The week on the radar
Emails, news, videos, podcasts and AI research from the week of October 19 – October 25, 2026.
October 19 – October 25, 2026
From Monday to Sunday the radar picked up 556 items: 191 emails, 96 news articles, 231 videos and 38 podcast episodes. AI models did a first reading of 509 of them. This is what stood out.
Emails
191
from 74 organizations
News articles
96
from 26 sources
Videos
231
from 72 channels in 28 countries
Podcast episodes
38
from 16 shows
24 of 39
emails on October 22 asked for money: matches of 3X, 4X, 5X, even $10 for each $1. 7 subject lines offered one.
Fundraising emails per day
14
subject lines were about the election or voting, against 8 the week before. Election Day is November 3.
Subject lines about the election, per day. Counted by code
48%
of emails aim for urgency, against 39% the week before, and hope fell from 50% to 43%. Compassion leads, at 49%.
Share of emails. 191 this week, 206 the week before
33%
of the week’s 2.30M video views went to Rädda Barnen’s song against violence toward children. World Vision Korea posted 5 of the top ten.
Views of the week’s 231 videos, by October 26
16 of 96
articles read as negative, against 6 of 81 the week before. Government funding was the gloomiest: 8 of 15.
Share of articles by tone
44 of 78
opinions were worried, against 37 of 73 the week before. Leadership was again the top problem (19 of 31 episodes).
The 78 opinions, by tone
The same AI model read both weeks, so their labels can be compared, with care: feelings and tone are soft labels. Subject-line counts are plain code.
What landed in the inbox
191 emails reached us from 74 nonprofits, nearly all in the United States: fewer than the week before (206), with as many appeals (89, against 90). They peaked on Thursday, October 22, as matching gifts came due.
Emails received per day. Dark: fundraising appeals. Light: every other purpose. n = 191
Emails per day using a matching gift, and subject lines about the election
Device: a matching gift
Subject line: the election
What each email is for, one label per email. n = 191
The one thing each email most wants you to do. Dark: asks for money. n = 191
Six of the 24 fundraising emails that arrived on Thursday, October 22. Central Time
01 · Emails, continued
Devices and feelings are softer labels than purpose and ask. Read the first two charts as a rough guide.
Emails using each persuasion device; up to three per email. n = 191
Feelings each email aims for; up to three per email. n = 191
Emails by day and hour of arrival, Central Time. n = 191
Emails per cause, for causes with 18 or more. n = 191
The date or season an email is tied to. n = 101 of 191
The voice of each email. n = 191
50%
use the reader’s first name in the text; 17 put it in the subject line, and 25 subject lines carry an emoji.
Main words, with the number of emails each one marks
Main words are the terms that make one email different from the others. Statistics, not AI.
What the trade press wrote
96 articles from 26 sector publications, more than the week before (81). Running a nonprofit led the topics again, then philanthropy and fundraising, with public policy and government funding behind. Most stories read as neutral; 16 read as negative, against 6 the week before.
Articles published per day. n = 96
Topics of the articles; up to three per article. n = 96
Tone of each article, overall and for the eight most covered topics. n = 96
Feelings each story leans on; up to four per article. n = 96
Topics, tone and feelings are an AI model’s reading. The news labels have not had our hand-labelled test yet: read them as a rough map, not as findings.
02 · News, continued
Six stories chosen by us for range, then what the news could start. Each headline opens the article.
In the order of the list, not of importance
The Agitator · Monday
The Chronicle of Philanthropy · Monday
Nonprofit Law Prof Blog · Wednesday
The Chronicle of Philanthropy · Thursday
M+R · Thursday
Media Cause · Friday
Articles per source: the seven most active of 26. n = 96
For each article the AI drafts three ideas a nonprofit could use, each with a channel. n = 288
Drafted by the AI model from one article each. We picked the six; the wording is the model’s
Send a thank-you and impact update to two-year-plus donors this quarter.
Loyal donors supply most revenue, per the cited RKD study. From an article in The Agitator.
Add a short legacy gift mention to your existing newsletter.
Uses a channel you already run at no cost. From an article in The Chronicle of Philanthropy.
Recruit board members and volunteers as ambassadors to forward campaign emails.
Ambassadors extend reach to new potential supporters. From an article by Get Fully Funded.
Run a donor-eye audit: test appeal, form, and receipt on a real phone.
Finds friction donors hit before year-end giving peaks. From an article in NonProfit PRO.
Publish a clear ‘human at the helm’ statement on how AI is used.
Addresses donor wariness with transparency. From an article in The Chronicle of Philanthropy.
Keep a December log of what broke and surprised you.
Reasons behind totals fade before budgets are set. From an article by Media Cause.
What they posted on YouTube
231 videos from 72 channels in 28 countries, in 17 languages. 77% run under three minutes and 53% are Shorts. Rädda Barnen’s song against violence toward children was watched most.
Videos posted per day. n = 231
Videos by the organization’s country. n = 231
Videos by language. n = 231
Videos by length, as YouTube reports it. n = 230; 1 scheduled video has no length yet
53%
are Shorts: 122 of 231 videos.
1:22
is the median length, in minutes and seconds.
257
views for the median video. Half had fewer.
With their views on October 26. All 231 videos together: 2,304,019. The one-line descriptions are ours.
A song of a childhood by the lake: don’t let violence take it away.
Rädda Barnen
Sweden · Video, 2:01
A bear keychain for donors to a campaign against child marriage.
World Vision Korea
South Korea · Short, 0:52
Three young adults who grew up with sponsors’ help.
World Vision Korea
South Korea · Video, 6:09
France’s 2027 budget would cut aid by 11%, the sixth cut in two years.
Action contre la Faim
France · Short, 1:00
Once helped by aid, he now runs a disaster-relief volunteer group.
World Vision Korea
South Korea · Short, 1:10
03 · Videos, continued
191 of the 231 videos had a transcript. AI asked each one five questions: who speaks, about what, why, how, and the ask.
Topics of the videos; up to three per video. n = 191
Why the video was made; a video can have several aims. n = 191
How it is told. n = 191
Who speaks. n = 191
What the video asks you to do. n = 191
Feelings each video appeals to, and how strongly. n = 191
Videos by aim (rows) and ask (columns). n = 191
The video labels have not had our hand-labelled test yet: read them as a rough map, not as findings. A video without a transcript is not labelled.
What the sector said out loud
38 new episodes from 16 shows about the sector. We read the transcripts of 31, about 12 hours of talk, and pulled out 242 statements: what hosts and guests think, recommend, learned and expect.
Problems the episodes discuss; an episode can raise several. n = 31 episodes with a transcript
Statements by kind. n = 242
Opinions by tone. n = 78
Topics of the episodes; up to four per episode. n = 31
New episodes and clips per show: the seven most active of 16. n = 38
04 · Podcasts, continued
Eight statements from the week, in our words. Code matched each one to its place in the transcript; the show’s name opens the episode.
Revenue fell about 70% in 2023. Since it began showing donors life on the ground, as a filmmaker would, it has grown 70% or more every year.
Yuriy Boyechko, Hope For Ukraine · on Nonprofit Nation with Julia Campbell
Canvassing at the zoo changed monthly giving: each new donor’s handwritten thank-you was in the mail before the shift ended, on a card of the animal they talked about.
San Diego Zoo · on Sustainable Giving
Opening a gala with a $50,000 ask from a cold stage freezes the room. His order: live auction, mission moment, then the paddle raise.
Trey Morris, Morris Auctioneers · on Hey Nonprofits!
Before building the budget, write a contingency plan: what you will do if expenses run 5% over, or growth beats the plan.
Jeremy Brown, Kari Hub · on The Nonprofit Show
Funders paying for policy advocacy, on the left and the right, have sped up polarization rather than healed it, he argues.
Daniel Stid, American Enterprise Institute · on Giving Done Right
As requests keep growing, foundations will lean more on AI to evaluate grant proposals, just as nonprofits use it to write them.
Erik Daubert · on The Fundraising School Podcast
46% of nonprofit CEOs say burnout is very much a concern, nearly double the year before. Why are we still losing this fight?
Dana Snyder · on Missions to Movements
A museum that stretched its campaign timeline, with prospects to back it, just got its largest gift ever, from a donor new to its community.
Amy Eisenstein and Andrea Kihlstedt · on All About Capital Campaigns
Statements by topic (rows) and kind (columns). n = 242
What the readings add up to
AI models did the first reading of 509 of the week’s 556 items. Side by side, the readings show what no single inbox can: a different feeling leading on each channel, two separate conversations, and two different asks.
What the readings show when they are put side by side
1
Compassion leads in the inbox (49% of emails, urgency 48%), pride in the news (46%) and hope in videos (69%). Fear and anger never pass 20%.
2
News and podcasts talk about running a nonprofit: management is a topic in 58% of articles and 68% of episodes, and in 3% of videos. The videos talk about children and health.
3
Email leads with the gift: money is the main ask in 46% of emails, and 20 subject lines offered a match. On video, 55% say “learn more” and 20% ask for a donation.
Items with their AI reading done, of the week’s items
40 had no transcript, so no labels
7 had no transcript
Code looks up every quote in the transcript; the rest are trusted less
Share of items on each topic, %. One topic list for all three sources
05 · AI research, continued
The same questions, asked of different channels. Feelings first, then what each channel asks you to do.
Share of items that aim for each feeling, on one 0–100% scale
A dot: that source’s list of feelings has no such entry. Email labels say “compassion”; news and video labels say “empathy”.
The main ask, one per email, % of emails. n = 191
Asks per video, % of videos; a video can make several. n = 191
Dark bars: asks for money. Two different samples: the emails come from 74 nonprofits we subscribed to, nearly all in the United States; the videos from 72 channels in 28 countries.
The same rules for every source
Every item of a kind gets the same questions, and each answer comes from a closed list. Labels cannot be invented, so they can be counted.
A video or episode without a transcript is not labelled. Nothing is guessed from a title.
Email labels are tested against 200 emails labelled by hand. News and video labels have not had that test yet: read them as a rough map.
About this report
So you can decide how much to trust them
Everything Giving Radar collected from Monday, October 19 to Sunday, October 25, 2026, counted by the day it arrived or was published, U.S. Central Time. Views are YouTube’s own counts, read on October 26.
We subscribe to nonprofits’ public mailing lists with a separate address for each one, since September 18, 2026. We read as a recent subscriber who has never given, so much of what arrives is a welcome series. Nearly all of this week’s emails came from organizations in the United States.
37 news feeds, 158 YouTube channels of organizations on our list, and 27 podcasts about the sector. All of it is public.
Anything with a right answer (dates, links, words, views) is counted by plain code. AI models answer fixed questions from fixed lists of answers. A video or episode without a transcript is not labelled.
Email labels are tested against 200 emails we labelled by hand; persuasion devices and feelings are softer and serve as a rough guide. The same model read this week and the week before, so the two weeks’ labels can be compared, with that care. News and video labels have not had that test yet: read them as a rough map, not as findings. Podcast statements are matched to the transcript by code; the eight shown here were found there.
No figure about one organization’s emails is published below 15 emails, so this report has none. A blank means not researched, never “no”. The full method is at givingradar.com/method.
Dates fundraisers plan around, from our Giving Calendar
The whole year is at givingradar.com/calendar.
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Giving Radar Daily is a short, free morning email for fundraisers: what nonprofits sent, said and posted the day before.