Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Demographics in Social Media Data for Public Health Research: Does it
  matter?   Social media data provides propitious opportunities for public health
research. However, studies suggest that disparities may exist in the
representation of certain populations (e.g., people of lower socioeconomic
status). To quantify and address these disparities in population
representation, we need demographic information, which is usually missing from
most social media platforms. Here, we propose an ensemble approach for
inferring demographics from social media data.
  Several methods have been proposed for inferring demographic attributes such
as, age, gender and race/ethnicity. However, most of these methods require
large volumes of data, which makes their application to large scale studies
challenging. We develop a scalable approach that relies only on user names to
predict gender. We develop three separate classifiers trained on data
containing the gender labels of 7,953 Twitter users from Kaggle.com. Next, we
combine predictions from the individual classifiers using a stacked
generalization technique and apply the ensemble classifier to a dataset of
36,085 geotagged foodborne illness related tweets from the United States.
  Our ensemble approach achieves an accuracy, precision, recall, and F1 score
of 0.828, 0.851, 0.852 and 0.837, respectively, higher than the individual
machine learning approaches. The ensemble classifier also covers any user with
an alphanumeric name, while the data matching approach, which achieves an
accuracy of 0.917, only covers 67% of users. Application of our method to
reports of foodborne illness in the United States highlights disparities in
tweeting by gender and shows that counties with a high volume of
foodborne-illness related tweets are heavily overrepresented by female Twitter
users.

20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin