Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cyberbullying Detection -- Technical Report 2/2018, Department of
  Computer Science AGH, University of Science and Technology   The research described in this paper concerns automatic cyberbullying
detection in social media. There are two goals to achieve: building a gold
standard cyberbullying detection dataset and measuring the performance of the
Samurai cyberbullying detection system. The Formspring dataset provided in a
Kaggle competition was re-annotated as a part of the research. The annotation
procedure is described in detail and, unlike many other recent data annotation
initiatives, does not use Mechanical Turk for finding people willing to perform
the annotation. The new annotation compared to the old one seems to be more
coherent since all tested cyberbullying detection system performed better on
the former. The performance of the Samurai system is compared with 5 commercial
systems and one well-known machine learning algorithm, used for classifying
textual content, namely Fasttext. It turns out that Samurai scores the best in
all measures (accuracy, precision and recall), while Fasttext is the
second-best performing algorithm.

13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph