#Manuscript speech advantages and disadvantages manual
This is a practical concern because systems that automatically censor a person’s speech likely need a manual appeal process. One limitation of these approaches is that the decisions they make can be opaque and difficult for humans to interpret why the decision was made. The proposed solutions employ machine learning techniques to classify text as hate speech. Nuance and subtleties in language provide further challenges in automatic hate speech identification, again depending on the definition.ĭespite differences, some recent approaches found promising results for detecting hate speech in textual content. We discuss the various datasets available to train and measure the performance of hate speech detection systems in the next section.
This can make it difficult to directly access which aspects of hate speech to identify. Our aim is simply to illustrate variances highlighting difficulties that arise from such.Ĭompeting definitions provide challenges for evaluation of hate speech detection systems existing datasets differ in their definition of hate speech, leading to datastets that are not only from different sources, but also capture different information. We are by no means, nor can we be, comprehensive as new definitions appear regularly. We start by covering competing definitions, focusing on the different aspects that contribute to hate speech. This means that some content can be considered hate speech to some and not to others, based on their respective definitions. First, there are disagreements in how hate speech should be defined. By automating its detection, the spread of hateful content can be reduced.ĭetecting hate speech is a challenging task, however. Due to the societal concern and how widespread hate speech is becoming on the Internet, there is strong motivation to study automatic detection of hate speech. As such, many online forums such as Facebook, YouTube, and Twitter consider hate speech harmful, and have policies to remove hate speech content.
For instance, The American Bar Association asserts that in the United States, hate speech is legal and protected by the First Amendment, although not if it directly calls for violence. While the ability to freely express oneself is a human right that should be cherished, inducing and spreading hate towards another group is an abuse of this liberty. Vast online communication forums, including social media, enable users to express themselves freely, at times, anonymously. In some cases, social media can play an even more direct role video footage from the suspect of the 2019 terror attack in Christchurch, New Zealand, was broadcast live on Facebook. For instance, suspects in several recent hate-related terror attacks had an extensive social media history of hate-related posts, suggesting that social media contributes to their radicalization.
However, social media and other means of online communication have begun playing a larger role in hate crimes. Hate crimes are unfortunately nothing new in society. įunding: The author(s) received no specific funding for this work.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the manuscript, its Supporting Information files, and the provided data links as follows. Received: ApAccepted: JPublished: August 20, 2019Ĭopyright: © 2019 MacAvaney et al.
Citation: MacAvaney S, Yao H-R, Yang E, Russell K, Goharian N, Frieder O (2019) Hate speech detection: Challenges and solutions.
0 kommentar(er)
