Question: What is the difference between the authors' datasets and existing hate speech datasets?
Evidence: The growing popularity of online interactions through social media has been shown to have both positive and negative impacts. While social media improves information sharing, it also facilitates the propagation of online harassment, including hate speech. These negative experiences can have a measurable negative impact on users. Recently, the Pew Research Center BIBREF0 reported that “roughly four-in-ten Americans have personally experienced online harassment, and 63% consider it a major problem.”
To address the growing problem of online hate, an extensive body of work has focused on developing automatic hate speech detection models and datasets BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. However, simply detecting and blocking hate speech or suspicious users often has limited ability to prevent these users from simply turning to other social media platforms to continue to engage in hate speech as can be seen in the large move of individuals blocked from Twitter to Gab BIBREF9. What's more, such a strategy is often at odds with the concept of free speech. As reported by the Pew Research Center BIBREF0, “Despite this broad concern over online harassment, 45% of Americans say it is more important to let people speak their minds freely online; a slightly larger share (53%) feels that it is more important for people to feel welcome and safe online.” The special rapporteurs representing the Office of the United Nations High Commissioner for Human Rights (OHCHR) have recommended that “The strategic response to hate speech is more speech.” BIBREF10 They encourage to change what people think instead of merely changing what they do, so they advocate more speech that educates about cultural differences, diversity, and minorities as a better strategy to counter hate speech.
Therefore, in order to encourage strategies of countering online hate speech, we propose a novel task of generative hate speech intervention and introduce two new datasets for this task. Figure FIGREF5 illustrates the task. Our datasets consist of 5K conversations retrieved from Reddit and 12k conversations retrieved from Gab. Distinct from existing hate speech datasets, our datasets retain their conversational context and introduce human-written intervention responses. The conversational context and intervention responses are critical in order to build generative models to automatically mitigate the spread of these types of conversations.
To summarize, our contributions are three-fold:
We introduce the generative hate speech intervention task and provide two fully-labeled hate speech datasets with human-written intervention responses.
Our data is collected in the form of conversations, providing better context.
The two data sources, Gab and Reddit, are not well studied for hate speech. Our datasets fill this gap.
Due to our data collecting strategy, all the posts in our datasets are manually labeled as hate or non-hate speech by Mechanical Turk workers, so they can also be used for the hate speech detection task. The performance of commonly-used classifiers on our datasets is shown in Section SECREF6.
Distinct from existing hate speech datasets, our datasets retain their conversational context and introduce human-written intervention responses.
Answer: Distinct from existing hate speech datasets, the authors retain their conversational context and introduce human-written intervention responses.