spam

 spam  is unsolicited content, often transmitted via email, submitted as blog comments or posted to popular wikis.

Almost any type of data entry on the web could potentially be vulnerable to spammy content. Typically the most vulnerable are unprotected comments forms and endpoints consistent across domains due to monoculture.

The Coming Spam Storm
Spam has effectively destroyed earlier protocols of pingback/trackback, and WordPress reblogs.
 * http://piedtype.com/2013/05/21/spammers-use-trackbacks-pingbacks-and-reblogs/

As Webmention + h-entry replies/comments/likes/reposts/RSVPs adoption grows, the probability the spammers will adopt it also grows.

It behooves us, the indieweb, to be pre-emptively thinking about how to fight spam.

If you accept webmentions and alter your display in any way (showing cross-site comments, likes, reposts, RSVPs), then you are potentially vulnerable to being spammed.

We have a proposal for preventing Webmention spam:
 * Vouch

Take a look at the Vouch extension to Webmention, try implementing it, interoperating with existing implementations, and report your experience.

WordPress Spam
WordPress is both a huge target and tool for spammers. The more popular WordPress has become, the more they've targeted it. By analyzing spammers tactics (and weaknesses) against WordPress, we may find clues that help us block them from the indieweb.

Akismet
Akismet, Automattic's flagship spam filtering service, is very popular with self-hosted WordPress users and enabled by default on WordPress.com blogs. The team publishes statistics on their blog every month. They're pretty thin - just global spam vs ham counts per day - but still interesting. Ham generally comprises just 1-5% of all comments.

During November 2014, Akismet saw 12.2B spam comments and 160M ham comments, with a success rate of 99.997%. Not bad.

Comment Spam
Comment spam is one of the most common forms of WordPress spam, if not the most common.

Anecdotally, during the month of November 2014, https://snarfed.org/ received 796,990 POSTs to its comment form,, vs just 2139 POSTs to. Of those, 9 comments and 3 trackbacks were ham. also uses XML-RPC to create and edit snarfed.org posts. So there was at least ~400x more comment form spam than pingback/trackback spam.

Pingback Spam
Pingback and trackback are well-known WordPress spam vectors, so much so that Akismet is about to shut off (or has already?) pingbacks. As cited above:
 * 2013-05-21: Spammers use trackbacks, pingbacks, and reblogs

Notably, the Akismet team commented:

These days almost 100% of Trackbacks and Pingbacks are spam.
 * 2022-06-09 Miriam Eric Suzanne: The Spam Has Arrived
 * 2022-06-15 : Pingbacks, Trackbacks, and CSS-Tricks

Like Spam
WordPress introduced a WordPress-only feature called "WordPress Like" which is essentially their version of the Facebook "Like" button. Spammers create WordPress.com spam blogs and then "Like" other WordPress blogs in order to get links to their spam blogs. As such, WordPress users complain of most of their likes being "fake" or from spammers only.
 * http://piedtype.com/2013/05/02/spammers-like-wordpress-news-too/
 * http://piedtype.com/2013/04/20/why-i-turned-off-wordpress-like/

Such likespam is a frequent occurrence on various silos as well, where spam account like various posts in order to get the authors to see who liked their posts, show them a spam message on their profile, and perhaps even get them to click a spam link in their profile. Instances of likespam are readily findable on: In both cases, even if you block the spam account, their "like" on your silo post remains. Though hidden from you (since you blocked them), viewers of your posts still see the spamlikes, and are thus still exposed to following them to see spam messages or links to spam sites in their profiles.
 * Instagram - often with "get more followers" type messages
 * Twitter

This failure to remove spamlikes of blocked accounts from posts is sufficient reason to go silo-private, since posts on silo private account can only be "liked" by those you explicitly give permission to, thus preventing all likespam on such accounts.

Silo likespam is particularly bad for the indieweb when you automatically backfeed (e.g. using Bridgy) from your silo POSSE copies, and thus end up linking to the likespammers from your own site!

Follower Spam
As noted in:
 * http://onecoolsitebloggingtips.com/2013/04/09/wordpress-com-follower-management/
 * http://piedtype.com/2013/04/12/issues-wordpress-needs-to-address/

As noted in the onecoolsitebloggingtips post, spam blogs have several weaknesses:
 * linking to non-existent Twitter/Facebook/WordPress.com profiles
 * linking to Twitter/Facebook/WordPress.com profiles not updated in months

This implies that if we require rel-me symmetry link checks on any Twitter/Facebook/WordPress.com (and other?) sites that someone links to, we can filter out some of the spam.

In the case of Twitter, people try to use protected mode to prevent unsolicited access to their posts. This can lead into a situation where a fleet of accounts can flood someone's follower request stream.

Spam Prevention
There are a great many techniques that can be utilized to prevent spam.

Note: Prevent, not minimize. The goal here is make spam defense a zero maintenance feature. Partial solutions, going through moderation queues etc. still adds a maintenance tax, which no one wants.

Degrees of Separation
Note: The following is a brainstorm, does not reflect current implementations and probably needs work --Waterpigs.co.uk 16:21, 22 May 2013 (PDT)

Typically I consider allowlists to be a negative thing for accepting general commentary — it seems elitist and similar to search bubbling (i.e. trapped in a bubble). However, allowlists are the most straightforward and probably most effective prevention measures.

So, a more flexible allowlist-based approach might be implemented as follows:

Initial setup:


 * I start with a allowlist of people — e.g. my address book (1st degree contacts)
 * My software crawls their websites and extracts XFN data and mentions of people (2nd degree contacts)
 * I associate each 2nd degree contact with their associated 1st degree contact, building a graph without actually adding the 2nd degree people to my address book
 * I periodically re-crawl the graph and update accordingly
 * perhaps subscribing to their feeds via pubsubhubbub and adding any mentioned people would be a good solution
 * Do we have a way of notifying interested parties that an XFN listing has been updated?
 * Whenever a new contact is added (or a 2nd degree contact is moved to 1st degree) their website is crawled for mentions to add to the graph

Posting a note mentioning someone:


 * 1) I post a note mentioning/in reply to someone not yet in my address book
 * 2) Their website is crawled, any microformats are parsed and they are added as a 1st degree contact
 * 3) Any future comments from them are accepted automatically as detailed below

Receiving a comment from a 1st degree contact:


 * 1) I post a note, and someone in my address book submits a comment
 * 2) The comment is automatically approved as they are 1st degree contact
 * 3) Any person they mention in that comment is added to the graph as 2nd degree contacts
 * 4) * TODO: or 1st degree, as they’ve actively been included in the conversation?

Receiving a comment from an unknown person:


 * 1) I post a note
 * 2) Someone I don’t know submits a comment, which is not displayed publicly
 * 3) I receive a notification asking me to approve the person
 * 4) * If I do so, they are added to my address book, their comment goes public and all future comments from them are accepted
 * 5) * If I do not, they are added to a blocklist and any further comments from them are ignored

Twitter follows follows
In a turnabout of sorts, we can use existing silo connections as a potential spam filter as well.

Here's you could use Twitter's social graph as a dynamic second degree allowlist:

Note: when you visit someone's profile on Twitter, if you're not already following them, it shows you which of the people you are following (your "follows"), are following them. This could be used in the following way:

Algorithm:

When your site A receives a comment from an indieweb site B:
 * Does B have a confirmed rel-me link to a Twitter profile Tb?
 * Does your Twitter Ta follow Tb? Yes? Accept the comment.
 * Do any of Ta's follows follow Tb? Yes? Accept the comment.

Levels of presentation based on combination of trust signals
Displaying responses doesn't have to be an all or nothing option. Trusted sources could be shown more completely, while untrusted sources could be displayed in a way that limited their usefulness as spam vectors.

As discussed in 2018/Berlin/responses, possible options for displaying content based on trust could include:
 * 1) icon, name, summary content (most trusted)
 * 2) icon, name, sanitized content (no images or links)
 * 3) icon and name, but no content?
 * 4) name only, no content
 * 5) count only
 * 6) ignore/show nothing (least trusted)

Multiple signals can be combined to determine how trusted the source is. More significant signals could be weighted more highly in the trust algorithm. The display options could be mapped to different "trust score" thresholds.

Possible signals for trust could include
 * Allowlist (personal or shared)
 * Sites you follow or link to
 * Followed or linked to by a trusted source (Vouch)
 * In indieweb chat-names or a trusted webring
 * Support of indieweb protocols

Possible signals for distrust could include
 * Has link/image
 * Blocked words (i.e. hateful language blocklist)
 * Low quality content that could be automated (e.g. "Thank you")
 * Is only a link (no comment text)
 * Webmention source does not link to page
 * A report button users can click
 * Block list (personal or shared)

Other
Perhaps break these into separate sections if you're considering pursuing them:


 * Moderation — the most simple form of prevention. Don’t publicly display any 3rd party submitted data until you’ve manually approved it


 * captchas — images or challenges designed to flummox a machine but be trivial for a human to complete
 * For example “write the (obfuscated image) code in the box” or “enter the sum of 4 and 6”
 * Major UX issues
 * Increasingly hard to solve for humans, due to race of arms between CAPTCHAs and AI
 * Exclude users in need of assistive technology


 * Automated spam detection — use some learning algorithm/training data to decide whether or not it’s likely to be spam
 * Example service: akismet
 * Pump.io's spam filtering software: e14n/activityspam


 * Blocklisting — figure out from which IPs a lot of spam comes, blocklist them
 * Alternatively blocklist the domains that try to advertise themselves. This is what Matomo (formerly Piwik) does against referer spam using a public domain blocklist: matomo-org/referrer-spam-blacklist


 * Allowlisting — the opposite of blocklisting. Have a predetermined list of people from which you will accept data


 * High barrier to entry — a little similar to a captcha: force the user to perform some task/satisfy some condition before allowing them to enter data.
 * An example being this very wiki — we have zero spam due to the need to log in using your own domain via indieauth (we've been lucky / under the radar - spammers have blogs and know how to link to spam profiles on other networks like Twitter.


 * Payment via Hashcash

Expiring token in endpoint
While an expiring token in the endpoint may help mitigate DDOS, based on recent experience it seems ineffective for preventing (or even minimizing?) spam.

Overcomplicated
Webmentions could implement a DKIM solution as e-mail has, as in: public keys in DNS record; messages have signed by private key additional headers. The issues are: complexity ( both in setup and in implementation ); dependency on the DNS system which is not always trivial; if it's optional only, it would not make enough difference.

Webmention
This might be the first native webmention spam seen in the wild, 2018-01-29. Source, target, discussion.

It's unclear whether it's true spam, or whether the sender was just testing their webmention sending on an unrelated target. The target was an article about testing webmentions, and the sender sent another similar webmention on 2019-06-18 (source), which makes it seem more like testing than spam.

As of 2019-07-12, it's still the only native webmention spam candidate we've seen in the wild.

Anti-spam techniques
dobrado supports both blocklisting and allowlisting by domain, of webmention comments and notifications since July 2016.

Spam Detection
In some cases it may be difficult to prevent spam, and thus the next best thing is to detect it quickly and delete it after the fact.

Approaches:
 * 2016-03-01 Spamduffing - detecting multiple posts on a new account too quickly to likely be human, auto-deleting.

Recognizing auto-generated spam text:
 * "Evil Blog Comment Spammer just exposed his template through some error and the whole thing showed up in my comments." https://gist.github.com/shanselman/5422230 (unlinked due to comments on that gist itself getting spammed, becoming a honeypot of sorts)

Manual tips for recognizing and detecting spam:
 * Compliment author-link spam. Learn to look for content-free "Thank you" or "Grateful for" or other short complimentary text posts which often provide a clue that the author’s website link is a spam link, often brazenly with two word link text that on a quick skim looks like an author’s name, e.g.
 * https://blog.archive.org/2020/09/17/internet-archive-partners-with-cloudflare-to-help-make-the-web-more-useful-and-reliable/#comment-399498 "Flipkart Offer September 26, 2020 at 9:10 am Nice post thank you so much" (author name "Flipkart Offer" deliberately not linked here in-quote, though it was linked to a dash-separated spam blog name "flipkart-cashback-offers-today" .Blogspot.com which is still up at Google-owned Blogspot as well! Note that the spam comment has been up for ~1.5 years, so it was either undetected by the blog post author, or possibly even moderated and allowed due to the complimentary text)

Spam Prevention Service Brainstorming
A web service which, given a source and a target, returns a simple spam/ham/requires-moderation response.

Sign up+crawl flow:
 * You sign up for anti-spam using indieauth
 * anti-spam builds a list of all the people in your social graph
 * crawls XFN
 * crawls+subscribes to feeds, continuously automatically adds people you link to, reducing maintenance
 * detects linked-to silos and crawls silo social graphs

Webmention filtering flow:
 * When you receive a webmention, you pass the source and target URLs to an anti-spam endpoint, which returns “spam/ham/requires-moderation” based on whether you, or anyone you linked to, has linked to the source domain before
 * optionally also a bunch of other information e.g. how many degrees of separation between you, which of the people you’ve linked to also linked to them — this could be put to excellent use in a moderation UI
 * your publishing software processes or discards the webmention as required — it’s up to your software to handle moderation UI and flow.

Spam reporting flow:
 * one way or another you’ve come across some spam
 * send a POST request to anti-spam.com/report or some such endpoint with the URL
 * that domain gets added to your personal blocklist
 * perhaps also either to a global anti-spam list, or maybe blocklists should be shared in the same way allowlists are, where if a domain is in your blocklist or the blocklist of anyone you’ve linked to, they get blocked.

Possible other option: webmention proxy. You use anti-spam.com or whatever as your webmention endpoint, which forwards non-spam webmentions to your real endpoint, perhaps with some extra parameter indicating if it needs moderation. Requests would be signed, making it easy to determine that they are in fact from anti-spam

iMessage Spam
Apparently iMessage is now being spammed, by spammers registering hotmail.com addresses as AppleIDs:

Received 2013-11-30 11:14 PST



Related Sessions
IndieWebCamp sessions related to spam and webmentions in particular
 * 2016 Brighton: What can Webmention learn from Email