Sentence examples for classification of cyber from inspiring English sources

To better understand how this is improving the classification of cyber hate for individual protected characteristics, Table 4 shows the performance for the individual classes using the same combined single dataset but retaining the separate class labels.

This combination of linguistics and sociology potentially provides a very interesting set of features for the more nuanced classification of cyber hate, beyond the BOW approach that utilizes expletives and derogatory terms.

To summarise this experiment, we have produced evidence to suggest the inclusion of typed dependencies as features in the classification of cyber hate reduced false positive rate in the classification of 2 out of 3 types of hate speech - race and sexual orientation - when compared to using a BOW model and/or hateful terms as features.

This is a significant improvement for the classification of racial cyber hate and suggests the typed dependency inclusion is necessary for improving classifier performance.

The key finding from previous research was that the inclusion of typed dependencies in the classification of religious cyber hate reduced the false negative rate by 7% when compared to using hateful terms alone [1, 2].

The first set of results document the findings of applying machine classification to cyber hate directed towards each protected characteristic individually, based on disability, race, and sexual orientation.

That is, features that contribute highly to the classification of each type of cyber hate.

In this instance, the blended model is shown to improve classification performance for instances of cyber hate in an intertextual context.

We used text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture 'othering' language - consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms, which have been the main method for identifying cyber hate previously.

We use text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture 'othering' language - consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms.

Fig. 2 Classification and requirements of cyber-physical attacks in NCSs.