Question: What is the main reason that causes the high false positive rate in word-based approaches?
Evidence: Simple word-based approaches, if used for blocking the posting of text or blacklisting users, not only fail to identify subtle offensive content, but they also affect the freedom of speech and expression. The word ambiguity problem – that is, a word can have different meanings in different contexts – is mainly responsible for the high false positive rate in such approaches. Ordinary NLP approaches on the other hand, are ineffective to detect unusual spelling, experienced in user-generated comment text. This is best known as the spelling variation problem, and it is caused either by unintentional or intentional replacement of single characters in a token, aiming to obfuscate the detectors.
In general, the complexity of the natural language constructs renders the task quite challenging. The employment of supervised learning classification methods for hate speech detection is not new. BIBREF6 reported performance for a simple LSTM classifier not better than an ordinary SVM, when evaluated on a small sample of Facebook data for only 2 classes (Hate, No-Hate), and 3 different levels of strength of hatred. BIBREF7 described another way of detecting offensive language in tweets, based on some supervised model. They differentiate hate speech from offensive language, using a classifier that involves naive Bayes, decision trees and SVM. Also, BIBREF8 attempted to discern abusive content with a supervised model combining various linguistic and syntactic features in the text, considered at character uni-gram and bi-gram level, and tested on Amazon data. In general, we can point out the main weaknesses of NLP-based models in their non-language agnostic nature and the low scores in detection.
Unsupervised learning approaches are quite common for detecting offensive messages in text by applying concepts from NLP to exploit the lexical syntactic features of sentences BIBREF9 , or using AI-solutions and bag-of-words based text-representations BIBREF10 . The latter is known to be less effective for automatic detection, since hatred users apply various obfuscation tricks, such as replacing a single character in offensive words. For instance, applying a binary classifier onto a paragraph2vec representation of words has already been attempted on Amazon data in the past BIBREF11 , but it only performed well on a binary classification problem. Another unsupervised learning based solution is the work by BIBREF12 , in which the authors proposed a set of criteria that a tweet should exhibit in order to be classified as offensive. They also showed that differences in geographic distribution of users have only marginal effect on the detection performance. Despite the above observation, we explore other features that might be possible to improve the detection accuracy in the solution outlined below.
The work by BIBREF5 applied a crowd-sourced solution to tackle hate-speech, with the creation of an additional dataset of annotations to extend the existing corpus. The impact of the experience of annotators in the classification performance was investigated. The work by BIBREF13 dealt with the classification problem of tweets, but their interest was on sexism alone, which they distinguished into `Hostile', `Benevolent' or `Other'. While the authors used the dataset of tweets from BIBREF12 , they treated the existing `Sexism' tweets as being of class `Hostile', while they collected their own tweets for the `Benevolent' class, on which they finally applied the FastText by BIBREF14 , and SVM classification.
 BIBREF15 approached the issue with a supervised learning model that is based on a neural network. Their method achieved higher score over the same dataset of tweets than any unsupervised learning solution known so far. That solution uses an LSTM model, with features extracted by character n-grams, and assisted by Gradient Boosted Decision Trees. Convolution Neural Networks (CNN) has also been explored as a potential solution in the hate-speech problem in tweets, with character n-grams and word2vec pre-trained vectors being the main tools. For example, BIBREF16 transformed the classification into a 2-step problem, where abusive text first is distinguished from the non-abusive, and then the class of abuse (Sexism or Racism) is determined. BIBREF17 employed pre-trained CNN vectors in an effort to predict four classes. They achieved slightly higher F-score than character n-grams.
In spite of the high popularity of NLP approaches in hate-speech classification BIBREF3 , we believe there is still a high potential for deep learning models to further contribute to the issue. At this point it is also relevant to note the inherent difficulty of the challenge itself, which can be clearly noted by the fact that no solution thus far has been able to obtain an F-score above 0.93.
Simple word-based approaches, if used for blocking the posting of text or blacklisting users, not only fail to identify subtle offensive content, but they also affect the freedom of speech and expression. The word ambiguity problem – that is, a word can have different meanings in different contexts – is mainly responsible for the high false positive rate in such approaches.
Answer: The word ambiguity problem – that is, a word can have different meanings in different contexts.