debias_twitter_leave_one_region_CV_org_as_human.R
• Ignoring organization account counts: debias_twitter_leave_one_region_CV.R

To compute the inclusion probabilities for each nuts3 region following equation 2 in the paper, run compute_inclusion_probabilities_by_region.R

Please cite our WWW 2019 paper if you use these scripts in your project.

@inproceedings{wang2019demographic,
  title={Demographic Inference and Representative Population Estimates from Multilingual Social Media Data},
  author={Wang, Zijian and Hale, Scott A. and Adelani, David and Grabowicz, Przemyslaw A. and Hartmann, Timo and Fl{\"o"}ck, Fabian and Jurgens, David},
  booktitle={Proceedings of the 2019 World Wide Web Conference},
  year={2019},
  organization={ACM}
}

We use issues on this GitHub for all questions or suggestions. For specific inqueries, please contact us as hello@euagendas.org. Please note that we are unable to release or provide training data for this model due to legal restrictions.

This source code is licensed under the GNU Affero General Public License, which allows for non-commercial re-use of this software. For commercial inqueries, please contact us directly. Please see the LICENSE file in the root directory of this source tree for details.