a picture is worth a thousand words

Posts Tagged ‘words’

Trump on his Tweets

I was curious about what kind of words did Trump used in his polemical Tweets. With the help of http://www.trumptwitterarchive.com/ I was able to collect about 30.000 Trump Tweets and Retweets, from 2009 to 2017.

I counted words and keep only these said two or more times (also removed some I found less relevant like “Trump”, “but”, “we” or “I”). I got more than 10.300 different words.

Then spread these words, with sizes based in repetition, over Trump portrait. It mean each word has a proportional size based in word repetition. He said 4182 word “Thanks”, 4135 word “Great”, and so on…

And here you have the result!. The man on his Tweets.

Another sample.


Lord of the Words


This man killed thousand Orcs with his own hands. Well, with is hands and one pen.

My little tribute to JRR Tolkien the man who created a Universe from scratch. For this illustration I counted more relevant words from Lord of the Rings and spread over author’s portrait. I know it’s probably more popular book from Tolkien but I really love the beginning of The Silmarillion and his kind of Genesis… wonderful!

Vector file here: JO-D-151218-Tolkien01



Butterfly Experiment


I had recently the opportunity to create something for great institution Bridgend Women’s Aid, who I really thanks for choosing me, and did a lot of experiments with a butterfly image, changing colors patterns and randomizing it.

They finally used another incredible photo, I really like more than my crazy stuff, as base for my typo work. But I don’t want to waste my experiments so I thought it could be a good idea to share it with you.

I created different version with words in English and Spanish.

Multicolor Butterfly English Words
Multicolor Butterfly Spanish Words
Blue Butterfly English Words
Blue Butterfly Spanish Words

I choose words I found related to butterfly. But after a while I realized these words fit with life itself, and it’s probably the reason why butterfly concept is relevant for Bridgend Women’s Aid. May be not, but it makes me wonder how human brain connect dots to find something behind… and it’s something I always wanted in my illustrations.

Being honest I have to say my favourite are most abstract ones. I mean so abstract that you are not able to identify the image behind it and words are some kind of clues to find main subject. Should be only after a long time, may be third time you look at the image, when you brain find the pattern behind it and really see what it is.

Like a surprise wrapped with words.

Think about this cropped version for a big wall, like the background of a restaurant or something like that… do you think you will be able to see what it is at first sight?


Probably not so difficult with this one but if you like it I promise to came back with more.

Oh, somebody ask for my vector pdf versions. Yes I’m still working in a vector base and I think I should share more with you.





My little tribute to one of the most beautiful cities in the world, but at the same time one of the craziest. I did a little family trip last April and it was a great experience… unforgettable pizza at da Baffetto!

Illustration composed with my personal selection of words related to Rome.

Gabriel García Márquez



My little tribute to Gabriel García Márquez. In fact I had been working in this illustration for a while but never found time enought to end it. Sadly I think this moment finally arrived.

I count words in 28 Gabriel’s books and did some illustrations with words size proportional to repetition.

Books analyzed: Cien Años De Soledad, Cronica De Una Muerte Anunciada, Del Amor Y Otros Demonios, Doce Cuentos Peregrinos, El Ahogado Mas Hermoso Del Mundo, El Amor En Los Tiempos Del Colera, El Avion De La Bella Durmiente, El Coronel No Tiene Quien Le Escriba, El Otoño Del Patriarca, El Rastro De Tu Sangre En La Nieve, El Ultimo Viaje Del Buque Fantasma, En Agosto Nos Vemos, Espantos De Agosto, La Candida Erendira Y Su Abuela Desalmada, La Hojarasca, La Luz Es Como El Agua, La Santa, La Siesta De Los Martes, Memorias De Mis Putas Tristes, Muerte Constante Mas Alla Del Amor, Noticia De Un Secuestro, Ojos De Perro Azul, Relato De Un Naufrago, Solo Vine A Llamar Por Telefono, Tramontana, Un Dia De Estos, Un Senor Muy Viejo Con Unas Alas Enormes, Vivir Para Contarla.

Most used words (beggining of the list): casa (2986),sentía (1614),noche (1580),tiempo (1527),parecía (1509),muerte (1499),llevaba (1491),hombre (1449),podía (1411),volvió (1370),primera (1367),llegó (1274),mar (1261),encontró (1242),vida (1232),quedó (1225),sala (1221),pensar (1208),nunca (1111),vivir (1107),mientras (1087),siempre (1087),mano (1082),puerta (1051),tarde (1039),único (1033),decir (1021),nadie (1019),dejó (1004),madre (993),aquella (990),niña (983),padre (983),esperaba (978),tratando (973),cuenta (937),hijo (905),último (890),buen (888),modo (887),hablar (871),miró (859),preguntó (855),empezó (851),conocía (830),mujer (821),Aureliano (814),terminó (805),amor (799),bien (782),coronel (782),mundo (782),aquel (774),medio (774),pues (753),debía (728),dio (725),visto (722),dormir (718),ningún (716),embargo (703),orden (703),cuarto (700),pudo (693),calle (692),vio (688),veces (681),cosas (670),mal (661),ojos (659)…

Most used two relevant words combo: Florentino Ariza (565),Fermina Daza (411),José Arcadio (390),Aureliano Buendía (213),Aureliano Segundo (210),coronel Aureliano (209),dio cuenta (185),Sierva María (183),Arcadio Buendía (173),día siguiente (169),Santiago Nasar (158),Juvenal Urbino (143),mismo tiempo (139),doctor Urbino (134),muchos años (125),tres días (120),dijo ella (119),mucho tiempo (112),muchas veces (112),Nena Daconte (111),doctor Juvenal (109),aquella noche (105),ella misma (102),varias veces (100),Bendición Alvarado (95),dos años (93),Billy Sánchez (89),dijo la mujer (88),Amaranta Úrsula (86),poco a poco (86),veinte años (86),aquel día (85),Arcadio Segundo (84),dos veces (84),estaba seguro (83),tres meses (79),dos días (78),dos horas (78),dijo el coronel (76),Pablo Escobar (74),di cuenta (73),Lorenzo Daza (70),Gerineldo Márquez (69),llamó la atención (69),seis meses (69),media noche (68)…

Nice words by the way!

More Samples:



Bye Gabo, thank you for doing spanish language even more beautiful.


Charles Dickens

There is a lot of people celebrating two hundred years of Charles Dickens and I also want to do a little tribute to this great English writer.

I took more than fifty books and novels by Dickens (including the most iconic like Oliver Twist, David Copperfield, Great Expectations, A Tale of Two Cities and A Christmas Carol) and counted the words. I spread the words over two different portraits images.

The first words in the list. oliver (868),upon (755),replied (548),bumble (399),gentleman (366),lady (359),sikes (357),dear (330),jew (325),fagin (317),sir (314),away (298),another (275),without (253),woman (229),poor (204),window (202),shall (192),heart (185),quite (181),child (177),arm (172),brownlow (167),something (166),returned (164),doctor (161),master (161),manner (160),whether (156),moment (153),observed (152),seen (150),london (149),sat (149),indeed (147),present (147),office (145),rather (145),bill (144),speak (143),expression (139)…

The illustrations represents the single word counting, but I also counted two and three words combo. If you are curious the three words combo list start with: gentleman in the white waistcoat (13),poor mercantile jack (11),waiter who ought to wait upon (8),great salt lake (7),queer small boy (7),spoken young man (7),well spoken young (7),french flemish country (6),titbull’s aims houses (6)…

Another portrait.

Did you know that his life is probably most challenging that any of his novel histories. Visit his Wikipedia article to know more about this incredible man.

In Obama’s Words

This is not my first post about Barack Obama. One of my first words illustration was done in 2009 by using all the Obama speeches in the run for president. Nice words for people, I have to say. He became president in January 2009 and the thing got serious.

It has been nearly three years and now we can analyze the words he used in his speeches throughout this time. Nicely people at washingtonpost.com has around 800 Obama speeches ready to be read (also, I have taken the title!) . It was not an easy task because I had to isolate Obama words from all the recorded text, most of them contains words from other people and press questions, etc… I sorted all the speeches because I want to know the word’s evolution in time.

With all these ‘cleaned’ speeches, and using my own tools, I did three different kind of analysis. First I counted the most used words, and also the combo of two and three (significant) words. Also I looked at word’s evolution in time from January 2009 to October 2011.  And finally I looked at the words analyzing distance from others.

Most used single words: going (10817),make (8059),people (7843),can (6912),just (6511),work (6368),know (6165),now (6036),want (6013),get (5620),got (5290),right (5149),years (5148),american (5028),jobs (4789),think (4705),one (4615),time (4520),country (4457),america (4078),states (3879),like (3680),thank (3677),need (3536),help (3491),businesses (3305),things (3275),also (3265),take (3215),way (3199),well (3163),new (3159),sure (3094),say (3064),back (2980),economy (2957),health (2905),come (2898),care (2749),nation (2701),good (2624),united (2565),world (2565),every (2562),everybody (2521),look (2502),families (2469),see (2464),said (2460),tax (2414)…

Two words combo: make sure (2700),united states (2302),health care (1864),right now (1204),small businesses (1143),american people (1122),thank you very much (840),middle class (766),clean energy (739),tax cuts (693),across the country (680),health insurance (645),young people (629),states of america (621),long term (619),two years (516),$ billion (508),god bless (491),want to thank (484),want to make (481),white house (478),around the world (477),move forward (476),insurance companies (475),men and women (462),years ago (457),st century (452),create jobs (450),last year (435),going to make (401),just want (401),private sector (392),can make (367),wall street (366),work hard (366),every day (357),work together (356),one of the things (352),next year (344),recovery act (341),tax breaks (337)…

Three words combo: united states of america (618),want to make sure (302),middle class families (292),health care system (251),health care reform (231),small business owners (227),bless the united states (185),god bless the united (183),health care costs (182),since the great depression (169),got to make sure (166),big round of applause (164),people back to work (150),last two years (144),right here in the united states (138),private sector jobs (128),put people back (124),going to make sure (120),president of the united states (117),investment in clean energy (114),give them a big round (110),cost of health care (106),every single day (106),make sure that we’ve got (106),live within our means (103),can make sure (102),giving tax breaks (97),dependence on foreign oil (96)…

The leading illustration was done by spreading the most commonly used words over a portrait of Barack Obama.

In the next graph you can see the evolution of the first 100 words in time, and the combo of two and three words. Click to enlarge, but if you want to see a high resolution go to this Zoom.it link.

The next group of illustrations represents words close to the main word in all the speeches. So the bigger the word is the more times it appears close to the main word. The distance to the main word is the average distance of all the instances of that word. There is no relationship between each illustration, so you have to look at each one as independent single analysis. Main words sorted alphabetically.

And my word’s trees. The bigger are the most commonly used. You can find the single words tree and the two and three words combo trees. There is a high-res image of the single words tree uploaded at Zoom.it .



I personally have drawn interesting conclusions about the use of words. But I prefer that each draw their own conclusions. Here there is only data… in a visual way.

Words that kill

Agatha Christie is probably the best crime writer ever. She wrote more than ninety books, but only 82 as Agatha Christie. For this illustration I counted the words of 65 of those books. Including her most famous like “Ten Little Niggers” and “Murder on the Orient Express”. She also have a long list of movie and television adaptations. Take a look at her wikipedia article.

She used the word “murder” only 4158 times… not too bad!.

Most commonly used words: poirot (13236), mrs (10845), mr (9100), quite (7388), something (6469), really (6288), sir (5243), rather (4883), woman (4431), anything (4394), doctor (4167), murder (4158), suppose (3856), away (3611), inspector (3531), marple (3390), lady (3273), moment (3261), believe (3210), dear (2729), suddenly (2719), case (2704), tuppence (2609), matter (2549), smiled (2523), killed (2421), shall (2373), someone (2367), seen (2336), understand (2302), death (2240), wife (2201), nice (2150), dead (2107), felt (2042), died (2036), tommy (2005), nodded (1985), another (1964), police (1940), sort (1927), everything (1917), husband (1902), shook (1897), married (1896), bit (1881), natural (1855), else (1849), oliver (1823), exactly (1822)…

The battle between Hercule Poirot and Miss Marple has a clear winner. Poirot has more than quad hits than Miss Marple.

I’m working in a new implementation of my word-counting tool. The target is to be able to count words combo, instead of single words. I will publish someday a whole analysis, but I want to share some of my first results with you. For these 65 books one of the most used four (representative) words combo are “miss marple shook her head”. And the three words combo winner is “said miss marple” with 1217 hits.

Another illustration. This time the words sizes are proportional to it’s repetition.

Lost, the words they said

This visualization represents the words used by each character of the popular tv show Lost. The size of the words are proportional to the number of times the character used it. The word position and the distance to the character are mostly random.

I don’t like remakes. I prefer to waste my time in new ideas. But I’m going to do an exception with this post. First because when I did it, the show wasn’t end so It was unfinished… somehow. And second because I think that Lost deserve it. I know Lost end received a lot of critiques (I share some of them) but I really enjoyed all the time I spend watching Jack, Ben and Locke making the rounds.

In addition to adding the final season information, I have taken the opportunity to improve the visualization. Remember I know the words each character said, exactly, thanks to the scripts hosted in lostpedia.wikia.com.

Yo can take a look at my original post talking about this image and others here.

Click in the image to see a high resolution version of the visualization. But if you want to see it in his full glory go to this link in Zoom.it.

Words Grow

What if words could grow as trees?. A while ago I did a visualization with the words of all Obama speeches, growing as a tree. I’ve updated my code and tested it with some of the words image I’ve published along the time.

Here you have my huge collection of words trees. The first one represents the Darwin book “On the Origin of Species”.

All these trees have a post reference and represent the most used words of a book, a speech or lyrics. The size of the words are proportional to the number of times the word appears in these books or speeches.

Think on it as a step after the word image creation. Because the words colours of these trees was taken from the colours these words have in the image composition. it’s like a combination between the original words analysis and the image colours.

All the images comes from vector based pdfs.

The most commonly used words of all Lorca poems.

The most commonly used words of all ACDC lyrics.

The most commonly used words of The Bible (New Testament).

The most commonly used words of The Bible (Old Testament).

The most commonly used words of all Bob Dylan Lyrics/a>.

The most commonly used words of all Bruce Springsteen lyrics.

The most commonly used words of the Che Guevara book “Diarios de Motocicleta”.

The most commonly used words of all Miguel Delibes books.

The most commonly used words of messages to Dennis Hopper.

The most commonly used words of all Iron Maiden lyrics.

The most commonly used words of all Gilmore’s Girls shows.

The most commonly used words of all House MD shows.

The most commonly used words of all reactions to the Sara and Iker kiss.

The most commonly used words of words from Jesus.

The most commonly used words of all Juan Carlos speeches.

The most commonly used words of all Nirvana lyrics.

The most commonly used words of all reactions after the dead of Leslie Nielsen.

The most commonly used words of John Maeda book “Laws of Simplicity”.

The most commonly used words of all Marillion lyrics.

The most commonly used words of all Queen lyrics.

The most commonly used words of all messages to Michael Jackson after his dead.

The most commonly used words of all Radio Head lyrics.

The most commonly used words of all Rollig Stones lyrics.

The most commonly used words of all TED talks speeches.