Content Moderation at Scale, DC Version

Foundations: The Legal and Public Policy Framework for
Content
Eric Goldman gave a spirited overview of 230 and related
rules, including his outrage at the canard that federal criminal law hadn’t
applied to websites until recently—he pointed out that online gambling and drug
ads had been enforced, and that Backpage was shut down based on conduct that
had always been illegal despite section 230. 
Also a FOSTA/SESTA rant, including about supplementing federal
prosecutors with state prosecutors with various motivations: new enforcers, new
focus on knowledge which used to be irrelevant, and new ambiguities about what’s
covered.
Tiffany Li, Yale ISP Fellow: Wikimedia/YLS initiative on
intermediaries: Global perspective: a few basic issues. US is relatively unique
in having a strong liability framework. In many countries there aren’t even
internet-specific laws, much less intermediary-specific.  Defamation, IP, speech & expression,
& privacy all regulated.  Legal
issues outside content are also important: jurisdiction, competition, and
trade. Extremist content, privacy, child protection, hate speech, fake news—all
important around the world.
EU is a leader in creating law (descriptive, not normative
claim).  There is a right to receive
information, but when rights clash, free speech often loses out. (RTBF, etc.)  E-Commerce Directive: no general monitoring
obligation.  Draft copyright directive
requires (contradictorily) measures to prevent infringement.  GDPR (argh). 
Terrorism Directive—similar to anti-material support to terror
provisions in US.  Hate speech
regulations.  Hate speech is understood
differently in the EU. Germany criminalizes a form of speech US companies don’t
understand: obviously illegal speech; high fines & short notice &
takedown period.  AV Services directive—proposed
changes for disability rights.  UK
defamation is particularly strong compared to US.  New case: Lewis v. FB, in which someone is
suing FB for false ads w/his name or image.
Latin America: human rights framework is different.  Generally, many free expression laws but also
regulation requiring takedown.  Innovative
as to intermediary liability but also many legislative threats to
intermediaries, especially social media.
Asia: less intermediary law generally.  India has solid precedent on intermediary
liability: restrictions on intermediaries and internet websites are subject to
freedom of speech protections.  China: developing
legal system. Draft e-commerce law tries to put in © specifically, as well as
something similar to the RTBF. Singapore: proposed law to criminalize fake
news.  Privacy & fake news are often
wedges for govts to propose/enact greater regulation generally.
Should any one country be able to regulate the entire
world?  US tech industry is exporting US
values like free speech.
Under the Hood: UGC Moderation (Part 1)
Casey Burton, Match: Multiple brands/platforms: Tinder,
Match, Black People Meet.  Over 300
people involved in community & content moderation issues, both in house and
outsourced. 15 people do anti-fraud at match.com; 30 are engaged fulltime in
content moderation in different countries. 
Done by brand, each of which has written guidelines.  Special considerations: their platforms are
generally where people who don’t already know each other meet. Give reporters
of bad behavior the benefit of the doubt. 
Zero tolerance for bad behavior. 
Also not a place for political speech; not a general use site: users
have only one thing on their minds. If your content is not obviously working
towards that goal you & your content will be removed. Also use some
automated/human review for behavior—if you try to send 100 messages in the
first minute, you’re probably a bot.  And
some users take the mission of the site to heart and report bad actions.
Section 230 enables us to do the moderation we want.
Becky Foley, TripAdvisor: Fraud is separate from content moderation—reviews
intended to boost or vandalize a ranking. 
Millions of reviews and photos. 
Have little to no upfront moderation; rely on users to report. Reviews
go through initial set of complex machine learning algorithms, filters, etc. to
determine whether they’re safe to be posted. A small percentage are deemed
unsafe and go to the team for manual review prior to publication. Less than 1%
of reviews get reported after they’re posted.  Local language experts are important.  Relevance is also important to us, uniquely
b/c we’re a travel site.  We need to
determine how much of a review can go off the main focus.  E.g., someone reviews a local fish &
chips shop & then talks about a better place down the street: we will try
to decide how much additional content is relevant to the review.
Health, safety & discrimination committee which includes
PR and legal as well as content: goal is to make sure that content related to
these topics is available to travelers so they’re aware of issues. There’s
nobody from sales on that committee. Strict separation from commerce side.
Dale Harvey, Twitter: Behaviors moderation, which is
different from content moderation. Given size, we know there’s stuff we don’t
know. In a billion tweets, 99.99% ok is 10,000 not ok, and that’s our week.
Many different teams, including information quality, IP/identity, threats,
spam, fraud.  Contributors: have a voice
but not a vote—may be subject matter experts, members of Trust & Safety
Council—organizations/NGOs from around the world, or other external or internal
experts.
Best practices: employee resilience efforts as a feature.
The people we deal with are doing bad things; it’s not always pleasant.
Counseling may be mandatory; you may not realize the impact or you may feel
bravado.  Fully disclose to potential
employees if they’re potentially going to encounter this.  Cultural context trainings: Silicon Valley is
not the world.  Regular cadence of refreshers
and updates so you don’t get lost.  Cross
functional collaborations & partnerships, mentioned above.  Growth mindset.
Shireen Keen, Twitch: real time interactions. Live chat
responds to broadcast and vice versa, increasing the moderation challenge. Core
values: creators first.  Trust and safety
to help creators succeed. When you have toxicity/bad behavior, you lose users
and creators need users on their channels. Moderation/trust & safety as
good business. Community guidelines overlay the TOS, indicating expectations.  Tools for user reporting, processing, Audible
Magic filtering for music, machine learning for chat filtering. Goal:
consistent enforcement.  5 minute SLA for
content.  
Gaming focus allowed them to short circuit many policy
issues because if it wasn’t gaming content it wasn’t welcome, but that has
changed. 2015 launched category “creative,” still defining what was allowed. Over
time have opened it further—“IRL” which can be almost anything.  Early guidelines used a lot of gaming language;
had to change that.  All reported
incidents are reviewed by human monitors—need to know gaming history and lingo,
how video and chat are interacting, etc. 
Moderators come from the community. Creators often monitor/appoint
moderators for their own channels, which reduces what Twitch staff has to deal
with. Automated detection, spam autodetection, auto-mod—creator can choose
level of auto-moderation for their channel. 
Sean McGillivray, Vimeo: largest ad-free open video
platform, 70 million users in 150 countries. 
A place for intentional videos, not accidental (though they’ll take
those too).  No porn.  [Now I really want to hear from a Tube site
operator about how it does content moderation.] 
Wants to avoid being blocked in any jurisdiction while respecting free
speech.  5 person team (about half legal
background, half community moderation background) + developer, working w/others
including community support, machine learning. 
We get some notices about extremist content, some demands from
censorship bodies around the world. We have algorithmic detection of everything
from keywords to user behavior (velocity from signupàaction).  Some auto-mod for easy things like spam and rips
of TV shows. Some proactive investigation, though the balance tips in favor of
user flagging. We may use that as a springboard depending on the type of
content. Find every account that interacted w/ a piece of content to take down
networks of related accounts—for child porn, extremist content.  We can scrub through footage pretty quickly
for many things. 
There are definitely edge cases/outliers/oddballs, which is
usually what drives a decision to update/add new policy/tweak existing policy.  When new policy has to be made it can go to
the top, including “O.G. Vimeans”—people who’ve been w/the community from the beginning.  If there’s disagreement it can escalate, but usually
if you kill it, you clean it: if user appeals/complains, you explain.  If you can’t explain why you took it down,
you probably shouldn’t have taken it down. 
There’s remediation—if we think an account can be saved, if they show
willingness to change behavior or explain how they misunderstood the
guidelines, there’s no reason not to reverse a decision. We’re not parents and
we don’t say “because I said so.”
Challenges: we do allow nudity and some sexual content, as
long as it serves an artistic, narrative or documentary purpose. We have always
been that way, and so we have to know it when we see it. He might go for
something more binary, but that’s where we are. We make a lot of decisions based
on internal and external guidelines that can appear subjective (our nipple
appearance/timing index).  Scale is an
issue; we aren’t as large as some, but we’re large and growing with a small
team.
We may need help w/language & context—how do you tell if
a rant to the camera is a Nazi rant if you can’t speak the language?
Bots never sleep, but we do.
Being ad free: we don’t have a path to monetization.  We comply w/DMCA. No ad-sharing agreement we
can enter into w/them.  Related: we have
pro userbase.  Almost 50% of user are
some form of pro filmmaker, editor, videographer. They can be very
temperamental. Their understanding of © and privacy may require a lot of handholding.  It’s more of a platform to just share work.
We do have a very positive community that has always been focused on sharing
and critique in a positive environment. 
That has limited our commitment to free speech—we remove abusive
comments/user-to-user interaction/harassing videos.  We also have an advantage of just dealing
w/videos, not all the different types of speech, w/a bit of comments/discussion.  Users spend a lot of time monitoring/flagging
and we listen to them.  We weight some of
the more successful flaggers so their flags bubble up to review more quickly.
Goldman: what’s not working as well as you’d like?
Foley: how much can we automate w/o risking quality? We don’t
have unlimited resources so we need to figure out where we can make compromises,
reduce risk in automation.
McGillivray: you’re looking to do more w/less.
Keen: Similar. Need to build things as quickly as possible.
Harvey: Transparency around actions we take, why we take
those actions. Twitter has a significant amount of work planned in that
space.  Relatedly, continuing to share
best practices across industry & make sure that people know who to reach
out to if they’re new in this space.
Burton: Keep in mind that we’re engaged in automation arms race
w/spambots, fake followers, highly automated adversaries. Have to keep
human/automated review balanced to be competitive.
Under the Hood: UGC Moderation (Part 2)
Tal Niv, Github: Policy depends a lot on content hosted,
users, etc. Github = world’s largest software development platform. The heart
of Github is source control/version system, allowing many users to coordinate
on files with tracked changes. Useful for collaboration on many different types
of content, though mostly software development. 
27 million users worldwide, including individuals & companies, NGOs,
gov’t.  85 million repositories. Natural
community.
Takedowns must be narrow. 
Software involves contribution of many people over time; often a full
project will be identified for takedown, but when we look, we see it’s sometimes
just a file, a few lines of code, or a comment. 
15 people out of 800 work on relevant issues, e.g., support subteam for
TOS support, made of software programmers, who receive initial intake of
takedowns/complaints.  User-facing
policies are all open on the site, CC-licensed, and open to comment.  Legal team is the maintainer & engages
w/user contributions.  Users can open
forks. Users can also open issues.  Legal
team will respond/engage.  List of
repositories as to which a takedown has been upheld: Constantly updated in near
real time, so no waiting for a yearly transparency report.
Nora Puckett, Google
Legal removals (takedowns) v. content policies (what we don’t
want): hate speech, harassment; scaled issues like spam and malware.  User flags are important signals. Where
request is sufficiently specific, we do local removals for violation of local
law (general removals for © and child exploitation).  Questions we prompt takedown senders to
answer in our form help you understand what our removal policies are.  YT hosts content and has trusted flaggers who
can be 90% effective in flagging certain content.  In Q4 2017, removed 8.2 million videos
violating community guidelines, found via automation as well as flags and
trusted flags.  6.5 million were flagged
by automated means; 1.1 million by trusted users; 400,000 by regular users.  We got 20 million flags during the same
period  [?? Does she mean DMCA notices,
or flags of content that was actually ok?]. 
We use these for machine learning: we have human reviewers verifying
automated flags are accurate and use that to train machine learning algorithms
so content can be removed as quickly as possible. 75% of automatically flagged videos
are taken down before a single view; can get extremist videos down in 8 hours
and half in less than 2 hours. Since 2014, 2.5 million URL requests under RTBF
and removed over 940,000 URLs since then. In 2018, 10,000 people working on
content policies and legal removal.
Best practices: Transparency. We publish a lot of info about
help center, TOS, policies w/ exemplars.
Jacob Rogers, Wikimedia Foundation: Free access to
knowledge, but while preserving user privacy; self-governing community allowing
users to make their own decisions as much as possible. Where there are clear
rules requiring removal, we do so. Sometimes take action in particularly problematic
situations, e.g. where someone is especially technically adept at disrupting
the site/evading user actions. Biannual transparency report. No automated tools
but tools to rate content & draw volunteers’ attention to it.  E.g., will rate quality of edits to
articles.  70-90% accurate depending on
the type of content. User interaction timeline: can identify users’ interactions
across Wikipedia and determine if there’s harassment going on.  Relatively informal b/c of relatively small #
of requests. Users handle the lion’s share of the work. Foundation gets 300-500
content requests per year.  More
restrictive than many other communities—many languages don’t accept fair use
images at all, though they could have them. 
Some removals trigger the Streisand effect—more attention than if you’d
left it alone.
Peter Stern, Facebook: Community standards are at core of content
moderation.  Cover full range of
policies, from bullying to terrorism to authentic ID and many other areas.
Stakeholder engagement: reaching out to people w/an interest in policies.  Language is a big issue—looking to fill many
slots w/languages.  Full-time and outsourced
reviewers.  Automation deals w/spam and
flags for human review and prioritizes certain types of reports/gets them to
people w/relevant language/expertise. Humans play a special role b/c of their ability
to understand context.  Training tries to
get them to be as rigid as possible and not interpret as they go; try to break
things down to a very detailed level tracking the substance of the guidelines,
now available on the web.  It only takes
one report for a policy violation to be removed; multiple reports don’t
increase the likelihood of removal, and after a certain point automation shuts
off the review so we don’t have 1000 people reviewing the same piece of content
that’s been deemed ok. Millions of reports/week, usually reviewed w/in 24
hours. Route issues of safety & terrorism more quickly into the queue.
Most messaging explains the nature of the violation to users.  Appeals process is new—will discuss on Transparency
panel. 
Resiliency training is also part of the intake—counseling available
to all reviewers; require that for all our vendors who provide reviewers. Do audits
for consistency; if reviewers are having difficulty, then we may need to rewrite
the policy.
Community integrity creates tools for operations to tools,
e.g. spotting certain types of images.
Strategic response team. E.g., there’s an active
shooter.  Would have to decide whether he’s
a terrorist, which would change the way they’d have to treat speech praising
him. Would scan for impersonation accounts.
Q: how is content moderation incorporated into product
development pipeline?
Niv: input from content moderation team—what tools will they
need?
Puckett: either how current policies apply or whether we
need to revise/refine existing policies—a crucial part.
Rogers: similar, review w/legal team. Our product development
is entirely public; the community is very vocal about content policy and will
tell us if they worry about spam/low quality content or other impediments to
moderation.
Stern: Similar: we do our best to think through how a
product might be abused and that we can enforce existing policies. Create new
if needed.

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