Demographic Inference and Representative Population Estimates from Multilingual Social Media Data

Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inference tools towards dominant languages and groups. While demographic attribute inference could be used to mitigate such bias, current techniques are almost entirely monolingual and fail to work in a global environment. We address these challenges by combining multilingual demographic inference with post-stratification to create a more representative population sample. To learn demographic attributes, we create a new multimodal deep neural architecture for joint classification of age, gender, and organization-status of social media users that operates in 32 languages. This method substantially outperforms current state of the art while also reducing algorithmic bias. To correct for sampling biases, we propose fully interpretable multilevel regression methods that estimate inclusion probabilities from inferred joint population counts and ground-truth population counts. In a large experiment over multilingual heterogeneous European regions, we show that our demographic inference and bias correction together allow for more accurate estimates of populations and make a significant step towards representative social sensing in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web Conference (WWW '19

Essay: Sunday shopping - the case of three surveys

"There is a growing discussion about the use of non-probability sampling in survey research. Probability sampling is the preferred method of sample selection, but practical problems like reduced data collection budgets, increasing nonresponse rates, and lack of adequate sampling frames force researchers to use different sampling methods. Particularly, online surveys based on self-selection of respondents have become very popular. Some say that use of such alternative sampling methods is not without risks as often proper inference from sample to population is not possible. Others say that non-probability sampling can produce satisfactory estimates provided effective correction techniques are applied. To obtain more insight in various sample selection methods, it would be nice to be able to compare them in practical situations. This paper describes a case in which three different surveys were carried out on the same topic, at the same time, and with the same questionnaire, but with different sample selection methods: an online panel based on probability sampling, an online survey based on self-selection, and a face-to-face survey in shopping centers. The results of these three polls differ substantially. This is a warning to be careful when choosing a sample selection method." (author's abstract

Indeterminacy problems and the interpretation of factor analysis results

Abstract  This paper reviews indeterminacy problems for the factor analysis model and their consequences for the interpretation of the results. Two types of indeterminacy are discerned: indeterminacy of the parameters in the model (the number of factors, the specific variances and the factorloadings) and the indeterminacy of the factors, given the parameters in the model. It is argued that parameter indeterminacy is partly to be overcome, provided that a strong underlying theory for the subject matter under research is present. Factor indeterminacy remains a major stumbling‐block for the interpretation of results. The Guttman criterion is advocated as a measure of factor indeterminacy. Copyrigh