Atıf Hoca. engagement report
@ALIATIFBIR - 294K followers on X
Measured over 17 original posts from a 30-day window, last computed on October 20, 2026.
Engagement
A typical post picks up 7 interactions against 294K followers, an engagement rate of 0.002%. Measured over 17 original posts, its engagement rate beats 11% 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 4.3K times each, and 0.161% of those impressions turn into an interaction. That is about 1.48% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 60% of days. Most posts go out around 05:00 UTC, and Wednesday is the busiest day of the week. Of the 17 posts sampled, 29% carry an image or video and 18% link out. The account's strongest tracked post pulled 2.2K interactions, about 321x its own typical post.
Measured over 17 original posts from a 30-day window, last computed on October 20, 2026. Recurring tag: #galatasaraybarça.
Compared with accounts its own size
Atıf Hoca.'s engagement rate beats 11% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 9 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 9% 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 0.002%, Atıf Hoca. sits below the 10th percentile of the 205,048 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
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 05:00 UTC, and Wednesday 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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 29% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 18% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 29% 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.
- 18% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
- Its average post runs 662 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
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
- Oct 8, 2026321x their median
Patiswiss öldü bitti diye analizler uçuşuyordu, ne oldu? Hiçbir şey. Migros’ta baş köşede takılıyor. Buralardaki krizleri linçleri çok fazla ciddiye alıyorsunuz. İnsanların devasa bir çoğunluğunun olan bitenden haberi yok. Haberi olan da iki günde unutuyor. Hesabınızı buna göre yapın.
- Aug 9, 2026203x their median
Fatih Altaylı: “Özel üniversiteye verdiğiniz parayı bankada faize koysanız, 4 sene boyunca onun faizi çocuğun alacağı maaştan daha yüksek bir getiri elde ettirebilir. Fiyatlar o denli yükselmiş vaziyette. Bir ebeveynin bir çocuk için her ay yaklaşık 4 asgari ücretlik okul ödemesi gerekiyor. Hele Cumhurbaşkanı'nı dinleyip 4 çocuk yaptıysanız yandınız."
- Jun 23, 2026197x their median
İlk 2 maç sonunda dünya kupası 2026'dan elenen takımlar: Haiti, Tunus, Türkiye, Irak, Ürdün, Özbekistan.. Ligimizi bilelim🥲
- Oct 13, 202646x their median
📚 Amin Maalouf : “Nous sommes en présence de deux évolutions majeures, portées par les mêmes avancées scientifiques, et porteuses elles-mêmes de bouleversements potentiels sans aucun précédent dans l’histoire. Deux évolutions parallèles, et que l’on peut voir comme contradictoires, puisqu’elles pourraient mener notre espèce aussi bien vers son apothéose que vers son anéantissement. Et, là encore, ce ne sont pas de simples métaphores. Si notre science parvient, dans les prochaines décennies, à maîtriser le processus de vieillissement de nos tissus et de nos organes, elle sera en mesure de prolonger la vie humaine de manière significative, voire, à terme, de manière illimitée. Mais si cette même science parvient à construire des machines supérieurement intelligentes, capables de s’affranchir de la tutelle humaine, elle aura créé les conditions d’une marginalisation irrévocable de notre espèce. Nous avançons donc, d’un même mouvement, vers deux destins contradictoires : devenir les maîtres de la Création, ou tomber dans l’insignifiance.” (…) “À certains tournants de l’histoire, on a vu émerger des personnages hors pair, qui ont su s’élever au-dessus d’eux-mêmes, au-dessus de leur classe ou de leur nation, pour incarner les espérances universelles de leur temps. Aujourd’hui, on ne constate rien de tel. Quand je promène mon regard sur le monde, du nord au sud et de l’est à l’ouest, je vois peu de dirigeants remarquables, dotés d’une véritable autorité morale, porteurs d’une vision pour la terre entière, auxquels je confierais volontiers mon sort, celui des êtres qui me sont chers et celui des générations à venir. La discordance entre les rythmes d’évolution est désormais si criante qu’elle en devient caricaturale. Dans les domaines scientifiques et techniques, notre espèce est en train de bousculer toutes les barrières ; plus aucune ambition ne paraît trop grande, plus aucun objectif ne paraît impossible à atteindre. Alors que, dans la gestion des affaires de la cité, au niveau des nations comme au niveau planétaire, c’est l’incurie, l’impuissance, la régression. Demeurée prisonnière de conceptions qui ont eu autrefois leur raison d’être, mais qui sont devenues, en ce siècle, inopérantes, destructrices, et globalement suicidaires, l’humanité semble avoir atteint, dans le champ politique, son seuil d’incompétence. Ce qui menace de réduire à néant les bienfaits que lui promet la science, et d’augmenter significativement, à l’inverse, les risques de dérapage.”
- Oct 7, 202646x their median
MIT and Berkeley researchers made an AI nearly twice as good at math with one word. The word was "Okay." The paper studies base models, the raw version of an AI before it gets special reasoning training. They forced the model to start every answer with "Okay." Nothing else changed. No examples. No instructions to think. Olmo 3 7B jumped from 42% to 78% on a standard math test. The version that actually got the reasoning training scored 75%. This is not hypothetical. When OpenAI launched o1 in 2024, it said a large scale reinforcement learning algorithm "teaches the model how to think productively." DeepSeek published its R1 model in Nature, pointing to the moment it wrote "Wait, wait. Wait. That's an aha moment" as proof of "the power and beauty" of that same training. So researchers at MIT, UC Berkeley, the University of Washington and the Allen Institute for AI tested where the reasoning actually comes from. Test one: start the answer with a single word. Result: Olmo went from 42% to 78% with "Okay." Qwen3 14B went from 72% to 87% with "Alright," matching its reasoning trained version. Test two: look at what the training actually changed. Result: it made those opening words more likely, with the biggest changes in the first few words of the answer. Forcing the word recovered much of what the training added. Test three: replace "okay" with "chicken" in the training data. Result: "Chicken" became a reasoning trigger. Before the edit, starting with "Chicken" made the model invent a chicken nugget problem. After it, the model solved the math. They did the same with "step by step," swapping in "duck duck goose." "Think duck duck goose" then worked about as well as "Think step by step." The reasoning was already there. The word just unlocked it. The reason is the training data. "Okay" tends to show up in documents where someone works through a problem step by step. The model learned that pattern, and the word calls it back up. The words even steer safety. Starting with "I'm sorry" made the model refuse more requests, harmful and harmless alike. Reasoning training is not useless. But a big part of what looks like thinking is a model that learned which words come right before thinking. What else is hiding inside these models, waiting for the right first word?
- Aug 11, 202625x their median
Gazeteci Cüneyt Özdemir: “Mahalle baskısı Yeni Parti'nin rotasını belirliyor. Yeni Parti'nin tek rotası Erdoğan'ı devirmek gibi gözüküyor. Şimdiden ben söyleyeyim, böyle olmaz.” https://t.co/FLOyiJQUl6
- Sep 9, 202625x their median
Aptallığın Beş Temel Yasası: Birinci Yasa: Toplumda dolaşan aptal insanların sayısı, her zaman ve kaçınılmaz biçimde hafife alınır. İstatistiksel tahminler bu yüzden işlemez. İkinci Yasa: Aptallık evrenseldir. Belirli bir kişinin aptal olma olasılığı, o kişinin eğitim, unvan, sınıf, ırk, politik görüş veya toplumsal statü gibi diğer bütün özelliklerinden bağımsızdır. Bunların her birinin içindeki aptallık, aşağı yukarı eşit oranda ve yüksektir. Aptallığın sınırsız dolaşımıdır bu. Üçüncü Yasa (Altın Yasa): Aptal kişi, başka bir kişiye veya gruba zarar verirken kendisi de bu eylemden hiçbir kâr sağlamayan ve hatta zarar görebilen kişidir. Çaresizler (safdiller), zekiler, haydutlar da zarar verebilir ama sadece bir aptal kendi faydasına mugayir şekilde tahripkardır. Bu irrasyonalitenin, bilgisizlikle de ilgisi yoktur. Dördüncü Yasa: Aptal olmayan insanlar, aptal kişilerin yıkıcı güçlerini daima küçümserler. Aptal olmayanlar, aptal bir insanın her zaman ve her yerde her an zarar verebileceğini unuturlar. Aptallığın yol açtığı yıkım ölçüsü öngörülemezdir. Beşinci Yasa: Aptal insan, var olan en tehlikeli canlı türüdür. Bir haydut, öngörülebilir, tedbir alınabilir, onunla işbirliği yapılabilir, pazarlık edilebilir ama bir aptala karşı sadece tevekkül etmek vardır. (Carlo Cipolla)
- Aug 6, 202622x their median
The Arday affair raises an important question: Is this a one-off scandal or the tip of the iceberg? My research with Kevin speaks directly to that debate. @compactmag https://t.co/srZ8TgfHz9
- Sep 6, 20267.4x their median
Gerçekten ilginç bir tesadüfî etki olarak oksitosin, yardımseverliği -ama sadece zaten yardımsever olanlarda-artırmaktadır. Bu, testosteronun saldırganlığı sadece saldırganlık eğilimi olan insanlarda artırmasına benzer. Hormonlar nadiren bireyin bağlamı ya da çevresi dışında faaliyet gösterir... Oksitosin sizi kendinize benzeyenlere karşı daha toplum yanlısı yaparken (yani takım arkadaşlarınıza karşı) tehdit oluşturan Ötekilere karşı anlık olarak daha kötü birine dönüştürüyor. (Robert M. Sapolsky)
- Oct 13, 20265.0x their median
GS-Barselona maçına saat 18.30'da Kısıklı'dan çıkıp tam 21.30'da varabildik. Yine çok sayıda çakarlı araba öne geçti. Kimi bakan, kimi milletvekili, kimi birşeyin müdürü. Sanki biz o araçları, o korumaları onlara maça gitsinler diye vermişiz gibi..Bir de öncelik alıp önünüze geçip, sıramızı alıp başköşeye kuruluyorlar. Utanmadan sıkılmadan..Maç esnasında birileri merdivenlerde ayakta duruyor, maçı izlememi engelliyordu güvenlik görevlisine görevini yapmasını söyledim, "emniyet müdürünün koruması "nasıl sölim" dedi. Bizde kültür bu..Garip bir protokol kültürü. Garip bir devlet imkanlarını, forsunu özel işlerde kullanma, halktan kendini ayırma kültürü. Sonuç siyaset, bürokrasi el ele bir sürü halktan kopuk, çıkar ilşkilerine, zenginleşmeye dayalı olaylar. Yeni de değil 1927–1928 Yavuz–Havuz davasından beri. Yavuz zırhlısının onarımıyla ilgili usulsüzlükler nedeniyle eski Bahriye Bakanı İhsan Eryavuz yargılandı. İki yıl ağır hapse mahkum edilmiş. Cumhuriyet’in ilk Yüce Divan mahkumiyeti.Ülkemizde siyasetçi-bürokrat ikilisinin sivilleri de kullanıp bal tutunca parmaklarını yaladıkları kültürü değiştirmeli, makamını halkım aleyhine kullananlara yıllarca hapis dahil ağır cezalar getirmeliyiz. Bu arada bir atasözünün varlığı, o toplumun yolsuzluğu onayladığını göstermez. Bu sözler, kaynakları yöneten kişinin kendisine de pay ayırmasını kimi bağlamlarda olağanlaştırır, kimi bağlamlarda ise alayla eleştirir. Dolayısıyla “makamın getireceği güçle yapılan yolsuzluğa arsızlığa göz yumma yalnızca bizim kültürümüzde var” sonucunu bal tutan parmağını yalar atasözünün varlığından çıkarmak doğru olmaz. İspanyolca, italyanca, İngilizce ve Fransızcada da bal tutan parmağını yalar şeklinde özlü söz var ama bu ülkelerin ülke yolsuzluk sıralamasındaki yerlerinin bizden aşağıda olması bize bazı ipuçlarını da verebilir. Kültür değişimi şart!
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.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
Buy or sell Twitter (X) accounts - escrow-protected
PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 7 interactions against 294K followers, an engagement rate of 0.002%. Measured over 17 original posts, its engagement rate beats 11% 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 4.3K times each, and 0.161% of those impressions turn into an interaction. That is about 1.48% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 60% of days. Most posts go out around 05:00 UTC, and Wednesday is the busiest day of the week. Of the 17 posts sampled, 29% carry an image or video and 18% link out. The account's strongest tracked post pulled 2.2K interactions, about 321x its own typical post.
- What is Atıf Hoca.'s engagement rate on X?
- Atıf Hoca. (@ALIATIFBIR) has an engagement rate of 0.002%, based on the median interactions across 17 original posts from the last 30 days against 294,359 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.002%, Atıf Hoca. sits below the 10th percentile of the 205,048 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 @ALIATIFBIR have real engagement?
- Its engagement rate beats 11% 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 @ALIATIFBIR post?
- Most posts go out around 05:00 UTC, and Wednesday is its busiest day, at roughly 1.47 posts per day across the measured window.