=Paper=
{{Paper
|id=Vol-2655/paper26
|storemode=property
|title=Structural Onomatologic for Username Generation: A Partial Account
|pdfUrl=https://ceur-ws.org/Vol-2655/paper26.pdf
|volume=Vol-2655
|authors=Francisco Supino Marcondes, Jose Joao Almeida, Paulo Novais
|dblpUrl=https://dblp.org/rec/conf/ecai/MarcondesAN20
}}
==Structural Onomatologic for Username Generation: A Partial Account==
https://ceur-ws.org/Vol-2655/paper26.pdf
Figure 1: Instances of account managers username suggestions provided by Twitter, Goggle and Facebook.
A search on ACM DL did not returned any relevant literature. The query (username AND generat*) (NOT
(username OR generat*)) retrieved 7,049 results in January, 2020. By reading the titles and abstracts of the
100 most relevant papers (as classified by the ACM DL), only [4] draws near to this paper research problem. Nev-
ertheless, it aims to generate pronounceable random words and not human appealing usernames. The forward
snowballing over [4] also did not also retrieved any relevant literature. The literature profile correlates with
account managers state of the art (as in figure 1) suggesting this subject as quite unexplored. Most of retrieved
papers, such as [7], aims to extract information such as gender and language from nicknames or to meet a same
person on several social media [11, 16].
The need for username generation is due to the fact that the most common usernames were already taken.
The difficulty then is to generating human likable usernames that were not yet used.
Username in Twitter is called handle being also a Twitter URI. This means that it is possible to verify a Twitter
handle by fetching the URL https://twitter.com/handle (returning the 404 error the username is considered
available). This checking readiness is the main reason for focusing this paper on Twitter, yet, presumably, the
same results can be generalized for other the social-media.
In addition, for this paper only American names were considered, therefore it cannot be expected to reach
similar results outside of this scope. Whereas there could be universal heuristics, presumably most of them are
related to a language and a culture, especially from a structural perspective. For instance, the use of diminutive
for nickname formation is common for several cultures including Latin (Chico in Portuguese), Saxon (Franky in
English) and Oriental (Furan-kun in Japanese), however, the different structure requires particular heuristics.
This paper does not also consider the structural difference between male and female nicknames. It is possible
to guess that Franky stands for Frank and Frankie for Francine but for properly assess such structures a data-
set like LDC2012T11 cf. [3] should be further explored considering the weighted relation between gender and
nickname structure. Additionally, even the used data-sets providing popularity weight information, they are
not considered in this paper. Finally, name order for composed nicknames or the prevalence of name/surname
derived nicknames is also not considered.
2 Pseudonym, Nicknames and Usernames
Nicknames and usernames are pseudonym types distinct to each other [13] shaping its own onomastic category
[1]. The username relates to the nickname in the sense that both share etymological motivations and they
depart from each other as nicknames result from interaction whereas usernames are demanded to participate
within a community. Also, like given names and ”proper” pseudonyms, usernames are chosen but different from
them it must be unique. In addition, usernames do not necessarily refer to a person but also to an idea or an
account content i.e., usernames are not necessarily an anthroponym. By not being necessarily anthroponym, the
username research should focus more on structure than in semantics [5]. The structural approach suits usernames
because they can be considered as linguistic exceptions [6] as they may never say aloud or be part of a syntactic
context; they are not committed to grammar and orthography rules (including gender distinctions) [13].
A Twitter username1 is case-insensitive alpha-numeric 4 to 15 char length2 string in the form
username::=[a-z|0-9| ]. With 300+ million active users [12] a major problem is to find a suitable unique
1 https://help.twitter.com/en/managing-your-account/twitter-username-rules, fetched in Jan. 2020
2 In the Twitter rules it is written that the handle is between 1-15, however it only accepts new usernames between 4 and 15.
This is, probably, for avoiding username squatting i.e., the act of selling social media accounts with associated earned value that
had created a black-market for rare handles. For a reference, it was offered around $50,000 for the username @N in 2014 [10]; lesser
rare handles, up to three letters, were traded by couple hundred dollars [2]. Currently, a Twitter account in the black-market vary
from couple cents to dozens of dollars due to followers’ number, SMS verified and account age.
username that is still available without resorting to numbers and non-name elements. Highlight that Twitter
holds both username and nickname, this paper focus on the username. In addition, there is an important ono-
mastic distinction between Google and Twitter/Facebook usernames as reveled in 1. For creating a Google
account, the user must define its own username that is unchangeable afterward. Twitter and Facebook use a
different strategy, they set a generic username through a simple heuristic and assign it to the new account; if the
user wants, the username can be changed afterward (to the expense of losing all linked references [9]).
It must then to be verified the relation between nicknames and Twitter handles. For verifying this relation,
given that Twitter’s active users are around 300Mi in 2020 [12] a sample of 30k random nicknames was retrieved
from LDC’s Nicknames data-set [3] and checked against Twitter’s user-base for availability. The results are that
1763 (5.87% of the sample) of plain nicknames are available in Twitter. This result suggests that nicknames
are being widely used as handles in Twitter, therefore, using structural nickname formation strategies suits this
paper intent on generating name-based usernames and strengthens the idea that nicknames and usernames are
structurally related.
3 Structural Heuristics for Nickname Generation
The name formation uses two data sets, one first-names3 another with last-names4 . For a reference, by joining
one first name with one last name (f irst × last) it is possible to generate 1, 68923e10 names. It is also common
for occidental names to be formed by composed names and with two or three surnames, increasing the amount.
Certainly, there are “common names” that most of the generated usernames will be already taken. The
idea is to verify if there are a name combination and nickname heuristics that are more likely to be avail-
able as a handle on Twitter. Therefore all names were considered within the same probability. The name
builder has a function signature as buildName(gender, compound:Boolean, surnames:Integer):Name. The com-
mand generateName(random.choice([‘Male’,‘Female’]), random.choice([True,False]), random.randint(1,3))
was then used for building the sample.
Then, given a name, the username generation starts. As presented in table 1a there are several elements that
can be used for composing a username. This paper focuses on generating usernames based on personal names
as elements. When usernames are formed upon personal names, they share the same structure and rules of a
nickname [13] getting into a form like [5].
Structurally, a nickname is formed by abbreviation, modification or name portions, however, there are also
nicknames without any clear formation rule. For the first case scenario, a set of heuristics based on structural
onomastics can be used for generating nicknames. For the second case scenario, a data-set based approach
must be used. Fortunately, this second scenario matches with contracted nicknames and then implements the
contraction heuristic. A straightforward and convenient structural onomastic typology for nickname formation
was found in [15] and adapted as depicted in table 2 guiding the heuristic development. A graphical description
of the way that these heuristics relates to themselves are presented in figure 1b. For this paper it was possible
for formulating suitable, human likeable, heuristics for Separation, Portions, Initials, Contraction, Diminutive
and Fancy but not for Swapping, Phonetic, Dropping and Combination. The focus is given for the first group.
Any word generator may unexpectedly produce bad-words and this is not an exception. For an instance,
names such as Analee and Nazifa may produce bad words by picking the first four letters. For handling this
problem a blacklist5 is used as a strategy, even not being the perfect solution is aids on avoiding, at least, the
most scandalous situations. It must be stressed that username suggestions are supposed to be presented privately
for each user, therefore, even names in the “gray area” may be presented being up to the human to choose it or
not. This will vary according to each user’s personality. Therefore, all generations are filtered for bad-words.
Finally, for limitations, the lack of papers that were found for this subject results in a preliminary set of
heuristic rules that must be further developed until they can reach some maturity. The proposed heuristic for
this paper is suitable yet not fully developed in the sense that every time a new adjustment emerges. For an
instance, a rule picking the three first letters of name with the pattern consonant-vowel-vowel (CVV) suits for
GIO[vanni] but not for JOA[n], then, for this paper, this rule was dropped yet a “smarter” rule may be conceived
on future works. Nevertheless, some heuristics overlaps filling some gaps of each other, for instance, the gap left
by CVV rule dropped from the heuristic on the portion heuristics is mostly filled by the separation. Therefore,
understanding how the presented heuristics interact with themselves is also important for this paper.
3 104,110 names from the US Social Security without duplicated names https://www.kaggle.com/kaggle/us-baby-names/version/2
4 162,254 surnames from the US 2010 https://data.world/uscensusbureau/frequently-occurring-surnames-from-the-census-2010
5 1704 English bad-words https://www.freewebheaders.com/full-list-of-bad-words-banned-by-google/
Element Type Characteristics Instance Elem. Example
Titles Tendency to appear first, be dr, ms, just, real justKrista
followed by a personal name.
Personal names Tendency to appear first, be chris, mike chirsAdams
followed by a surname.
Determiners Tendency to appear first, be the, that, big bigJoe
followed by a noun.
a) Person. pronoun Tendency to appear first, be i, your, my iDrinkOJ b)
followed by a verb.
Org. suffixes Tendency to appear last, fol- uk, news girlAtNY
lowing an organisation name.
Circumflexes Tendency to appear both first x, xo, xOliviax
and last, adjacent to a name.
Number Tendency to appear last, fol- (all numbers) Johnson78
lowing a personal name.
Table 1: a) Typology of common elements present in usernames cf. [5]. The highlighted row shows this paper’s
focus. b) A heuristic formulation for structural onomastic nickname formulation based on rules presented in
table 2 (the number within each circle is the ‘L.’). A nickname may stop at each level or may proceed to the
next for encompassing more features. The Fancy rule-set as a special case was not included in this picture.
I. L. Heuristic Example Description
1 Initials ZS from Zachary Smith The first letter of each name.
1 Portions Liz from Elizabeth A nickname may come from the front, end or middle of name.
1 Separation Mary-Ann from Maryann If a name is a composition of two other names then split.
1 Contraction Ike from Eisenhower Ad hoc formation, usually due to socio-historical circumstances.
× 2 Swapping Bill from Will Swap letters for the first letter of a name portion.
× 2 Phonetic Bob from Robert Like swapping but based on the phonetic structure.
2 Diminutive Charlie from Charles Include terminations such as -EE or -Y in a name portion.
× 3 Dropping Fanny from Frances Dropping such as R or H within consonant compounds.
× 3 Combination Miz from Mary Elizabeth A combination of the nicknames of a compound name.
* Fancy Markus or from Mark Creative possibilities, a general heuristic cannot be envisioned.
Table 2: Nickname formation rule-set adapted from [15]. ‘I’ shows the heuristics discussed in this paper. ‘L’
is the heuristic’s transformation level, for an instance, a diminutive (level 2 heuristic) suits better to a name
portion (level 1) than to a name (level 0), see table 1b for reference. The Fancy Rule Name is highly ad hoc
being able to encompass several heuristic possibilities, therefore it may be placed on any level according to the
defined heuristics and may involve some other typologies as presented in 1a.
Name Portions. Name portion-based nicknames can emerge from the front of a name, from its back and
from its middle. Most of these formations can be reduced into a letter trinomial composed by vowels (V) and
consonants (C), such as, for the name, CHARLES the front trinomial is CCV, the middle CVV, and the back
CVC (for name generation purposes the letter ‘Y’ is considered a vowel). Eventually, the fourth and fifth letters
must be also considered, such as on CCC formations, as for the front of CHRISTINE. For the middle-name
portion, good heuristics could not be conceived for this paper. There were then created 32 rules except for those
that did not suit after a brief “human likability” inspection (that were dropped). Follows a code snippet:
t r i n o m i a l = name [ : 3 ]
i f isCVV ( t r i n o m i a l ) :
c a n d i d a t e s . append ( name [ : 2 ] ) # JO [ an ]
# c a n d i d a t e s . append ( name [ : 3 ] ) # JOA[ n ] <−− DROPPED
i f isVCC ( t r i n o m i a l ) :
...
i f len ( name ) > 3 and i s V o w e l ( name [ 3 ] ) : c a n d i d a t e s . append ( name [ : 4 ] ) # ALLI [ s s o n ]
Name Separation. It is common to a compound name to become a name such as Mary Ann becoming
Maryann that can be easily re-splat when a nickname is emerging. Therefore, structurally, name separation
is looking for inner-names within a name in the form innernames.append([n for n in dataset if n in name]).
Name separation and portions are qualitatively distinct yet structurally similar (a separation is a portion of a
name). On average, separations produces 2 ± 2 names that were not within portions against 7 ± 3 that are. This
suggests that these heuristics have a high structural relation yet important qualitative differences. Nevertheless,
in practice, these heuristic names can be used interchangeably.
Name Contraction. There are two possibilities for name contractions, one is a result from a socio-historical
process such as Greta from Margaret, another one is a result of portion, swapping and dropping letters combi-
nation such as Mike from Michael. Therefore, for this paper, name contraction is used for denoting nicknames
whose formation rule cannot be properly determined from a structural perspective. Therefore, for handling
contraction a bag-of-words strategy will be used. Among a set of evaluated nickname data-sets (only five were
found) the American English Nickname Collection (LDC2012T11) [3] presents the higher rate of non-obvious
nicknames, being then chosen. Highlight that data-set is copyrighted and cannot be freely distributed. Also, it
presents some compound nicknames such as Johnny Boy from John (being the space replaced with an underscore)
and it may present quite uncommon nicknames such as James from Monroe.
For understanding the benefit of using that approach, the other heuristics proposed in this paper were used
for generating nicknames and compared to the nicknames proposed by the data-set, [i for i in h if i in c]
where c ∈ Contraction and h ∈ Heuristic). In short, the other heuristics do not over-set this one, meaning
that it provides a set of non-obvious nicknames adding value when joined with the other heuristics for username
formation. As a result, whereas portion and separations holds a high structural relation between each other,
contraction does not.
Name Diminutive. The strategy is to include the terminals -Y, -IE, -EY, -EE, -IN and -KIN at the end of
names, also, by doubling consonants that are not H or R and replacing C and Q with K (becoming -KY, -KIE,
etc.). A caution to be taken is with names ending on I or Y as it would become something like -IY or -YEY
(some of these terminations would suits as fancy name yet not for this one). For this heuristics, it was defined
40 rules. Being a level two ruleset, it suits better for name portions than to full-names. For instance, Burnam
as full-name results in Burnamy and Burnamey whereas as portions becomes Burny and Burney.
i f name [ − 1 ] == ’ i ’ or name [ − 1 ] == ’ y ’ :
c a n d i d a t e s . append ( name [ : len ( name)−1]+ ’ k i n ’ )
i f name [ − 2 ] != ’ y ’ and name [ − 2 ] != ’ i ’ :
c a n d i d a t e s . append ( name [ : len ( name)−1]+ ’ e e ’ )
...
i f i s C o n s o n a n t ( name [ − 2 ] ) and name [ − 2 ] != name [ − 3 ] and name [ − 2 ] != ’ r ’ and name [ − 2 ] != ’ h ’ :
c a n d i d a t e s . append ( name [ : len ( name)−1]+name[ −2]+name [ − 1 ] )
Fancy Names. There is not a “correct” set of fancy variations for nickname formation, in short, creativity
is limited by human likability. Therefore a strict research on fancy username generation involves getting people
feedback (out of scope for this paper). For a proof of concept, three rule types is being used. The first is fancy
characters, such as the use of “ ” for creating nicknames like MARY and M A R Y and letter replacing such as ‘3’
for ‘E’ and ‘4’ for ‘FOR’, e.g. R3DFORD and RED4D. The second is “foreignnessization” by including letters such as
‘H’ and ‘G’ after the last name vowel for strength and ‘US’ and ‘UM’ for “latinization”. The third is based on
the repetition of name portions such as JAJA from Janet and CICI from C[hr]ISTINE. These rules are extremely
ad hoc, for this paper, there were defined 26 of them, for a snippet:
c a n d i d a t e s . append ( ’ ’ . j o i n ( [ c+ ’ ’ f o r c in name ] ) [ : − 1 ] )
i f ’ f o r ’ in name : c a n d i d a t e s . append ( name . r e p l a c e ( ’ f o r ’ , ’ 4 ’ ) )
...
i f name [ − 1 ] in vowel :
i f name [ − 2 ] != ’ u ’ : c a n d i d a t e s . append ( name [ : len ( name)−1]+ ’ us ’ )
...
i f isVCV ( t r i n o m i a l ) : c a n d i d a t e s . append ( ( name [ 1 : 3 ] + name [ 1 : 3 ] ) . c a p i t a l i z e ( ) )
3.1 Nickname Evaluation
For internal evaluation, it was built a name sample with 168922 (0.00001% of the population). The idea is to
verify how many nicknames, on average, each generator creates. The results are presented in table 3.
Considering a total of 91 nicknames and an availability rate on Twitter of 5.87% (see section 2), the estimation
is that, for each name, it is possible to find around 5 nicknames. This is a quite narrow margin to work with.
Then the topic must be further explored. The probability for a not available username varies according to the
range of people that they encompass. For instance, a handle such as F encompasses, at least, all names starting
with ‘F’ whereas FSMarcondes is quite less embracing. In short, as general a nickname is, less probable to be
available due to its naming scope. For numbers, table 4 shows the availability rate in Twitter for the proposed
heuristics. Ba aware that table data may be a little biased due to Twitter’s policies changes over the time. For
instance, it is not currently possible to set a handle less than 4 characters2 yet these existing accounts may still
be suspended and removed1 , therefore initials availability may raised.
Sample
Nickname Generator Avail.
AVG ± Max Min
Name 3 1 5 2 0.47%
Initials 3 1 5 2 3.85%
Portions 18 5 31 2 4.23%
Separation 9 3 26 0 1.25%
a) Contraction 4 5 40 0 5.87%
b)
Diminutive 24 10 63 0 26.89%
Fancy 39 13 81 6 59.27%
Sub-Total 1 (raw) 101 29 197 24 -
Sub-Total 2 (no rep.) 92 26 177 23 -
Total (no bad words) 91 26 177 23 -
Table 3: a) Descriptive statistics for the proposed nickname generators. The initials depicts the shape of built
names. For a proper benchmark, all strategies received the same name data-set (even higher level heuristics
such as diminutive). Sub-Total 1 is the summation of all generated nicknames and Sub-Total 2 excludes
repetitions form the summation and Total filters for bad-words. It is important to highlight that bad-word
filtering reduced one nickname on average and then is significant. For the availability, for each heuristic, a
non-repeating random sample of 30k nicknames was gathered and checked against Twitter’s for availability. b)
The nickname generators data plotted.
Another availability variable is the appeal, i.e., how a likable a nickname is. Table 4 reveals that the most
appealing username is the proper name itself, followed by separations that is also mostly proper names. These
is followed by initials, portions and contractions. Curiously, diminutive is not a high appealing username yet is
a highly appealing nickname, supposedly, it is not expected to a person to call himself with a diminutive; fancy
result on the other hand was expected, presumably, due to its ad-hocness. Therefore, on the other hand, as more
specific is a nickname more likely to be available.
4 Username Composition and Suggestion Ranking
A common feature on usernames is to compound a nickname with a name (e.g. BillGates) or with another
nickname (e.g. JLo). This brings products into handle generation, therefore, it is possible, on average, to
produce 8281 (91 × 91 from table 3) username suggestions for each name; ranging from 529 to 31329 variations.
For assessing the availability for compositions as such, the products were generated for each pair of heuristic
resulting into sets composed by elements formed as < h1 >< h2 > and < h2 >< h1 >, where h1 and h2 are two
heuristics. From each set, a subset with 30k (around (0.00001% of Twitters active users cf. [12]) of non-repeating
nicknames within a length of 4 ≤ x ≤ 15 were randomly selected and checked over Twitter in March 2020; the
results are presented in table 4a.
Table 4a shows that usernames with initials tends to be more appealing, supporting the idea that smaller
handles are preferred to longer ones, except, as shown in table 4c, that the longer handle is a personal name.
This means that EDijk is a more appealing username than EdsgerDijkstra, yet it is as appealing as Edsger or
Dijkstra. The diminutive and fancy heuristics followed the pattern raised in table 3.
As for appealing, the difference between name and name-name availability, may be explained by considering
that repetition for the former requires a two-name homonym. Also, it must be considered the difference between
“common” and “rare” names and sociological issues in creating names such as marriage, e.g. it is more likely
to exist a German-German name than to a German-Japanese, therefore names such BernonKoyama tends to be
less common whereas splitting them, they tend to be equally common. In short, since this paper sample is
artificial, it may be “sociologically biased” and further studies should be performed with an actual name sample
for refining the name-name availability.
For a suggestion ranking, the results gathered in tables 3 and 4a can be normalized and used as an appeal index
(depicted in 4c). According to the proposed index, as higher is the appeal of a generator, higher is the human
likability potential, yet, also, that the “best” usernames are probably already be taken. Therefore a trade-off
must be considered for suggesting. For this paper, the selected compositions are Por-Por (#8, 22.63%), Sep-Sep
(#10, 28.84%), Sep-Por (#11, 28.22%) and Dim-Ini (#13, 35.42%); see suggestion instances in table 4b.
For some highlights, the suggestions is a starting point for attaining a valuable handle e.g. the DeGeGene
Name Oprah Gail Winfrey
Suggestions (109) OprPrah, -, WinOpra, WOprey
Name Ellen Lee DeGeneres
Suggestions (91) ElleDeGe, ElleGener, DeGeGene, DeGGyE
Name Willard Carroll Smith
a) b)
Suggestions (224) MithWill, CarrolWilla, OllWill, SmiteyW
Name Angelina Jolie Voight
Suggestions (58) IeJoli, JoliAngeli, AnVo, VJollee
Name Francisco Supino Marcondes
Suggestions (272) SupPino, MarconSup, CoFrancisc, FSuppey
#01 Sep-Ini 0.00706888 #02 Name 0.0071633 #03 Ini-Por 0.00901419 #04 Separation 0.01905134
#05 Con-Ini 0.03164555 #06 Name-Ini 0.03802946 #07 Initials 0.05867811 #08 Por-Por 0.06200336
#09 Portion 0.06446972 #10 Sep-Sep 0.07353824 #11 Sep-Por 0.07731926 #12 Contraction 0.08946507
#13 Ini-Dim 0.09704636 #14 Con-Sep 0.12345875 #15 Con-Por 0.12833573 #16 Con-Con 0.13370589
#17 Name-Con 0.20209316 #18 Name-Sep 0.21308006 #19 Name-Por 0.21562814 #20 Ini-Fancy 0.21579253
#21 Sep-Dim 0.22190245 #22 Por-Dim 0.22192985 #23 Name-Name 0.23012208 #24 Por-Fancy 0.24023221
#25 Sep-Fancy 0.24088978 #26 Con-Fancy 0.24146516 #27 Con-Dim 0.24897241 #28 Name-Fancy 0.26171283
#29 Fancy-Fancy 0.26494588 #30 Dim-Dim 0.26869951 #31 Name-Dim 0.26957627 #32 Dim-Fancy 0.26987766
#33 Diminutive 0.40983232 #34 Fancy 0.90333811 #35 Ini-Ini n/a
Table 4: a) Twitter availability for generated nicknames (sample of 30k for each composition). b) Selected
instances of username suggestions from Por-Por, Sep-Sep, Sep-Por and Dim-Ini composition (one for each). The
values within parentheses are the total number of suggestions retrieved by the selected heuristics. c) Appeal
Index (Ā) — ordered availability data presented in tables 3 and 4a normalized.
may become the DeGGene (also available in Twitter) that perhaps pleases that person. Eventually, appears
“finished” likable handles such as MithWill and WOprey. Among the suggestions there are both “fun” and “serios”
suggestions, respectively, IeJoli and AnVo. It was also noticed that bigger names compositions are more like to
present bad suggestions due to the 15 character length restriction. Finally, for a personal account, all the
presented suggestions for the last instance name are quite acceptable, they are not dream usernames but better
than those presented in figure 1. This suggests by one side that this paper has succeeded in presenting a proof-
of-concept (TRL-3 [14]) that there is room for explore before recurring to number streams. By another side that
the proposed heuristics can be improved in future works.
5 Conclusion
This paper has shown that it is possible to generate a comfortable diversity of human likable nicknames to be
explored before recurring to number streams as it is being done so far. Also, structural onomastics suits for
guiding the generation heuristics for username creation. For this paper, there was proposed a handle suggestion
system that (1) generates nicknames based on the products of six onomastic categories, and (2) ranked them
through the appeal index (Ā) (after checking its availability in Twitter).
For future works, it is suggested to expand, improve and refine the proposed heuristics for generating more
and better human likable usernames suggestions. That may include the middle portion of a name, phonetic
coincidences and name combinations and may consider other properties such as gender, nationality, etc. Also,
there can be proposed ontology relating name, nicknames and usernames through several parameters. For
instance, Franky is a male English diminutive for Frank that relates to Chico in Portuguese which in turn is
a diminutive for Francisco. Finally, submit the usernames for human inspection, without neglecting cultural
differences, for improving the heuristics and the appeal index.
Some questions that remains open are the appealing difference between username patters, e.g. is more appealing than or ?
Is there an important bias comparing automatic generated names, as used in this paper, and people actual
names? Is it possible to generate structurally odd nicknames by feeding a neural network?
Acknowledgements
This work has been supported by FCT Fundação para a Ciência e Tecnologia within the RD Units Project
Scope: UIDB/00319/2020
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