Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do we really need to catch them all? A new User-guided Social Media
  Crawling method   With the growing use of popular social media services like Facebook and
Twitter it is challenging to collect all content from the networks without
access to the core infrastructure or paying for it. Thus, if all content cannot
be collected one must consider which data are of most importance. In this work
we present a novel User-guided Social Media Crawling method (USMC) that is able
to collect data from social media, utilizing the wisdom of the crowd to decide
the order in which user generated content should be collected to cover as many
user interactions as possible. USMC is validated by crawling 160 public
Facebook pages, containing content from 368 million users including 1.3 billion
interactions, and it is compared with two other crawling methods. The results
show that it is possible to cover approximately 75% of the interactions on a
Facebook page by sampling just 20% of its posts, and at the same time reduce
the crawling time by 53%. In addition, the social network constructed from the
20% sample contains more than 75% of the users and edges compared to the social
network created from all posts, and it has similar degree distribution.

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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
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The categories of this paper are: q-fin
18
The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
3
The categories of this paper are: physics