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Dylan Ratigan engagement report

@DylanRatigan - 78K followers on X

Measured over 30 original posts from a 30-day window, last computed on October 25, 2026.

Engagement

Bottom quarter for its size
Per follower
0.012%
of 78K followers
Per impression
0.849%
1.1K views on a typical post
Reach
1.36%
of its followers see a post
Typical post
9
interactions (median)
Saved
0%
0 bookmarks on a typical post
Posting rate
2.27/day
active 17% of days
Peak time
16:00 UTC
Friday

A typical post picks up 9 interactions against 78K followers, an engagement rate of 0.012%. Measured over 30 original posts, its engagement rate beats 18% of 20,444 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 1.1K times each, and 0.849% of those impressions turn into an interaction. That is about 1.36% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, though only 17% of days saw any activity at all. Most posts go out around 16:00 UTC, and Friday is the busiest day of the week. Of the 30 posts sampled, 17% carry an image or video, 13% are part of a thread and 77% link out. The account's strongest tracked post pulled 74K interactions, about 8225x its own typical post.

Measured over 30 original posts from a 30-day window, last computed on October 25, 2026.

Compared with accounts its own size

Dylan Ratigan's engagement rate beats 18% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 6 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 35% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.012%, Dylan Ratigan sits above the 10th percentile of the 205,676 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.

p100.004%
p250.024%
p50 (median)0.143%
p750.679%
p902.56%
p9982.4%
Engagement rate as a share of followers, across the 205,676 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,897 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 percentile0.004%
25th percentile0.024%
50th percentile0.143%
75th percentile0.679%
90th percentile2.56%
99th percentile82.4%

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 16:00 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: 16:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 16:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
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 111%Busiest day: Friday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video17% of posts+111%+108% to +115%34K
Outbound link77% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 17% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 77% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
  • Its average post runs 228 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Sep 9, 20268225x their median

    We have to build AI that murders us, because if we don’t, China will build it first, and I don’t want to get murdered by a computer that speaks Chinese. That would be ridiculous.

    66K6.9K6064271.4M viewsView on X
  • Aug 20, 2026260x their median

    When I left MSNBC in 2012, I went to Louisiana to build equipment. The idea was to turn shipping containers into self-contained resource stations: solar power, water filtration, food production, and wireless connectivity. Linked together, they could form a distributed infrastructure for basic human needs. We built them in Louisiana and Texas, using local manufacturing and training returning combat veterans to deploy and maintain them. The goal was simple: replace dependency with distributed local capacity. The idea attracted attention. The New York Times wrote about it. Foundations, corporations, and governments met with us. Eventually, I presented it at the State Department. People consistently liked the concept. Meetings went well. There was interest and encouragement. But no one put it into practice. It’s possible the technology wasn’t ready or that better systems existed. That would be a reasonable explanation. What was harder to explain was the outcome: nothing. More than a decade later, global resource infrastructure still largely revolves around centralized delivery—moving water, food, power, and equipment into places —rather than enabling local production. One meeting at the State Department sticks with me and may clarified why. I presented the system: power, water, food, connectivity—local resilience, using oil industry expertise for the resource hubs and veterans as the workforce. It was met with the standard high level of enthusiasm, but with zero engagement beyond that. A state department official took me aside and explained the problem with my idea. “If we control peoples access to food, water, electricity and connectivity they need us and we can control them. If we build a distributed network of resource hubs, they don’t -- and we lose power.” That reframed everything. I had assumed the goal of aid was identical to its stated purpose: to solve the problem. By that logic, the best system would eliminate the need for itself. But institutions operate under incentives as well. A corporation gains value from being the controlling provider of resources. A charity depends on perpetuating ongoing need. A government program derives influence from administering temporary assistance that can be withdrawn as a punishment for non-compliance, Actually, solving the underlying problem -- remove the institution’s role. This doesn’t require conspiracy. It simply reflects how incentives align. It raises a question I’ve thought about since: Do our systems want to solve the problem? Often the people inside it do. But solving a problem -- and managing a problem to accumulate as much power and money as possible are not the same function.

    1.8K43111050116K viewsView on X
  • Sep 10, 2026162x their median

    Trump says tariffs will pay for the $5,000 checks. But tariffs have brought in about $140 billion extra so far, against a $1.2 trillion promise. That covers roughly eleven cents per dollar. The rest just gets borrowed. https://t.co/Xk4Gi6sAem

    1.1K33249823K viewsView on X
  • Sep 11, 202650x their median

    A key oil pipeline system in Saudi Arabia was struck by projectiles on Thursday, per two US officials. Early analysis found that pump stations, which are located next to the pipeline itself, were hit, 1 of the US officials said. It is not immediately clear who is responsible or if sections of the pipeline itself were damaged but 1 of the officials said it was struck by drones originating from Iraq. Story w/ @KatieBoLillis, @kylieatwood & @ThomasBordeaux7 https://t.co/KvsA8uBYSg

    301119822414K viewsView on X
  • Sep 10, 202647x their median

    "If some Bloomberg terminal bros are unhappy with what I'm doing, that's too bad," Bessent says on Bannon's War Room

    319442333163K viewsView on X
  • Oct 23, 202638x their median

    Absurd that there’s not personal and company liability for the people making the technology that is now capable of hacking bank accounts in the world apparently. The AI company should be held personally and as a company liable for any at all Financial losses.

    261572354.4K viewsView on X
  • Sep 8, 202630x their median

    Dylan Ratigan considers this to be the "greatest theft and cover-up in the history of the world." He explains that not only did we get an asset transfer in terms of the movement of money to the elite, but the money was then used by the elite to acquire all the broken debt prior to its reinflation by the taxpayer. Full report: https://t.co/nhhLKHQDWV

    212493558K viewsView on X
  • Sep 8, 202628x their median

    Have Your Cake and Take Mine Too There is an old expression for someone who wants an impossible bargain. They want to have their cake and eat it too. What happened in America was worse. A small group of people got to have their cake, eat it, and then take yours. That distinction matters. The great financial rescues of the past twenty years are usually discussed as arguments about economics. Should the banks have been saved? Should interest rates have gone to zero? Should the government have borrowed trillions? Should the Federal Reserve have flooded the system with money? Those are legitimate questions. But they obscure the uglier one. Who caused the problem? Who benefited from the solution? And who ultimately paid for it? I spent much of my professional life around financial markets. I knew the people making these decisions. I understood the argument in 2008: if the financial system collapsed, everyone would suffer. That was probably true. What followed was not inevitable. The institutions that had taken extraordinary risks were rescued. Their creditors were protected. Money became extraordinarily cheap. Asset prices recovered. Then they soared. Stocks went up. Bonds went up. Real estate went up. Private companies went up. If you already owned substantial assets, the rescue was magnificent. If you were a banker, an investor, a private-equity executive or simply wealthy enough to own a large portfolio, the years that followed created one of the greatest accumulations of financial wealth in modern history. Meanwhile, millions of Americans who had not designed mortgage derivatives, levered bank balance sheets or constructed the machinery that nearly collapsed the economy lost jobs, homes and savings. Then came the next rescue. More borrowing. More money. Lower rates. More support. Again, asset owners were protected first because asset markets are considered essential to the functioning of the economy. And again the bill did not disappear. Bills never disappear. They change hands. Eventually the cost arrived in the price of a house. The rent. The grocery bill. The insurance premium. The car payment. The interest rate on a credit card. The cost of starting a business. The person with $20 million in assets watched those assets become worth $30 million. The person with $20,000 in the bank watched the purchasing power of that money decline. That is not merely inequality. It is a transfer. And what makes the transfer so vicious is its direction. The people closest to the creation of the problem possessed the political access, financial sophistication and asset ownership necessary to benefit from the solution. The people furthest from the creation of the problem possessed the least protection from its cost. Think about how extraordinary that is. You make the bet. The bet goes bad. The government protects you from the loss. The rescue increases the value of what you own. Then somebody who never made the bet pays more for food, shelter and money itself. You have your cake. You eat it. Then you take theirs. We have spent years debating whether these interventions were technically necessary. Perhaps some were. But necessity does not excuse the distribution of the consequences. If society must intervene to save a system, then the people who created the danger cannot emerge richer while innocent people are handed the invoice. That is not capitalism. Capitalism requires the possibility of loss. It requires accountability. Otherwise profit belongs to the individual while failure belongs to everyone else. And eventually people notice. The bill always comes due. The real question is why we keep sending it to the people who ordered nothing. +++I write one of these every day. If you’d rather get them directly from me instead of hoping an algorithm shows them to you, subscribe free https://t.co/J9vB6ETYKV

    171473244.9K viewsView on X
  • Sep 11, 202622x their median

    I made a new video, and it's not about beating the S&P. It's about the six things I actually got from managing my own money — and none of them show up on a performance chart. If You Only Watch One Investment Video, Make It This One https://t.co/WUYJjTFqnP via @YouTube @Tony_BATtista @DylanRatigan @wearelossdog

    176164638K viewsView on X
  • Sep 11, 202621x their median

    The Pink Bicycle My Bloomberg Terminal was blinking furiously. “So much smoke… SO HOT.” “We’re trapped.” ”Go to the roof,” I said. “It’s impossible..people are jumping,” they responded. “Nobody is coming to help.” The messages were coming from friends, acquaintances and sources inside the World Trade Center. More than thirty people I knew were inside those buildings. I was sitting on the sparkling set of Bloomberg Television on the 15thfloor at 499 Park Avenue. It was the morning of September 11, 2001, shortly after 9 a.m. I was 29 years old. I was live on television. I was working with my colleagues trying to tell people what was happening. The people inside the buildings were telling me. I would look down at the terminal, read another message, absorb whatever I could and continue broadcasting. I had spent the first years of my career covering Wall Street. Bloomberg had thousands of clients across the financial industry and many inside the World Trade Center. These were traders, bankers, analysts and executives I had interviewed, called for information, met for drinks and gotten to know. Now they were writing to me from inside burning buildings. The messages became more frightened as the morning went on. Smoke. Fire. Confusion. People trapped above the impact zones. People making choices no human being should ever have to make. I had arrived at Bloomberg before 6 that morning expecting to finish the morning broadcast around 9. I stayed on the set. Imagine sitting behind a television desk trying to remain useful to the people watching while somebody you know is typing to you from inside a burning skyscraper. There were roughly thirty people I knew, in one way or another, inside those buildings who would die that morning. Then the towers came down. The messages stopped. There isn't much more to say about that moment. I had spent the years before September 11 having what I can now recognize as a golden period of in American life. New York was extraordinary. We were was young. We had a jobs we loved. We were learning every day. The city felt creative and free and slightly reckless. You could work all day, go out all night and somehow believe you hadn't missed anything the next morning. Life seemed very large. It also seemed endless. At 29, you know intellectually that people die. You don't really believe it applies to you or the people around you. September 11 ended that illusion in a few hours. But something else happened that day. New York became profoundly human. The normal machinery of the city stopped. Subways stopped.. Phones failed. Streets filled with people trying to get home. And people simply began helping one another. Cars became communal transportation. People walked together. People gave strangers food and water. Somebody had a bicycle. Somebody else needed it more. One of the most famous traders on Wall Street somehow ended up with a girl's pink banana-seat bicycle. There were no trains to Westchester. So he rode it home. One of the most powerful men in finance, pedaling north out of Manhattan on a child's pink bicycle because that was the way home. I still smile when I think about it. Nobody cared what the bicycle looked like. Nobody cared who was important. There was somewhere to go and somebody needed to get there. Bloomberg faced another problem. Many of our clients had worked in the towers. Their offices were gone. We had empty desks. So Bloomberg opened them. In the days that followed, I found myself sitting on a floor surrounded by people who had walked out of the World Trade Center. There was something strange about them. They were among the only people in New York who had not seen what happened. The rest of us had watched the airplanes hit. We had watched the fire. We had watched people fall. We had watched the towers disappear. The people who escaped had experienced it from the inside. They had simply walked down the stairs and kept walking. Then Monday came. The New York Stock Exchange was going to reopen. Getting downtown was still difficult. Lower Manhattan was restricted. Transportation wasn't functioning normally. You needed credentials to get anywhere near the Exchange. So I got a mountain bike and rose down 5th avenue at 330 am on Monday September 17. I had covered financial markets for years. I would cover them for many more. I have been on the floor of the New York Stock Exchange for crashes, rallies, panics and celebrations. Nothing felt like that Monday. The Exchange wasn't reopening because anybody particularly cared where the Dow traded. It was reopening because New York was trying to stand up. The mayor came onto the floor. People who had spent their careers shouting orders at one another stood together carrying the knowledge that friends and colleagues who had been alive the previous Monday were dead. Then the bell rang. The market opened. Life continued. The emotion was overwhelming. I stayed in New York for more than a decade after September 11. My career grew. I moved from Bloomberg to CNBC and MSNBC. I traveled. I made money. I lost things. I built things. I failed at things. I got older. But there is a clean line in my memory. New York before that morning. And New York after it. Before September 11, I thought life was primarily about what might happen next. Afterward, I understood that the fact that anything happening at all is remarkable. Twenty-five years later, what stays with me most is not the machinery that failed that morning. It is what people did when the machinery failed. The financial system couldn't help you walk home. The transportation system couldn't get you there. Your title couldn't save you. Your money couldn't help you. So people helped people. A stranger gave somebody a ride. Bloomberg gave somebody a desk. A Wall Street trader rode a girl's pink bicycle to Westchester. And people inside burning buildings sent messages to friends outside until they couldn't anymore. I have spent much of my life since then thinking about systems. Financial systems. Political systems. Media systems. Technology. September 11 taught me what sits underneath all of them. When the systems failed, human beings didn't. We build systems because they can help us live better lives. But the systems are never the point. Life is the point. For a terrible few days, nobody had to explain what had value. Money, status and all the other things we spend our lives accumulating became secondary to something much simpler. We had each other. We still do.+++I write one of these every day. If you’d rather get them directly from me instead of hoping an algorithm shows them to you, subscribe free to Dylan Ratigan on Substack.

    148251328.9K 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 9 interactions against 78K followers, an engagement rate of 0.012%. Measured over 30 original posts, its engagement rate beats 18% of 20,444 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 1.1K times each, and 0.849% of those impressions turn into an interaction. That is about 1.36% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.3 posts a day over the last 30 days, though only 17% of days saw any activity at all. Most posts go out around 16:00 UTC, and Friday is the busiest day of the week. Of the 30 posts sampled, 17% carry an image or video, 13% are part of a thread and 77% link out. The account's strongest tracked post pulled 74K interactions, about 8225x its own typical post.

What is Dylan Ratigan's engagement rate on X?
Dylan Ratigan (@DylanRatigan) has an engagement rate of 0.012%, based on the median interactions across 30 original posts from the last 30 days against 77,817 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.012%, Dylan Ratigan sits above the 10th percentile of the 205,676 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 @DylanRatigan have real engagement?
Its engagement rate beats 18% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the bottom quarter for its size group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @DylanRatigan post?
Most posts go out around 16:00 UTC, and Friday is its busiest day, at roughly 2.27 posts per day across the measured window.

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