News Distribution Ethics Recommendations, 2022

Reasons:  This disclosure will help a) identify potential questions of exclusion or inclusion for specific types of traditional or non-traditional news sources in different news markets, b) assess whether some definitions in themselves bear upon discourse, harm, or the right of people to receive information from specific types of sources and c) assess the similarities and dissimilarities in definitions across different platforms.

Disclosure recommendations: 

1. How does your platform define “news” for both curation and algorithmic classification purposes?
2. How does your platform currently accord “News” site or “News publisher” status to any entity?
3. How does that designation affect access for (or promotion of) news publishers
   1. Access: Access to platform policy, exemptions, resources, business partnerships, etc. For e.g. some platforms are offering news publishers exemptions of particular kinds for boosting/advertising of content (ad policy) otherwise deemed as "political". Another access example is just registering formally as a publisher to be included in listings like News Tab or Showcase. Some news publishers get beta access to features/other special access to distribution measures.
4. Does your platform define “who is a journalist” for curation and algorithms and if so, what is the definition or standard
5. Definitions for the following as used by the platform. “Curation”,  “Location”,  “Topics”, “Trends/Trending” , and “News categories” used on feeds.
6. How does your platform define and/or detect “Opinion” from news publishers?

Reasons: These disclosures help clarify the role and scope of discourse-directing leverage that curation has at the platforms, including whether curation is being done both at the piece of content level (during breaking news cycles or trending conversations for e.g.) or also at the news source level. This in turn will help external oversight bodies determine the scope of accountability for curation in mitigating harms and enabling the participation of marginalized or excluded communities.

Disclosure recommendations: 

Note: By ‘curation’ we specifically refer to human beings on the platform side, employed or contracted, choosing and/or ordering articles as well as news sources, for particular surfaces.

1. Disclose surfaces
   
   1. Disclose surfaces and ways where human curation is a part of the platform
   2. Disclose surfaces and ways where humans training models are used for curation
2. Disclose principles and policies that guide curation
3. Disclose list of curated topics in each <period/reporting cycle>
   1. g. of “Topic”: Racial Justice, Housing, Food, etc. When you click those links it shows all stories in that topic. Sometimes a term represents a developing story itself  like "Infant formula" but those go away after a while. Some terms are standard and product people are posting them on top. E.g. “COVID-19” is top on many news product surfaces.
4. Human curators
   1. Which news stories did human curators give prominence to (display at the top of page/feed) per topic/moment/event/#trend each <period/cycle> without algorithmic assistance?
   2. Which news stories did human curators give prominence to (as in a. above) but with algorithmic assistance?

Reason: These disclosures will bring to the surface how news publishing entities – large, medium, small, and boutique -- are connected into a platform distribution system. This data may be usable to cross-interrogate data disclosed on other sections to build a holistic picture. It will help to understand the extent to which mainline or mass news media outlets are contributing to discourse vs. other niche outlets that often cover systemic issues continuously. 

Disclosure recommendations: 

Note: We recognize that any release of lists could be weaponized. The generation of such lists needs to be implemented with guardrails to mitigate disclosure risks. Each platform may have its own approach to this and we recommend they document it as part of this disclosure. For instance, some platforms may document their appeals process for decisions and disclose that along with these lists. 

By market (US, Europe/countries, Asia/countries, US-local markets, etc.

1. List of entities accorded news publisher status on curated products or by human curators.
2. List of entities whose content was recommended algorithmically as “News”
3. List of publishers whose stories (claims in stories) were disputed and failed third-party fact-checkers (false or pants on fire..)
4. List of publishers who violated content policies and details
5. List of publishers who were demonetized and grounds
6. List of publishers whose accounts were suspended or deplatformed
7. List of publishers whose accounts were restored after suspension
8. List of publishers whose content triggered top #trends data (per cycle)
9. List of publishers who are labeling News v Opinion
10. List of entities whose content is not curated or not recommended as “News” but who have asked for News Publisher status (long tail), i.e. entities who have asked for, but never heard back and were not denied
11. List of entities who requested news publisher status by formal means but not granted with grounds (junk publishers, deception, imposters, etc..), i.e. news entities that have been explicitly denied status 

Reasons: These disclosures will help analyze the types of stories, posts, entities and accounts that are prominent in the news cycles of a given period. It will help review whether the types of coverage we outlined in the "Obligations to foster discourse" section are prominent or not, and under what conditions. 

Disclosure recommendations: 

Note: 

1. The terms used below may not carry the same specific meaning or apply to the distribution system the same way per platform.  To avoid becoming platform-specific, we recommend that platforms revise these terms to the closest valid interpretations. For instance in the list below, “prominent” may mean displayed on top of a feed for that topic, trend, event, or moment, for one platform. For other platforms, the appropriate measure of prominence may be “impressions”. A separate but related measure of consumption for video is “views”. Alternatively, each adopting platform may simply customize a list using this as a reference, and add to this recommendation item. 
2. The usage of “topics”, “trends”, and wordings in hashtags for “trends” are not standardized - they are partly dynamic-cultural, partly taxonomically intuited. We recommend that platforms release along their default schemas for categorization that is publicly visible on their surfaces.  

By curated topics/trends/moments/events (e.g. for Twitter) and/or 
By market (US, Europe/countries, Asia/countries, US-local markets)

1. List the top
   1. List the top #n most prominent recommendations
   2. List the top #n impressions
   3. List the top #n most viewed 
2. (a,b,c) List of top #n most prominent recommendations/impressions/views only from news publishers (entities that have identified themselves as news publishers, or that the platform otherwise considers as a news publisher)
3. (a,b,c) List of top #n most prominent recommendations/impressions/views only from organic human accounts  (exclude accounts of journalists connected with one of the news publisher entities)
4. List the top #n most shared posts (retweets/shares)
5. List of top #n most shared posts only from news publishers
6. List of top #n most shared posts only from organic human accounts  (exclude accounts of journalists connected with one of the news publisher entities)
7. Does the platform maintain a quantitative measure of the percentage of content that got into distribution from the curation side vs the algorithmic recommendation side?  If so, define the measure and disclose numbers for recent measurement periods.  

(Note: We recognize that there are going to be standardization challenges for terminology such as topics, trends, moments, events, etc., that will make normalized comparisons difficult across platforms. One of the goals of this effort would be to move to standardization after some cycles of disclosure and learning have taken place.)

Reasons: Platforms take remedial actions every day. For news posts violating policy, remedial actions may include demonetization, demotion in ranking, exclusion from prominence in lists, de-platforming of a post itself, and so forth. How long particular false claim-laden stories propagate, especially on systemic issues, before remedial actions are taken, may have significance for rights, harms as well as discourse. Early impressions are harder to reverse in general even as more accurate stories surface from other news publishers. Disclosures about remedial actions taken within distribution systems about news will help oversight entities understand the current role of such actions in relation to harms, rights, and discourse. It may also identify potential gaps and opportunities. 

For stories that news publishers may have themselves corrected upstream, remedial action may involve detecting the correction first. The question of whether platforms can re-distribute the corrected story without loss of prominence is somewhat moot since originating corrections have to be detected. (We recognize this is yet another major problem area in the news industry to standardize and signal corrections consistently.)  

Disclosure recommendations: 

Below, per day/week/per market:

1. List of news posts/articles referred to fact-checkers and flagged as disputed and their retweet/share #n counts after the flagging happened.
2. List of news posts/articles that violated content policies and were acted on in any way - engagement limited, labeled, demonetized, deplatformed, etc. (*Note: Items 3 and 4 below tagged “News Publisher Corrections”, are related to corrections in news articles at the publishers’end. Currently, news aggregators and platforms do not have standardized visibility into data showing corrections in real time. We acknowledge these two items are futuristic.)
3. *News Publisher Corrections: List of most prominent recommendations of news items that were later corrected by publishers, and the relative distribution data for those posts (This is to capture post-correction distribution (tweets, retweets) for the same story. Let's say WaPo posted the first story at 901am 5/16. And it got {x-impressions,y-shares/retweets,z-something} distribution metrics. At 12:02 pm they made a correction and posted that. Disclose the distribution for the correction in the same {x,y,z} terms)
4. *News Publisher Corrections: List of most shared posts of news items that were later corrected by publishers, and the relative distribution data for those posts (same as above, except engagement metric changes from impressions to sharing)

Reason:  Opinion journalism – which can often include reporting – is not only engaging, it has an independent strength to draw people into discourse that reportage alone does not. Livestreams may contain both reportage and opinion for instance. Crucial explanatory journalism at critical times by individuals can often elevate the discourse to a shared understanding where the distinction between opinion and reportage seems even unnecessary to some. But in general, when observing on-platform discourse, the role played by opinion journalism  (required by ethics for arguments to be based on facts) as opposed to political propaganda (which uses lies, false narratives, and “othering”) is not clear. 

The news industry itself (in most markets if not every) has not built a consistent practice of separating and signaling opinion journalism. Broadcast journalism blurred the lines between commentary and reportage before digital journalism arrived. Even when opinion labels exist, it isn’t clear whether they are accurate and whether news organizations are incentivized to mark them accurately. Disclosures will help oversight entities understand how otherwise robust opinion journalism from news publishers is passing through distribution systems and its role in the discourse on systemic issues, and whether distribution policies and design are determining any aspects of the role. 

Disclosure recommendations:

Policy 

1. Are you automatically detecting opinion as distinct from reporting? If so is your system itself designating an opinion label? 
2. Is your platform separating opinion feeds for topics/developing stories/etc.?
3. Is your platform passing opinion label metadata (such as schema.org labels, when present) downstream to the news feed/discovery surfaces for users to see as a label?
4. How is your platform handling conflict between a publisher-supplied label and your own auto-detection? (E.g. Publisher says “reportage or news”, but your detection says “opinion”) 
5. Is your platform subjecting opinion journalism to any form of vetting? If so, what? (e.g. internal validation/assessment for factual basis, or fact-checking..) 

Data

By curated topics/trends/moments/events AND/OR
By market (US, Europe/countries, Asia/countries, US-local markets) 

1. List of top#n most prominent recommendations/impressions/views of opinion posts from news publishers
2. List of top#n most shared opinion posts from news publishers
3. List of news publishers labeling opinion content formally whose stories you are distributing.