Last updated January 16 – see the update log.
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Image description: At the top, a box saying “dogpile target”. Below, boxes with words: “1. Find a post to target” with a red X over it and a circle below saying “Limit who can post”; “2. get wide visibility” with a complex diagram below it; “3. Others pile on with replies, DMs, notifications …” with a red X over it and a circle below saying “Limit visibility, limit replies, limit notifications, shields up mode”.
Below “Get Wide visibility”, two boxes side-by-side, both with red X’s over them: “Boost” and “Reply and boost”. Below those boxes, a circle with “Limit replies; Limit boosting”. To the left, a box with “Send link via DM, email, or chat”. To the right, a box with “Quote” on it, and a complex diagram below it.
Below the “quote” box, four boxes. On the left, “Screenshot and Link” and “Media(TV, newspaper)” with a circle below saying “Norms about consent. Content licensing?” Next, “Private quote boost” with a big red X, and a circle below it saying “Prohibit.” On the right, “Public quote boost” with a big red X, and a circle below saying “Limit who can QB; Require consent.”
Notes
1 Almost none of the research Bastian cites looks directly at harassment and abuse, let alone examine potential differences in how different kinds of attackers operate over time, as Twitter’s affordances, harassment techniques, and the dynamics of overall weaponization have all changed. While there are papers looking at hate speech, and in particular slurs against disabled people and anti-semitic, hone of the cited research appears to look specifically at harassment of trans people, Black women, and other common targets of harassment.
And some significant harassment techniques using quoting are invisible in the data. One obvious example: as we discuss later in the artcle, “private QTs” were a maor Twitter attack vector for a while. Since they’re private, they aren’t available in any of the data sets researchers have worked with … so we have no idea how much they are or aren’t used in harassment.
Or hypothetically suppose that Twitter’s moderation leaves trans people particularly at risk, and/or anti-trans bigots have refined techniques for weaponizing QTs … which of the 30+ research papers would reflect this?
2 Unfortunately, once a post has federated to other instances, there’s no guarantee you can delete it: buggy or incompatible software on other instances might ignore the deletion request, or if the other instances has defederated from yours they might not even see it. I’ve found posts from instances that no longer exist still available on other instances, and even in Google searches. Thi isn’t an issue for local-only posts, another reason they’re so important.
3 Supporting this once a post has federated to other instances could be very challenging to implement well (and as with deletion, buggy or icompatible instances might just ignore the change in visibility), but it should be straightforward to implement for local-only posts.
4 Of course, attackers could get around this by having somebody with fewer followers QB and then having the high-profile boosting the QB, so it’s not clear how much this would help … but it’s worth considering.
5 And unless the software supports a smooth way to discuss consent, requiring people to ask for consent could lead to a lot of posts cluttering up everybody’s timeline. “Can I quote this?” “What part of it are you going to quote?” “The first sentence.” etc. etc. etc.
Updates
January 16: add references to A better moderation system is possible for the social web, OcapPub: Towards networks of consent, and protocol improvements.
