Machine learning as a way to deal with online hate?
In the era of ubiquitous hate and more and more often organized social campaigns to combat it (e.g. regarding beauty hate, organized by the Rimmel brand, HejtStop or Stop Funding Hate), it is natural that some position is also expected from important and influential personalities or big companies. Therefore, Google decided to react and decided to launch a plugin for Chrome called Tune, although for now only in the test version.
Although the name might suggest a music-related browser extension, the connotations are quite different. Tune comes from an English expression tune outi.e. ignore, pay no attention to something. The purpose of the add-on is therefore to clean our feed on selected platforms from vulgar, pointless and hateful comments. We can try out this feature on YouTube, Facebook, Twitter, Reddit and Disqus.
The add-on filters comments on several levels of toxic behavior:
- profane – Concerns profanity and profanity
- insults – concerns offending and insulting others,
- threats – concerns threats, violence, harassment,
- attack on identity – applies to negative or hateful comments directed at someone because of their origin, different appearance, etc.,
- sexually explicit – concerns lewd comments, vulgar references to sexual acts and private parts of the body.
We can choose one of seven levels of comment filtering, which range from “hide it all”, i.e. hiding all comments, through “medium”, which removes the more controversial ones from view, to “show it all”, which ensures that the entire section is visible .
Tune was created on the basis of machine learning, and in addition to controlling comments, it is also to be an example of using this method in practice – in this case it is to work as a moderator of conversations between people. At the moment, it only supports English, although when we checked how it would behave in the case of comments in Polish, it also tried to moderate them – unfortunately with poor results. Below is a screenshot of how the sorting of comments under one of the YouTube videos looked like. Although both are positive, one has been hidden (always indicated by a purple dot), which is unjustified in this case.
It all boils down to the fact that the plugin is only a specially constructed mechanism, and although it is undoubtedly very extensive and surprising with intuition, there is little chance that it will understand the irony or match someone’s words to the appropriate context. The assumptions for creating Tune are noble, while the machine learning sector probably needs several more years to be able to afford a full-fledged combination of software with the human mind.