329,358 research outputs found
Characterizing videos, audience and advertising in Youtube channels for kids
Online video services, messaging systems, games and social media services are
tremendously popular among young people and children in many countries. Most of
the digital services offered on the internet are advertising funded, which
makes advertising ubiquitous in children's everyday life. To understand the
impact of advertising-based digital services on children, we study the
collective behavior of users of YouTube for kids channels and present the
demographics of a large number of users. We collected data from 12,848 videos
from 17 channels in US and UK and 24 channels in Brazil. The channels in
English have been viewed more than 37 billion times. We also collected more
than 14 million comments made by users. Based on a combination of text-analysis
and face recognition tools, we show the presence of racial and gender biases in
our large sample of users. We also identify children actively using YouTube,
although the minimum age for using the service is 13 years in most countries.
We provide comparisons of user behavior among the three countries, which
represent large user populations in the global North and the global South
What Your Username Says About You
Usernames are ubiquitous on the Internet, and they are often suggestive of
user demographics. This work looks at the degree to which gender and language
can be inferred from a username alone by making use of unsupervised morphology
induction to decompose usernames into sub-units. Experimental results on the
two tasks demonstrate the effectiveness of the proposed morphological features
compared to a character n-gram baseline
A Penalty Approach to Differential Item Functioning in Rasch Models
A new diagnostic tool for the identification of differential item functioning (DIF) is proposed. Classical approaches to DIF allow to consider only few subpopulations like ethnic groups when investigating if the solution of items depends on the membership to a subpopulation. We propose an explicit model for differential item functioning that includes a set of variables, containing metric as well as categorical components, as potential candidates for inducing DIF. The ability to include a set of covariates entails that the model contains a large number of parameters. Regularized estimators, in particular penalized maximum likelihood estimators, are used
to solve the estimation problem and to identify the items that induce DIF. It is shown that the method is able to detect items with DIF. Simulations and two applications demonstrate the applicability of the method
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