Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning for Hate Speech Detection in Social Media   In today's society more and more people are connected to the Internet, and
its information and communication technologies have become an essential part of
our everyday life. Unfortunately, the flip side of this increased connectivity
to social media and other online contents is cyber-bullying and -hatred, among
other harmful and anti-social behaviors. Models based on machine learning and
natural language processing provide a way to detect this hate speech in web
text in order to make discussion forums and other media and platforms safer.
The main difficulty, however, is annotating a sufficiently large number of
examples to train these models. In this paper, we report on developing
automated text analytics methods, capable of jointly learning a single
representation of hate from several smaller, unrelated data sets. We train and
test our methods on the total of $37,520$ English tweets that have been
annotated for differentiating harmless messages from racist or sexists contexts
in the first detection task, and hateful or offensive contents in the second
detection task. Our most sophisticated method combines a deep neural network
architecture with transfer learning. It is capable of creating word and
sentence embeddings that are specific to these tasks while also embedding the
meaning of generic hate speech. Its prediction correctness is the
macro-averaged F1 of $78\%$ and $72\%$ in the first and second task,
respectively. This method enables generating an interpretable two-dimensional
text visualization --- called the Map of Hate --- that is capable of separating
different types of hate speech and explaining what makes text harmful. These
methods and insights hold a potential for not only safer social media, but also
reduced need to expose human moderators and annotators to distressing
online~messaging.

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The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex