Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparative Studies of Detecting Abusive Language on Twitter   The context-dependent nature of online aggression makes annotating large
collections of data extremely difficult. Previously studied datasets in abusive
language detection have been insufficient in size to efficiently train deep
learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much
greater in size and reliability, has been released. However, this dataset has
not been comprehensively studied to its potential. In this paper, we conduct
the first comparative study of various learning models on Hate and Abusive
Speech on Twitter, and discuss the possibility of using additional features and
context data for improvements. Experimental results show that bidirectional GRU
networks trained on word-level features, with Latent Topic Clustering modules,
is the most accurate model scoring 0.805 F1.

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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci