Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEvaluation of Biases in Self-reported Demographic and Psychometric Information: Traditional versus Facebook-based Surveys
197
0
0.0
(
0
)
Added by
Kyriaki Kalimeri
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Social media in scientific research offer a unique digital observatory of human behaviours and hence great opportunities to conduct research at large scale answering complex sociodemographic questions. We focus on the identification and assessment of biases in social media administered surveys. This study aims to shed light on population, self-selection and behavioural biases, empirically comparing the consistency between self-reported information collected traditionally versus social media administered questionnaires, including demographic and psychometric attributes. We engaged a demographically representative cohort of young adults in Italy (approximately 4,000 participants) in taking a traditionally administered online survey and then, after one year, we invited them to use our ad hoc Facebook application (988 accepted) where they filled in part of the initial survey. We assess the statistically significant differences indicating population, self-selection, and behavioural biases due to the different context in which the questionnaire is administered. Our findings suggest that surveys administered on Facebook do not exhibit major biases with respect to traditionally administered surveys neither in terms of demographics, nor personality traits. Loyalty, authority, and social binding values were higher in the Facebook platform, probably due to the platforms intrinsic social character. We conclude, that Facebook apps are valid research tools for administering demographic and psychometric surveys provided that the entailed biases are taken into consideration. We contribute to the characterisation of Facebook apps as a valid scientific tool to administer demographic and psychometric surveys, and to the assessment of population, self-selection, and behavioural biases in the collected data.
rate research
Read More
In the global move toward urbanization, making sure the people remaining in rural areas are not left behind in terms of development and policy considerations is a priority for governments worldwide. However, it is increasingly challenging to track important statistics concerning this sparse, geographically dispersed population, resulting in a lack of reliable, up-to-date data. In this study, we examine the usefulness of the Facebook Advertising platform, which offers a digital census of over two billions of its users, in measuring potential rural-urban inequalities. We focus on Italy, a country where about 30% of the population lives in rural areas. First, we show that the population statistics that Facebook produces suffer from instability across time and incomplete coverage of sparsely populated municipalities. To overcome such limitation, we propose an alternative methodology for estimating Facebook Ads audiences that nearly triples the coverage of the rural municipalities from 19% to 55% and makes feasible fine-grained sub-population analysis. Using official national census data, we evaluate our approach and confirm known significant urban-rural divides in terms of educational attainment and income. Extending the analysis to Facebook-specific user interests and behaviors, we provide further insights on the divide, for instance, finding that rural areas show a higher interest in gambling. Notably, we find that the most predictive features of income in rural areas differ from those for urban centres, suggesting researchers need to consider a broader range of attributes when examining rural wellbeing. The findings of this study illustrate the necessity of improving existing tools and methodologies to include under-represented populations in digital demographic studies -- the failure to do so could result in misleading observations, conclusions, and most importantly, policies.
We conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers and preferences towards Asian workers female workers and more attractive workers. We also show that certain UI designs including provision of candidates information at the individual level and reducing the number of choices can significantly reduce discrimination. However provision of candidates information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.
We conduct a subjective experiment to compare the performance of traditional image coding methods and learning-based image coding methods. HEVC and VVC, the state-of-the-art traditional coding methods, are used as the representative traditional methods. The learning-based methods used contain not only CNN-based methods, but also a GAN-based method, all of which are advanced or typical. Single Stimuli (SS), which is also called Absolute Category Rating (ACR), is adopted as the methodology of the experiment to obtain perceptual quality of images. Additionally, we utilize some typical and frequently used objective quality metrics to evaluate the coding methods in the experiment as comparison. The experiment shows that CNN-based and GAN-based methods can perform better than traditional methods in low bit-rates. In high bit-rates, however, it is hard to verify whether CNN-based methods are superior to traditional methods. Because the GAN method does not provide models with high target bit-rates, we cannot exactly tell the performance of the GAN method in high bit-rates. Furthermore, some popular objective quality metrics have not shown the ability well to measure quality of images generated by learning-based coding methods, especially the GAN-based one.
Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media sites overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service Who-Makes-Trends at twitter-app.mpi-sws.org/who-makes-trends.
With increasing adoption of face recognition systems, it is important to ensure adequate performance of these technologies across demographic groups. Recently, phenotypes such as skin-tone, have been proposed as superior alternatives to traditional race categories when exploring performance differentials. However, there is little consensus regarding how to appropriately measure skin-tone in evaluations of biometric performance or in AI more broadly. In this study, we explore the relationship between face-area-lightness-measures (FALMs) estimated from images and ground-truth skin readings collected using a device designed to measure human skin. FALMs estimated from different images of the same individual varied significantly relative to ground-truth FALM. This variation was only reduced by greater control of acquisition (camera, background, and environment). Next, we compare ground-truth FALM to Fitzpatrick Skin Types (FST) categories obtained using the standard, in-person, medical survey and show FST is poorly predictive of skin-tone. Finally, we show how noisy estimation of FALM leads to errors selecting explanatory factors for demographic differentials. These results demonstrate that measures of skin-tone for biometric performance evaluations must come from objective, characterized, and controlled sources. Further, despite this being a currently practiced approach, estimating FST categories and FALMs from uncontrolled imagery does not provide an appropriate measure of skin-tone.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
