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Deep Detection of Hate Speech in Text Through a Two-Pronged Approach

Meyer, Johannes Skjeggestad
Master thesis
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http://hdl.handle.net/11250/2568080
Utgivelsesdato
2018
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  • Institutt for datateknologi og informatikk [3870]
Sammendrag
With the widespread use of online services like Facebook and Twitter, disseminating

hateful messages has become a simple matter. These messages not only

spoil the experience for other users of a service. There is also an increasing legal

pressure for the services to prevent and remove such hate-spreading content. For

this to be practically feasible, there is a need for systems that can automatically

detect hate speech in text.

Research on automatic detection of hateful and abusive language has been an

ongoing project over the last 20 years. However, the state-of-the-art is still not

good enough to be practically usable for identifying hate speech in a fully automatic

manner. Thus, this thesis continues the efforts to reach that goal.

With the increasing legal pressure to remove hate speech, and the multitude of

services and platforms this pressure applies to, detection approaches are needed

that do not depend on any information specific to a given platform. This is so

that the approach can be used across several different platforms without being

changed. For instance, the information stored about the text s author may differ

between services, and so using such data would reduce the general applicability

of the system. Therefore, the research in this thesis aims at avoiding any such

information, using exclusively text-based input in the detection.

This thesis proposes a novel, Deep Learning-based approach to hate speech detection,

using a two-pronged architecture that combines both Convolutional Neural

Networks and Long Short-Term Memory-networks. The proposed architecture uses

Character N-grams and Word Embeddings as inputs to its two prongs, which then

merge and produce a final classification. The experiments show that this architecture,

using its optimal configurations, performs better than most state-of-the-art

systems.
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