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John Burn-Murdoch engagement report

@jburnmurdoch - 475K followers on X

Measured over 1 original post from a 30-day window, last computed on August 28, 2026.

Engagement

Per follower
0.639%
of 475K followers
Per impression
0.327%
928K views on a typical post
Reach
195.2%
of its followers see a post
Typical post
3.0K
interactions (median)
Saved
0.149%
1.4K bookmarks on a typical post
Posting rate
0.37/day
active 20% of days
Peak time
13:00 UTC
Friday

Early reading. We have captured 1 original post for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.

A typical post picks up 3.0K interactions against 475K followers, an engagement rate of 0.639%. Posts are seen about 928K times each, and 0.327% of those impressions turn into an interaction. That is about 195.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.37 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 13:00 UTC, and Friday is the busiest day of the week. Of the 1 posts sampled, 100% carry an image or video, 100% are part of a thread and 100% link out. The account's strongest tracked post pulled 55K interactions, about 18x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

Measured over 1 original post from a 30-day window, last computed on August 28, 2026.

Where this sits in the catalog

At 0.639%, John Burn-Murdoch sits above the 75th percentile of the 36,261 accounts in this comparison. That places it in the top 25% band, which runs 0.431% to 2.10%.

p100.002%
p250.012%
p50 (median)0.08%
p750.431%
p902.10%
p99160.7%
Engagement rate as a share of followers, across the 36,261 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 107,137 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.002%
25th percentile0.012%
50th percentile0.08%
75th percentile0.431%
90th percentile2.10%
99th percentile160.7%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 13:00 UTC, and Friday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 13:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 6%Busiest hour: 13:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%50K
01:00 UTC-2%51K
02:00 UTC-3%50K
03:00 UTC-4%53K
04:00 UTC-6%43K
05:00 UTC-4%42K
06:00 UTC-4%48K
07:00 UTC-5%51K
08:00 UTC-4%60K
09:00 UTC-3%69K
10:00 UTC-2%71K
11:00 UTC-3%78K
12:00 UTC-2%86K
13:00 UTC-2%93K
14:00 UTC-3%96K
15:00 UTC-2%100K
16:00 UTC-3%97K
17:00 UTC-2%90K
18:00 UTC-1%84K
19:00 UTC-2%79K
20:00 UTC-1%74K
21:00 UTC-1%66K
22:00 UTC-2%57K
23:00 UTC-2%51K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Friday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 5%Busiest day: Friday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+4%229K
Monday0%284K
Tuesday-2%273K
Wednesday-1%250K
Thursday-2%243K
Friday-3%251K
Saturday+3%226K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Best tweets

  • Mar 31, 202318x their median

    NEW: I’m not sure people fully appreciate how dire the US life expectancy / mortality situation has got. My column: https://t.co/dBIhT9eZLv And some utterly damning charts. 1) at *every* point on the income distribution, Americans live shorter lives than the English. https://t.co/pOoziEK5mZ

    38K12K1.1K4.0K14M viewsView on X
  • May 16, 20262.0x their median

    No smoking gun, but the preponderance of evidence points to smartphones, not economics, as the culprit for the global drop in fertility: • In the US and UK, births fell first and fastest in areas that got 4G earliest • Birth rates were stable in the US, UK and Australia until 2007; in France and Poland until 2009; in Mexico and Indonesia until 2012; in Ghana, Nigeria and Senegal until 2013-15 Each of these inflection points matches local smartphone adoption (see picture). • The younger the age group, the sharper the drop. • in-person socialising among young adults is dropping. In SK, by 50% in 20 years • Sexual dysfunction is higher among heavy social media user • Effect is largest in culturally traditional societies — Middle East, Latin America, sub-Saharan Africa • Decline holds across countries hit hard by GFC 2008 and those not hit, fast-growing and not growing. Excellent again @jburnmurdoch. https://t.co/RYEMXD2bRM

    4.5K1.0K3052871.4M viewsView on X
  • May 18, 20261.7x their median

    Smartphones are not the explanation for the recent decline in fertility. Instead, they are an accelerator of deeper forces already at work. Let’s start with the facts. Fertility is falling almost everywhere: in rich, middle-income, and poor countries; in secular and religious countries; and in countries with high and low levels of gender equality. The decline accelerated around 2014. So, no country-specific explanation will work unless you are willing to believe that 200 distinct country-specific explanations arrived at roughly the same time. Smartphones look like the obvious candidate: the first iPhone was released in 2007, and global adoption has been astonishingly fast. Economists understand the first major decline in fertility in advanced economies, from 6 or 7 children per woman throughout most of human history to about 1.8, that occurred between the early 1800s and roughly 1970, well before smartphones. The main drivers were a sharp fall in child mortality (effective fertility was rarely above 3 and often close to 2) and the shift from a low-skill, rural agrarian economy to a high-skill, urban industrial one. We have quantitative models that fit these facts well. Country-specific factors mattered too, of course. Proximity to low-fertility neighbors accelerated Hungary’s decline, while fragmented landowning structures accelerated France’s. But these were second-order mechanisms. This is also why most economists long considered Paul Ehrlich’s doom scenarios implausible. We forecast that fertility in middle- and low-income economies would follow the same path as in the rich, probably faster, because reductions in child mortality reached India or Africa at lower income levels (medical technology is nearly universal, and most gains come from handwashing and cheap antibiotics, not Mayo Clinic-level care). Much of what we see in Africa or parts of Latin America today is still that old story. But in the 1980s, a new pattern appeared. Japan and Italy fell below 1.8, the level we had thought was the new floor. By 1990, Japan was at 1.54 and Italy at 1.36. This second fertility decline began in Japan and Italy earlier than elsewhere, driven by country-specific factors, but the underlying dynamics were widespread: secularization, an education arms race, expensive housing, the dissolution of old social networks, and the shift to a service economy in which women’s bargaining power within the household is higher. The U.S. lagged because secularization came later, suburban housing remained relatively cheap, and African American fertility was still high. U.S. demographic patterns are exceptional and skew how academics (most of whom are in the U.S.) and the New York Times see the world. My best guess is that, without smartphones, Italy’s 2025 fertility rate would be about 1.24 rather than 1.14. I doubt anyone will document an effect larger than 0.1-0.2. Italy was at 1.19 in 1995, not far from today’s 1.14. The TFR is cyclical due to tempo effects, so I do not read too much into the rise between 1995 and 2007 or the decline from 1.27 in 2019 to 1.14 today. The direct effect of smartphones is not zero, but it is not, by itself, that large. Where social media, in general, and smartphones, in particular, matter is in the diffusion of social norms. What would have taken 25 years now happens in 10. Social media are not the cause of fertility decline; modernity is. But they are a very fast accelerator. That is why social media are a major part of the story behind Guatemala (yes, Guatemala) going from 3.8 children per woman in 2005 to 1.9 in 2025. Without them, Guatemala would also have reached 1.9, just 20 years later. Modernity, in its current form, is incompatible with replacement-level fertility. By modernity, I do not mean capitalism: fertility fell earlier and faster in socialist economies than in market economies. Socialist Hungary fell below replacement in 1960, and socialist Czechoslovakia in 1966 (both experienced small, short-lived baby booms in the mid-1970s). By modernity, I mean a society organized around rational, large-scale systems and formalized knowledge. Countries will not converge to the same fertility rate. East Asia is likely stuck near 1, possibly below, given its unbalanced gender norms and toxic education systems. Latin America faces the same gender problem plus weak growth prospects, so I expect something around 1.2. Northern Europe has more egalitarian family structures and might hold near 1.5. The very religious societies are probably the only ones that will sustain 1.8. All of this could change with AI or changes in population composition. We will see. But on the current evidence, deep sub-replacement fertility is the “new new normal.” Unless we reorganize our societies, better learn to handle it as best we can.

    3.8K926227229917K viewsView on X
  • Jul 31, 2026

    New from me: Until recently, the gap between comfortably-off and just-getting-started could plausibly be crossed in a decade or two of hard work. Growth in asset prices means that’s no longer true. My column on moving from income world to wealth world: https://t.co/UDmzA8tmPA https://t.co/qA3DrR7QZI

    2.4K43186112928K viewsView on X
  • Jun 21, 2026

    There is only one “change” that will work for Burnham. A genuine, relentless focus on growth. Two decades without earnings growth. That’s why electorate is fed up. Only growth will repair contract between generations and allow social ills to be tackled https://t.co/2H68i2d0JG

    1.1K17418356287K viewsView on X
  • May 12, 2026

    Today will be dominated by UK political ructions but this economic growth report “An Honest Day” (also out this morning) by @LabourGrowth @MarkMcvitie is a serious piece of work. The current leadership - or any aspirants - should give it a proper read. Easier to co-opt these pro-growth positions whilst in opposition, the second best time is now: https://t.co/alWS1v24Cc

    17737119100K viewsView on X
  • Apr 27, 2026

    The FT @FinancialTimes is hiring a new data journalist to join our US data and visuals team in New York or DC. Great job, great team, great place to work. Apply here 👉 https://t.co/Gct6nUljFT

    136643154K viewsView on X
  • May 28, 2026

    New from me: I had the pleasure of sitting in as host of BBC’s Radical podcast this week, and had a fascinating conversation with leading geneticist Sir John Bell, where we explored the truly astonishing medical breakthroughs of recent (and coming) years https://t.co/tiay8Kv0Jb

    921310043K viewsView on X
  • May 17, 2026

    Science nerds of X: Nxt wk I’m interviewing Prof. Sir John Bell about recent medical breakthroughs, touching on - Gene therapies & cancer vaccines - Ethics & regulation - Impacts on society This includes a Q&A where I can field your questions. If you have any please post below!

    30313023K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

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Reading these numbers

A typical post picks up 3.0K interactions against 475K followers, an engagement rate of 0.639%. Posts are seen about 928K times each, and 0.327% of those impressions turn into an interaction. That is about 195.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.37 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 13:00 UTC, and Friday is the busiest day of the week. Of the 1 posts sampled, 100% carry an image or video, 100% are part of a thread and 100% link out. The account's strongest tracked post pulled 55K interactions, about 18x its own typical post. Only 1 original post have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

What is John Burn-Murdoch's engagement rate on X?
John Burn-Murdoch (@jburnmurdoch) has an engagement rate of 0.639%, based on the median interactions across 1 original posts from the last 30 days against 475,113 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.639%, John Burn-Murdoch sits above the 75th percentile of the 36,261 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @jburnmurdoch have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @jburnmurdoch post?
Most posts go out around 13:00 UTC, and Friday is its busiest day, at roughly 0.37 posts per day across the measured window.

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