3J's Guide to Alliteration

Apr 24, 2019 Last updated on Apr 24, 2019 Alliteration Alliteration is when you use the same initial letter or sound beginning words close together in a sentence or name. For instance: A miss is as good as a mile, Peter Parker picked a peck of pickled peppers1, Coca-Cola, or Marilyn Monroe. It’s “well known” that the names of comic-book heroes are often alliterative. The Big Bang Theory made a thing about it in one episode (S3E16). Famous examples are J. Jonah Jameson, Jr., Peter Parker, Bruce Banner2, and Steven Strange, Sorcerer Supreme. and the list goes on. Some fans have collected lists of such names at a few places. Which Marvel characters have alliterative names Which DC characters have alliterative names CBR’s alliterative names list One explanation for frequent use of alliteration comes direct from Stan Lee. Apparently, Stan Lee used alliteration particularly because he found those names more memorable. To quote: It would be hard for you to believe this, because I seem so perfect: I have the worst memory in the world, so I finally figured out, if I could give somebody a name, where the last name and the first name begin with the same letter, like Peter Parker, Bruce Banner, Matt Murdock, then if I could remember one name, it gave me a clue what the other one was, I knew it would begin with the same letter. Stan Lee, from an interview with Emmett Furey, 2006 cbr.com/... But that doesn’t explain it all. Alliteration is also common in DC-land. Superman’s girlfriends and enemies have a strange relationship with L.L., for instance: Lana Lang, Lois Lane, Lex Luther, A rumour says that Joe Shuster (creator of Superman with Jerry Seigel) had a girlfriend with the initials L.L. But the pattern sounds like it started as lark and then grew into a game (a longer discussion is here). DC didn’t just alliterate Superman’s clique. What about Wonder Woman, Captain Cold, and the Teen Titans. However a good scientist is always sceptical about received wisdom. So let’s test whether alliteration is really unusually common for superheroes. And yes, I will provide some (longish) lists of alliterative names. If you want to skip straight there follow the link, and then click on the tables for the full datasets (the second has more than 1,400 alliterative names). And I will show who uses alliteration more: Marvel or DC, and tell you which initial letters are most common for alliterative names. Analysis Data I looked at two datasets from Kaggle. The Complete Superhero Dataset, and Superheroes info and stats. Why two? Well, initially, it was actually three. The third dataset looked good, but when I started looking in detail it had some very strange entries, including many characters listed as both DC and Marvel. They aren’t (though they might look that way because of cross-over events) but we are interested in the origin of the character, not its extent. The two selected datasets both had something interesting to contribute. The first (DS 1), is a fairly short list at 743 entries, with 718 unique names. But it lists both the superhero alter ego’s name and the (full) real name (where there is one). The second dataset (DS 2) is much bigger, with well over 30,000 names, though there are more duplicates (because the same character appears in different guises or different alternative universes). However, each entry contains only one name, and some characters such as Spider-Man show up separately as Peter Parker, while others appear only under one guise. So it is larger, but not as clean. Both datasets come as CSV files (which is typical at Kaggle), and so were easy to input into Julia as described in this post, so I won’t include my own links to datasets, except to note that the second had several files: the one being used here is called superheroes_info.csv. One caveat: I don’t know how these were collected, so they may have sampling biases. In particular, as we will mention later, the sampling process may prefer alliterative names, at least in the case of the smaller dataset. Code We first remove names that include (small) Roman numerals – these are repeated versions of the same character and aren’t helpful. We also remove duplicate names that arise because of different variants of the same character. Then each name is tested by a small function that does a few things: It removes bracketed pieces (these seem to be comments rather than part of the name); It splits the name into component “words” around spaces or hyphens3; It discards empty pieces and stop words (e.g., “of”); and It finds the first letter of each word, the initial. If the name has 2 words, we insist they both start with the same letter4. If three, we insist that at least two match, and if 4 then we insist at least 3 match. If there are 5 or more words, we don’t consider it (these are rare, and tend to be complicated). using StatsBase # for 'countmap' using Languages # for stop words function alliterate(s::AbstractString) s = replace( s, r"$$.*$$" => "" ) words = split( s, r"[\s-]+" ) i = 1 sw = Set(stopwords(Languages.English())) while i <= length(words) if in(words[i], sw) deleteat!(words, i) end i += 1 end initials = [ words[i][1] for i in findall(length.(words) .> 0) ] f_n = length(initials) if f_n <= 1 return (false, f_n, f_n, ' ') end d = countmap(initials) mx = findmax(d) # mx[1] is the freq. of the most common letter, and mx[2] the letter if f_n == 2 && mx[1] == 2 result = true elseif f_n == 3 && mx[1] >= 2 result = true elseif f_n == 4 && mx[1] >= 3 result = true else result = false end return (result, f_n, mx[1], mx[2]) end Just a little translation of the Julia code seems warranted. The literals r"$$.*$$" and r"[\s-]+" are regular expressions. Julia allows extra codes at the start of the definition of string-like literals. In this case the “r” specifies it is a regular expression. The expression [ words[i][1] for i in findall(length.(words) .> 0) ] is a list comprehension; a syntactic device for creating new lists or arrays without writing out a cumbersome loop. It’s an approach that looks a little more like the way a mathematician would specify a set. We are using it here to get the first letter of each non-trivial element of the array words. Julia’s comprehensions are reasonably vanilla (compared to Haskell et al.) so I won’t go into more detail here. The function returns a quad-tuple containing true/false as well as the number of words (minus stop words), the maximum number of duplicates and the most common letter. Tuples in Julia are very like immutable arrays, though specified with round brackets. We can index into them just as for arrays to get values out, but we can’t change the array. Mostly they seem to be used to pass input and output arguments from functions. We don’t actually need the brackets in the return syntax, but it makes the elements being returned look a little more obvious to me. The key output is the true/false value telling us whether the name is alliterative. We use this as a filter on the names in the data frame and Bob Banner’s your uncle2. Results The simple things first. The proportion of alliterative names in datasets 1 and 2 (DS 1 and DS 2, respectively) are in the following table. dataset proportion of alliterative names DS 1 (full name) 8.6% DS 1 (hero name) 6.6% DS 2 6.4% The first is a little different from the other two. The first is based on the characters “real” name and the second on their hero alter ego’s name. The names in DS 2 are a mix, but often are their real name. So we might expect DS 2 to look more like the DS 1 full name list. The reason it doesn’t is that the “full names” often include middle names and extensions that are not usually used (and don’t appear in DS 2). For instance, I never knew that Peter Parker is sometimes listed as “Peter Benjamin Parker”, Stephen Strange is sometimes “Stephen Vincent Strange”, and and J. Jonah Jameson sometimes has “Jr.” tacked on the end. Because we allow that a three-word name is alliterative if only two initials match, and the longer names allow more matches (we’ll show this mathematically in the next section) and thus the full name version has more matches. Otherwise the datasets are remarkably close. Here’s the start of the lists. For DS 1 there are 94 heroes with either an alliterative hero name or full name. Name Full name Creator 3-D Man Charles Chandler Marvel Absorbing Man Carl Creel Marvel Animal Man Bernhard Baker DC Beak Barnell Bohusk Marvel Beta Ray Bill Beta Ray Bill (translation of his Korbinite name) Marvel Big Man Frederick Foswell Marvel Bird-Man Henry Hawk Marvel and DS 2, for which we have 1442 names Name Publisher Reed Richards Marvel Scott Summers Marvel Susan Storm Marvel Matthew Murdock Marvel Stephen Strange Marvel John Jonah Jameson Marvel James Buchanan Barnes Marvel You can get the data from my GitHub by clicking on the tables. The files also provide the publisher of the respective heroes’ comics. So a simple question we might ask is “did Stan Lee set a trend in Marvel that led to an unusually high proportion of alliterative names?” The following plot compares DC and Marvel using the two datasets. The obvious conclusion is that alliterative names are more common in DC-land than Marvel-land. gd = (function() { var WIDTH_IN_PERCENT_OF_PARENT = 70; var HEIGHT_IN_PERCENT_OF_PARENT = 50; var gd = Plotly.d3.select('article') .append('div').attr("id", "c7df98c5-a5af-41cc-9b43-16958b187a13") .style({ width: WIDTH_IN_PERCENT_OF_PARENT + '%', 'margin-left': (100 - WIDTH_IN_PERCENT_OF_PARENT) / 2 + '%', height: HEIGHT_IN_PERCENT_OF_PARENT + 'vh', 'margin-top': 0 }) .node(); var plot_json = {"layout":{"xaxis":{"titlefont":{"color":"rgb(0, 0, 0)","size":18},"tickfont":{"color":"rgb(0, 0, 0)","size":18},"title":"Publisher"},"paper_bgcolor":"rgba(0,0,0,0)","bargap":0.15,"legend":{"bordercolor":"rgba(255, 255, 255, 0)","y":1.0,"font":{"size":20},"bgcolor":"rgba(255, 255, 255, 0)","x":1.0},"barmode":"group","margin":10,"plot_bgcolor":"rgba(0,0,0,0)","yaxis":{"titlefont":{"color":"rgb(0, 0, 0)","size":18},"tickfont":{"color":"rgb(0, 0, 0)","size":18},"title":"Proportions (%)"},"bargroupgap":0.1},"data":[{"marker":{"color":{"color":"orange","opacity":0.5}},"y":[6.077348066298343,9.375],"type":"bar","name":"DS 1","opacity":0.7,"x":["Marvel","DC"]},{"marker":{"color":{"color":"orange","opacity":0.5}},"y":[5.907394066409064,7.85092408736826],"type":"bar","name":"DS 2","opacity":0.7,"x":["Marvel","DC"]}]}; var data = plot_json.data; var layout = plot_json.layout; Plotly.newPlot(gd, data, layout); window.onresize = function() { Plotly.Plots.resize(gd); }; return gd; })(); Stan Lee had a tremendous impact on comics. Maybe he indirectly influenced DC as well? That brings us back to the main question; the one I asked at the start was “is alliteration unusually common for superheroes?” We have numbers now, but we don’t really have any means of interpreting them. We can compare Marvel and DC, but is six and a bit percent a big number or not? What about 8? It seems significant, but not huge, however the only way to really understand it is to compare to names in the real world. Too much data science relies on cramming a bunch of data through some code without having any baseline idea of what to expect. So that’s what we will will look at next. How common are alliterative names in the real world? Real-world name data Ideally, we would start with a list of all names (in some region and time period) and check them all for alliteration. Lists exist (e.g., telephone books), but it’s surprisingly hard to get hold of them. Most name lists that are available freely are of baby names (first names) and a few have surnames (family names) but the lists with both together are hidden behind user interfaces that make getting a good sample difficult. See this link for some discussion. What I could get was from a blog titled “Two-Letter initials: Which are the most common?” It’s pretty close to what we want. It’s based on a real dataset from 4,502 employees of SAS (the company). The dataset has the number of each pair of initials. We have an alliterative name if the two match, e.g., J.J. The blog has some nice visualisations, but doesn’t explicitly calculate the alliterative percentage: I make it that 6.2% of pairs correspond to an alliterative name. The results seem to match up really very well with the Marvel-v-DC plot above. DC is a little higher (7.8% vs 6.2%), but Marvel is spot on, despite Stan Lee’s reputation for alliteration. So it seems the question might be answered, i.e. alliteration isn’t much more common in comics than in the real world. However the data only contains 2-letter initials and it isn’t huge. While 4,502 names may seem like a large dataset there are 676 possible pairs of initials so some possible pairings have a very small number of data points. We need a way to explore the possible bias in the data, and to extend it to longer names. The chance of a match We can explore the biases in the data using a more artificial approach. It won’t be as realistic, but it provides a reference point we will call a null-model. Let’s assume the letters of names are distributed in the same manner as letters in a wider corpus of English, and that first and family names are chosen independently. These are big assumptions as they presume that parents don’t select either for or against alliteration, but this is a null-model and not a perfect representation of reality. Naively, one might assume that the probability of getting the same letter for the second name is just 1 in 26 because there are 26 letters. But actually, the probability depends strongly on the distribution of letters. Imagine that the letter Q was chosen 9 times out of 10 as a first letter, then there would be a probability of 0.81 of getting Q.Q. Already this is much higher, without any chance of any other pairing, so we need to understand letter frequencies. Letter frequencies for English have often been compiled. However we don’t want the standard generic letter frequency. We want the frequencies of first letters or initials, which are quite different. Lists of first-letter frequencies for English are also easily available though. Then we have to do a little calculation. A little bit of probability is a good thing for a data-scientist to have in their swag of tools. So let’s work through it. In the lingo of probability, think of the initial letter of each name as a random variable (or RV) $$\displaystyle X_i$$ where i is the index into the list of words in a name. We assume that the RVs are IID (Independent and Identically Distributed). They aren’t, but we’re modelling them that way. We use an abbreviation for probabilities: $p_x = Prob( X_i = x ).$ Then we use the Law of Total Probability to write what we want (the probability the two random variables are the same) as a set of sub-events, one for each initial letter. $\begin{array}{rcl} Prob( X_1 = X_2 ) & = & \sum_x Prob( X_2=x | X_1=x ) Prob( X_1 = x ) \\ & = & \sum_x Prob( X_2=x ) Prob( X_1 = x ) \\ & = & \sum_x p_x^2, \end{array}$ where we write $$\displaystyle Prob( X_2=x | X_1=x )$$ to mean the conditional probability that the second initial is x, given the first one. And we drop the conditioning because the two random variables are independent. If all of the probabilities are the same, i.e., $$\displaystyle p_x = 1/26$$ then this gives the intuitive probability of a match of 1 in 26. However, now we can calculate the probability the two initials are the same given any frequency of initial letters. When we make the calculation for English word initials we get 7.4%. This is a little higher than the estimate from the data above. It is suggestive that parents are slightly averse to alliterative names. It’s also closer to DC’s percentage than to Marvel’s (for the larger dataset, DS 2). The advantage of this number over the real data is we are no longer limited by the small data we have, but also we can now generalise this to consider the probability that three-word names are alliterative. Three-name Monty We don’t have data for three-word names, but the null-model given above isn’t miles off for two-word names, so we’ll try to push it a little more. I called a three-word name alliterative if two initials matched. Calculating this probability is a little harder, but not much. The main point to get your head around is that probabilities don’t simply add. So when we calculate the probability that at least one pair of three initials matches, we add the probabilities of each possible matching pair, but then have to subtract the part that has been over-counted. We can visualise it as a Venn diagram. The event that a pair matches is the intersection of a pair of circles, but if we add the areas of each of these regions we triple count the centre region, so we have to subtract that away. Hence, the calculation is $\begin{array}{ll} & Prob( X_1 = X_2 \; or \; X_2 = X_3 \; or \; X_1 = X_3) \\ & = Prob( X_1 = X_2) + Prob( X_2 = X_3) + Prob( X_1 = X_3) - 2 Prob( X_1 = X_2 = X_3 ) \\ & = 3 \sum_x p_x^2 - 2 \sum_x p_x^3, \end{array}$ where the second summation is derived using conditional probability in the same way we derived the first. When we calculate this using the first letter frequencies we get 20.1% as the estimate of the proportion of alliterative names, which is much higher than for two-word names. So if the dataset has a high proportion of three-word names, it should have a higher percentage of alliterative names. If we extend this to three matching initials for four-word names we get 2.7%, and for five and more word names it drops to being insignificant (hence the cut off we used earlier). Comparison So, now we can finally compare the numbers we see in our calculation with the numbers in the real data. The confusing factor is that the numbers in the data are a mix-up of names composed of different numbers of words. So let’s separate out the different cases and take a look in the following plot. Note that the real names bar only appears for 2-word names, because we only have data for these. gd = (function() { var WIDTH_IN_PERCENT_OF_PARENT = 90; var HEIGHT_IN_PERCENT_OF_PARENT = 35; var gd = Plotly.d3.select('article') .append('div').attr("id", "b5b17384-8031-4a0a-9992-45c56a2d94fa") .style({ width: WIDTH_IN_PERCENT_OF_PARENT + '%', 'margin-left': (100 - WIDTH_IN_PERCENT_OF_PARENT) / 2 + '%', height: HEIGHT_IN_PERCENT_OF_PARENT + 'vh', 'margin-top': 0 }) .node(); var plot_json = {"layout":{"xaxis":{"titlefont":{"color":"rgb(0, 0, 0)","size":18},"tickfont":{"color":"rgb(0, 0, 0)","size":18},"title":"probability of alliteration"},"paper_bgcolor":"rgba(0,0,0,0)","bargap":0.15,"legend":{"bordercolor":"rgba(255, 255, 255, 0)","y":1.0,"bgcolor":"rgba(255, 255, 255, 0)","x":1.0},"barmode":"group","margin":{"l":50,"b":50,"r":50,"t":60},"plot_bgcolor":"rgba(0,0,0,0)","yaxis":{"titlefont":{"color":"rgb(0, 0, 0)","size":18},"tickfont":{"color":"rgb(0, 0, 0)","size":18},"title":"number of words","autotick":false,"autorange":"reversed"},"bargroupgap":0.1},"data":[{"marker":{"color":{"color":"orange","opacity":0.5}},"y":[1,2,3,4],"type":"bar","name":"Real (2-word) names","opacity":0.7,"orientation":"h","x":[0.0,0.06197245668600004,0.0,0.0,0.0]},{"marker":{"color":{"color":"orange","opacity":0.5}},"y":[1,2,3,4],"type":"bar","name":"Null-model","opacity":0.7,"orientation":"h","x":[0.0,0.07351885990000002,0.20538173449562608,0.027484489337271226]},{"marker":{"color":{"color":"orange","opacity":0.5}},"y":[1,2,3,4],"type":"bar","name":"Measured proportion (DS 1)","opacity":0.7,"text":[0,76,27,0],"orientation":"h","x":[0.0,0.1310344827586207,0.17532467532467533,0.0]},{"marker":{"color":{"color":"orange","opacity":0.5}},"y":[1,2,3,4],"type":"bar","name":"Measured proportion (DS 2)","opacity":0.7,"text":[0,1251,186,4],"orientation":"h","x":[0.0,0.09377108162806386,0.20350109409190373,0.04]}]}; var data = plot_json.data; var layout = plot_json.layout; Plotly.newPlot(gd, data, layout); window.onresize = function() { Plotly.Plots.resize(gd); }; return gd; })(); And the results are interesting. First, for two-word names there does seem to be an increase in alliteration, though we can’t pay too much attention to the smaller dataset here because it could easily be innately biased towards alliterative names. However we do see a small increase in alliteration in DS 2, compared to the real initials data and the null-model. However that isn’t so evident in 3-word names. There the null-model (remember we don’t have real data for 3-word names) matches unreasonably well with DS 2, and DS 1 is actually lower. The 4-word case appears to match reasonably, but it is based on very small numbers so we won’t say too much about that. Overall, the results aren’t as strongly suggestive of alliteration in superhero names as I had initially expected, given the received wisdom. I’ll talk about that a little more below, but first, a couple of little side questions “what letter starts an alliterative name most commonly, and is it different for superheroes compared to the real world?” What are the most common alliterative letters? The following plot shows the top-10 letters that are most common, and compares that to the null-model and the two-word name data. gd = (function() { var WIDTH_IN_PERCENT_OF_PARENT = 90; var HEIGHT_IN_PERCENT_OF_PARENT = 60; var gd = Plotly.d3.select('article') .append('div').attr("id", "3dfaee1c-d0fa-4190-ae1d-7b7b09005796") .style({ width: WIDTH_IN_PERCENT_OF_PARENT + '%', 'margin-left': (100 - WIDTH_IN_PERCENT_OF_PARENT) / 2 + '%', height: HEIGHT_IN_PERCENT_OF_PARENT + 'vh', 'margin-top': 0 }) .node(); var plot_json = {"layout":{"xaxis":{"titlefont":{"color":"rgb(0, 0, 0)","size":18},"tickfont":{"color":"rgb(0, 0, 0)","size":18},"title":"number"},"paper_bgcolor":"rgba(0,0,0,0)","bargap":0.15,"legend":{"bordercolor":"rgba(255, 255, 255, 0)","y":1.0,"bgcolor":"rgba(255, 255, 255, 0)","x":1.0},"barmode":"group","margin":{"l":50,"b":50,"r":50,"t":60},"plot_bgcolor":"rgba(0,0,0,0)","yaxis":{"titlefont":{"color":"rgb(0, 0, 0)","size":18},"tickfont":{"color":"rgb(0, 0, 0)","size":18},"title":"repeated initial letter","autotick":false,"autorange":"reversed"},"bargroupgap":0.1},"data":[{"marker":{"color":{"color":"orange","opacity":0.5}},"y":["m","s","b","c","d","r","a","j","g","h"],"type":"bar","name":"DS 2","opacity":0.7,"orientation":"h","x":[14.850798056904928,10.340041637751561,8.535739070090216,7.356002775850104,7.078417765440666,5.412907702984039,5.135322692574601,5.065926439972242,4.441360166551006,4.302567661346288]},{"marker":{"color":{"color":"orange","opacity":0.5}},"y":["m","s","b","c","d","r","a","j","g","h"],"type":"bar","name":"Alliterative real (2-word) names","opacity":0.7,"orientation":"h","x":[13.978494623655907,15.053763440860207,9.677419354838705,9.677419354838705,6.810035842831777,6.09318996361983,2.867383512006928,6.451612903225803,3.584229391218875,2.150537634408601]},{"marker":{"color":{"color":"orange","opacity":0.5}},"y":["m","s","b","c","d","r","a","j","g","h"],"type":"bar","name":"Null-model","opacity":0.7,"orientation":"h","x":[3.826,6.686,4.434,5.238,3.174,2.826,11.682,0.511,1.642,4.2]}]}; var data = plot_json.data; var layout = plot_json.layout; Plotly.newPlot(gd, data, layout); window.onresize = function() { Plotly.Plots.resize(gd); }; return gd; })(); We see firstly that the dataset and real-world names are far away from the null-model, so we will discount the null-model for the purpose of considering the most common alliterative letter. There are some similarities between real-world and superhero alliterative letters. The letter ’M’ is at the top of the superhero list, and it has almost the same frequency as in the real data. The two lists also share the same top-5 letters in almost the same order. However, there are also some differences. Alliterative superhero names are quite a lot less likely to start with ’S’ than real names and more likely to begin with ‘A’ or ‘H’. These, however, seem to be quirks, and the general trend is that alliteration in superhero names follows a similar pattern to that in the real world. Once again, that suggest that there isn’t anything super-special about the use of alliterative names for superheroes. Alliteration revisited Alliteration is a little more common for superhero names than in the real world. I had expected it to be much more common, based on what I thought I knew. There certainly are many characters from comics with alliterative names, but the proportion isn’t excessive. We can hardly accuse Stan Lee and his colleagues of paroemion (overuse of alliteration). Then why do we perceive alliterative names as so common? Stan Lee had the right idea, but he didn’t go far enough. Alliteration isn’t just a memory aid for him. And it isn’t just about remembering the second name (e.g., Parker comes after Peter). It’s about remembering the character wholesale. The comic universe has literally tens of thousands of characters. It fits the loads and loads of characters trope. The characters can tend to blur into one and another. In an invented universe with so many characters who do we latch onto? Who do we remember? The answer is that we remember the character with the memorable name. That could be because the name is easy (e.g., Superman) or exciting (e.g., Wolverine) or just because it’s alliterative. Memorability is why alliterative names have often been used as stage names. It’s why alliterative phrases are used in advertising (e.g. “Maybe it’s …” – you fill in the rest). It’s why alliterative aphorisms survive (“A miss is as good as a mile” makes much less sense than “An ynche in a misse is as good as an ell” but it is more memorable). Shakespeare and Dickens did it with flair, as have a thousand other authors. Comic-book authors are no different. All importantly alliterations stick in our mind better. So it is natural that we perceive alliteration as more common than it is. So my answer is that the data shows that alliteration isn’t as overwhelmingly common in the superhero realm as we might think, but the characters with alliterative names stick in the memory. Probably that’s why some are more successful as well, leading to them being more prominent, and this also enhances the perception that superheros have alliterative names. Why is alliteration so memorable to us? That’s a question that is a little too deep for me. At least for today! And of course, all of the discussion and data above is oriented towards the English language and Western comics. We have yet to discover how universal alliteration is. Footnotes: Forgive the minor name change for obvious reasons. ↩ The Hulk's real name is "Robert Bruce Banner" because Stan got it wrong (he wrote Bob Banner in one issue) and changed the name afterwards to fit. But everyone I know calls him Bruce6. ↩ The definition of alliteration doesn't usually specify how hyphens are treated. Here we treat hyphenated names as two words, but acknowledge that this is somewhat arbitrary. For instance, hyphenation isn't always standard. Often a name such as "Spider-Man" will be commonly written "Spiderman". So constructed names that are unhyphenated, e.g., "Daredevil" might also be considered alliteration, thanks to the conjunction of dare and devil. Alliteration is about the sound, and in this sense "Dare Devil" sounds alliterative. We have no hope of picking all of these cases here though. N.B. Daredevil does make his way into our list, but only because "Matt Murdock" is alliterative also. ↩ "Clark Kent" is alliterative. Alliteration allows for the same sound to be repeated, not just the same letter. It's a lot harder, however, to look for the same sound at the start of words than the same letter. To do so, we would be delving into phonetics. ↩ We dropped repeats from the list. This is problematic: sometimes repeats occur because of "reboots" or alternative universe versions of characters. There is no need to include these more than once. However in other cases the repeated character refers to someone else taking the same role (Batman's Robin, for instance). ↩ Just a little reference to They call me Bruce. It's not about the Hulk. ↩

alliteration julia marvel dc

Aleph-Zero-Heros

This is a blog about (mathematical) data science of large-scale hybrid narratives and superheros in particular.

The title of the blog comes from a perhaps obscure piece of mathematics: aleph zero is (mathematically speaking) the cardinality of the set of natural numbers, which is a pretty mathsy way of saying “infinity”. So the name, aleph-zero-heros, loosely translates as infinite heroes.

The blog is going to be focused on superhero narratives, hence the title, but it is broader than just that. We’re going to look at large-scale hybrid narratives. A hybrid narrative (to me) is a story (fiction or non-fiction) that involves multiple forms of media. We can get an idea by considering simple examples of old-school hybrids

• picture books and graphic novels