The emergence known by online press companies requires written filtered information for a better understanding and speed the understanding of texts and messages that are posted. Testing the readability of text in an online environment is important in the optimization process for indexing in search engines and not only.readability, web, text optimization

Eugen-Gabriel Garais

English

Research Papers in Economics

WEB APPLICATIONS READABILITY 
 
Eugen-Gabriel Garais 
1 
 
Abstract 
 
The emergence known by online press companies requires written filtered information for 
a  better  understanding  and speed  the  understanding  of texts 
and  messages  that  are  posted.  Testing  the  readability  of  text  in an online 
environment is important in the optimization process for indexing in search engines and 
not only. 
 
Keywords: readability, web, text optimization 
 
Readability of text is defined as a document that can easily be read and understood. 
 
As  [WLDB04],  Gunning Fog,  Flesch Reading Ease,  Flesch-Kincaid,  SMOG (Simple 
Measure Of Gobbledygook),  Fry Readability Formula,  Automated Readability Index 
(ARI),  Spache Readability Formula,  Dale-Chall Readability Formula,  Coleman-Liau 
Index  represent  algorithmic-level  models 
that  are  helping  the  site  rank  in  a  hierarchy  of  degrees  of  readability  and 
are  useful  in  filtering  and  sorting  of certain  information  depending on the 
resulting interpretation of texts. 
 
The models  that are  treated  below  are  implemented 
in  www.amosnews.ro  dynamic  site,  which  is  made  entirely  by  the 
author.  This  site  belongs  to a  news  agency  in  Romania  which issues  daily  news  to 
about  90.  Given the  large number  of  visitors to  the  news  and  decided to  test 
the readability of this site for future maintenance. The large number of aggregated data 
sets  just  over  24  hours  gives us  a  sufficient  area of  study  as 
a  suggestive  interpretation.  The  text  of the  test  models  were  observed  that  only  the 
following  tests have  a  consistent  minimum  acceptable  result  for  the texts  in 
Romanian  and  English:  Flesch-
Kincaid Reading Ease, Flesch Kincaid Grade Level, Gunning Fog Score, Coleman Liau I
ndex, Smog Index, Automated Readability Index. 
 
To observe the results in comparing manner there were two texts chosen and noted as 
story A and story B from the pages of the web site amosnews.ro: 
Story A - http://www.amosnews.ro/Stire-29-50027 
Story B - http://www.amosnews.ro/Stire-29-50235 
We refer to these texts as Story A and Story B. 
In table 1 are presented common parameters which stand at the basis of the following 
models 
tabel 1 – Analyzed parameters to calculate the readability formulas 
                                                             
1 PhD Candidate Faculty of Computer Science for Business Management, Romanian-American University, 1B 
Expozitiei Blvd., district 1, code 012101, Bucharest, Romania, e-mail:garais@g4.ro  
 
Measured parameter  Story A   Story B  
Characters  12903  528 
Letters  10466  413 
Phrases  109  7 
Words  2120  91 
Distinct words  932  49 
Average words / sentence  19,45  13 
Average nr of syllables / word  2,02  1,87 
Words with >= 3 syllables   611  25 
Total count of syllables  4275  170 
Percent of words with  >= 3 syllables  28,82  27,47 
 
Readability formulas are divided in two categories L1 and L2 which are differentiated 
through the way of interpreting the final result. There are results that: 
-  Are distributed on a 0 to 100 scale;  
-  Indicate the level of necessary education to understand the text. 
According to [DFJH09] the results of formulas that take account of number of syllables 
L1, is transposed on a 0 to 100 scale, in which 0 gives the text a lower level of readability 
(a hard to understand text), and 100 gives the text a high level of readability (text easy to 
understand). 
 
Flesch Reading Ease Model is of L1 category with levels from 0 to 100. As the score 
grows higher the document is easier to understand. Web Sites must reach a level between 
60 and 70 to be understood by a number of many readers.  
This calculation is based according to [PHWB94] on the next elements: 
- average of sentence length; 
- average number of syllables; 
- the amount of personal word used; 
- the amount of personal sentences used in 100 words. 
 
The model determines how much a person with average skills can read and understand 
from a written message. The results are compared with determined standards for the 
targeted audience considering that a readable Ad contains 14 words in a sentence, 140 
syllables at 100 words, 10 personal words an 43% personal sentences.    
 
The method represents a way of verifying the communication efficiency and it is 
advisable using this together with other pretested processes. 
The formula is: 
FRE = 206.835 – 1.015 �
𝑁𝑐𝑣
𝑃𝑟 � - 84.6 �
𝑇𝑠𝑖𝑙𝑎𝑏
𝑁𝑐𝑣 � 
where: 
FRE: Flesch Reading Ease readability formula 
Tsilab: total number of syllables 
Ncv: number of words 
Pr: number of sentences 
The coefficients 206.835, 1.015 and 84.6 are multiplying coefficients chosen according to 
[DFJH09] as a result of text tests on English language. The coefficients are a consequence of a refinement process of the amount of education degree of a person that reads and 
understands the English language. The coefficient of 84.6 represents the amount of 
importance assigned to the number of words within a text. 
 
The word processors that use this algorithm are according to [WWW5]: Microsof Word, 
Google Docs, Lotus WordPro, Kword.  
 
table 2 – The results after applying the Flesch Reading Ease formula on story A 
and B 
  Story A   Story B  
FRE  16,5  35,6 
 
The results after applying the Flesch Reading Ease formula on the two stories A and B, 
demonstrates the calibration strictly for the English language being impossible for the two 
stories to be on such a low level on the 0 – 100 scale. The obtained result as they are can 
be treated as if the persons who read these texts should at least have a PhD diploma. 
 
The researches on readability formulas shows that there are formulas for next languages: 
Italian, Spanish, French, Danish, Japanese according to [WWW26] and [WWW27]. The 
author for this article is developing and researching a formula specific for the Romanian 
language which is part of his PhD theses. 
 
After some tests it has been observed that the only formulas that are close as a result to 
the Romanian language are the formulas for the Italian and Spanish language, as it should 
be reasonable because of the lexical construction similarities between these languages. 
 
The calibration of the  Flesch Reading Ease formula for the Italian language is of L1 
category. The formula is also known as the Franchina-Vacca formula according 
to[WWW18] and [WWW29].  
FREIT = 217 - 1,3 Ncvmed - 0,6 Nsil100 
where: 
FREIT  - FRE formula for the Italian language (Franchina-Vacca) 
Ncvmed – number of average word on sentece 
Nsil100  - number of syllables in 100 words 
table 3 – The results of FREIT formula on stories A and B 
  Story A   Story B  
FREIT  70,07  88 
Applying the FREIT formula on story A and B results as in table 3 that this formula is 
closer to reality as those in table 2. So it is proved that using formulas of languages with a 
closer lexical form to the Romanian language is preferable. 
 
The amount of 0,6 is applied to the number of syllables identified in 100 words chose 
successively in the analyzed text and 1,3 is the amount applied to the average number of 
words from the total number of sentences. 
The adjustment of Flesch Reading Ease formula for Spanish is classified as a L1 
category.  According to WWW19, the adjusted formula is known in this field as  
 
Fernández Huerta. The Spanish label comes from the name of the scientist who adjusted 
the initial Flesch formula.  
FRESP = 206.84 - (0.60 * Nsil100) - (1.02 * Ncvmed) 
where:  
FRESP - FRE formula adjusted for Spanish language (Franchina-Vacca) 
Ncvmed – number of average words from a sentence 
Nsil100  - number of syllables at 100 words 
table 4 – The results of applying the FRESP formula on stories A and B 
  Story A   Story B  
FRESP  80,06  86,9 
   
The result from table 4 is another prove of small gap between the lexical form of the 
romanian language and others to base a new readability formula. 
Adapting the Flesch Reading Ease formula for the French languageis of L1 category. 
The adaptef romula can be found in literature under the Kandel - Moles name according to 
[WWW20], [WWW21] and [WWW30].  
FREFR = 207 – 1.015 �
𝑁𝑐𝑣
𝑃𝑟 � – 73.6 �
𝑇𝑠𝑖𝑙𝑎𝑏
𝑁𝑐𝑣 � 
where: 
FREFR - FRE Kandel-Moles formula 
Tsilab  - total number of syllables 
Ncv       - number of words 
Pr          - number of sentences 
 
table 5 – The FREFR formula results on stories A and B 
  Story A   Story B  
FREFR  38,8  56,3 
 
Here is to mention that in spite the fact that the French lexical forms are close to the 
Romanian language yet the result of the readability formula shows that numbers are much 
to different and that this formula cannot be useful in determining a new adaptation of the 
Flesch formula for the Romanian language. The coefficient of 73,6 is modified from the 
standard of 84,6 for adapting to the FRE formula of the French language. 
The models of determining readability with educational notations are of L2 category, 
which can be found in specialized literature as: Gunning-Fog, Flesch-Kincaid Grade 
Level, SMOG, Graficul de lizibilitate Fry, Automated Readability Index, Spache, 
Dale-Chall, Coleman-Liau Index. 
In this article it will be applied only one model which according to [DGJG09], the 
Gunning-Fog  model shows how many years of personal education e person needs to 
understand with ease a specific text. A lower number denotes a better understanding and 
at the other point of interval, a higher number shows a more complex text and so making 
it hard that such a text to be understood. In this case a number of 17 needs post-university 
education for a text to be understood. This test was created for the English language cand 
tests mainly the number of syllables from a word ignoring the numerical values.  
Testing this formula on stories A and B gives results in table 6.  
NIVedu = 0.4*(Ncv/Pr+((Cts/Ncv)*100)) 
where:  NIVedu – US education level 
Ncv       - Number of words  
Cts        - Number of words with more than 3 syllables 
Pr       - Number of sentences 
table 6 – The Gunning – Fog formula results on stories A and B 
  Story A   Story B  
NIVedu  18,5  12,7 
 
It is suggested that the number of long words should not be more than 10 to 15 at every 
100 words so that texts can be understood with an education equivalent to high school.  
After many test of more than 40.000 texts of different lengths and complexity a formula 
was created to calculate Romanian language readability. 
 
The relation which results from applying the rules in determining proportions is:    
Gcit = 0,0158 *  
𝑳?????? ∗ 𝑵𝑰𝑽????
𝑭𝑹𝑬????  
where: 
Gcit       - readability formula for texts write in Romanian language 
𝐹𝑅𝐸𝖤𝑠 ��������   - the average of FRE relations on a 0 to 100 scale 
𝑁𝐼𝑉𝑔𝑟 ���������  - average of relations which calculates the level of education needed for 
text understanding 
Ltxt           -  text length measured in number of characters 
This formula determines based on complexity formulas how much other texts are easier or 
harder than other. This formula can be used in many case scenarios. This formula and 
research was done by the author having  as a case study all the stories written and 
published by the news publishing agency Amos News (www.amosnews.ro). 
 
From the developers point of view they have access to a table of contents which suggests 
them quality and quantity values. The supervisors of texts that are being added to a web 
site use the Gcit indicator in an automated way through filtering and calculations of an 
algorithm which shows them not only final results but also the intermediate stages so that 
they can make better decisions about keeping or improving the quality of texts that are 
published on the web site. Better texts can grow the number of visitors. There is not quite 
a standard for what is a good text, but there are target audiences and for this, using the 
right tool can improve the experience of that target readers. 
 
References: 
[WLDB04] William H. DuBay - The Principles of Readability, 2004 
[DFJH09] Dana R. Ferris, John Hedgcock - Teaching Readers of English: Students, Texts, 
and Contexts, Taylor & Francis, 2009, ISBN: 978-041-5999-64-9 
[WWW26] http://www.lingv.ro/resources/scm_images/RRL-34-2006-LDinu.pdf 
(Conf. dr. Liviu P. Dinu (http://fmi.unibuc.ro/ro/catedre/funinf/dinu_liviu/ )) 
[WWW27]  http://www.utexas.edu/disability/ai/resource/readability/manual/formulas-
English.html (- Univeristatea Texas - Austin) 
 

DuBay - The Principles of Readability,

John Hedgcock - Teaching Readers of English: Students, Texts, and Contexts, Taylor & Francis,

WEB APPLICATIONS READABILITY

http://www.rebe.rau.ro/RePEc/rau/jisomg/SP11/JISOM-SP11-A13.pdf

WEB APPLICATIONS READABILITY

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