In this claim are several places where there are simplifications being made, particularly in the definitions of “Twitter users” and “spam bots.”
i find it very interesting how Twitter counts its bots to make the 5% claim. I also find it interesting how the small difference (and in wording allows them to get away with the claim
Metadata is information about some data. So we often think about a dataset as consisting of the main pieces of data (whatever those are in a specific situation), and whatever other information we have about that data (metadata).
I've heard the term metadata a few times in the past, but never understood what it meant. It now makes a lot of sense that it's data about data. It's helpful to know about the data we're collecting
conversations
It amazes me how quickly the internet tries to corrupt these bots. As another comment mentioned, the same thing is also happening to Twitter's Grok, as well as many other similar bots in recent days.
Antagonistic bots can also be used as a form of political pushback that may be ethically justifiable. For example, the “Gender Pay Gap Bot” bot on Twitter is connected to a database on gender pay gaps for companies in the UK. Then on International Women’s Day, the bot automatically finds when any of those companies make an official tweet celebrating International Women’s Day and it quote tweets it with the pay gap at that company:
I find it fascinating that while antagonist bots can be used negatively, there are ones, like the gender pay gap bot, that are used to create a positive impact and bring attention to issues like these. It's nice to see that, like all things, even 'antagonist bots' can also be used towards a positive change.