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Hatebase

Hatebase is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Hatebase rather than just read about it. In short: Hatebase is a joint project of the Sentinel Project for Genocide Prevention and the Dark Data Project that is described on its website as an "online repository of structured, multilingual, usage-based hate speech". It uses text analysis of speech and written content (including radio transcripts, transcripts of spoken web content, tweets, and articles) and identification of hate speech patterns within it to predict p…

Key takeaways

  • Hatebase belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Hatebase to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Hatebase from memory before moving on to harder problems.

Reference excerpt

Hatebase is a joint project of the Sentinel Project for Genocide Prevention and the Dark Data Project that is described on its website as an "online repository of structured, multilingual, usage-based hate speech". It uses text analysis of speech and written content (including radio transcripts, transcripts of spoken web content, tweets, and articles) and identification of hate speech patterns within it to predict potential regional violence.

History The introduction of Hatebase was announced on the Sentinel Project blog on March 25, 2013. The initiative is led by Timothy Quinn of the Dark Data Project.

Description In an article for Foreign Policy, Joshua Keating described Hatebase as follows: "There are two main features to Hatebase. The first is a Wikipedia-like interface which allows users to identify hate speech terms by region and the group they refer to. This could have some value for researchers, but Hatebase's developers are especially excited by the second main feature, which allows users to identify instances when they've heard these terms used." The example of the Rwandan genocide was cited in that article and also in an article about Hatebase on Maclean's: in the months leading up to the genocide, radio stations attempted to dehumanize Tutsis to Hutus by repeatedly referring to the Tutsis as cockroaches. The regional and multilingual focus of the site was deemed particularly useful for identifying words that could be construed as hate in some languages and contexts but that outsiders would not know of, such as the word "sakkiliya" in Sinhalese (the language in Sri Lanka) used to refer to a Tamil person as 'a very unhygienic or uncultured person' or the reference to Tutsis as cockroaches by the Rwandan radio stations, that an outsider may simply consider evidence that the region was suffering from a literal cockroach infestation. This relates to the challenge of identifying subtly different uses of the same or similar words, one of which connotes hate and the other doesn't. In the context of language that equates humans with pollution or stains, this is also called the human stain problem. Another related challenge is to control for the ambient level of casual hate speech in society (such as YouTube comments): in some societies and contexts, hateful language may not be accompanied by or followed by violence, whereas in others, it might. For this reason, the evidence was only considered valuable in conjunction with other evidence about the risk and threat of violence, and the project concentrated its efforts on mapping hate speech in regions with a history of violence.

API Hatebase provided an Application programming interface, which is now retired, and a PHP wrapper/SDK is available on GitHub. Information about the API can be found at Programmable Web and Mashape.

Reception The launch of Hatebase was covered in Wired Magazine and the story was picked up and discussed on Slashdot. Hatebase was also covered in Metro News, a Canadian publication. It was also covered in the Canadian weekly Maclean's. Joshua Keating covered Hatebase in an article for Foreign Policy. A week later, the magazine published a response letter by Gwyneth Sutherlin, a doctoral candidate at the University of Bradford, pointing out potential problems and limitations of the approach used by Hatebase. On September 10, 2019, TechCrunch published a feature about Hatebase called "Hatebase catalogues the world’s hate speech in real time so you don’t have to".

References

External links Official website

Worked examples

Example 1 — a first encounter with Hatebase

Start with the simplest possible case. Write down what Hatebase claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Hatebase before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Hatebase ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Hatebase

In research
Hatebase appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Hatebase in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Hatebase is common in secondary-school and first-year university syllabi. It links to neighbouring topics Genocide prevention, Hate speech, so understanding it makes those chapters shorter.
In everyday life
Look for Hatebase outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Hatebase in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Hatebase means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Hatebase out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Hatebase in simple terms?

Hatebase is a joint project of the Sentinel Project for Genocide Prevention and the Dark Data Project that is described on its website as an "online repository of structured, multilingual, usage-based hate speech". It uses text analysis of speech and written content (including radio transcripts, tr…

Why does Hatebase matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Hatebase?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Hatebase.

Tags

  • Genocide prevention
  • Hate speech

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