Average Word Length Research Articles

Using Probabilistic Models for Data Compression

Our research objective is to improve the Huffman coding efficiency by adjusting the data using a Poisson distribution, which avoids the undefined entropies too. The scientific value added by our paper consists in the fact of minimizing the average length of the code words, which is greater in the absence of applying the Poisson distribution. Huffman Coding is an error-free compression method, designed to remove the coding redundancy, by yielding the smallest number of code symbols per source symbol, which in practice can be represented by the intensity of an image or the output of a mapping operation. We shall use the images from the PASCAL Visual Object Classes (VOC) to evaluate our methods. In our work we use 10,102 randomly chosen images, such that half of them are for training, while the other half is for testing. The VOC data sets display significant variability regarding object size, orientation, pose, illumination, position and occlusion. The data sets are composed by 20 object classes, respectively: aeroplane, bicycle, bird, boat, bottle, bus, car, motorbike, train, sofa, table, chair, tv/monitor, potted plant, person, cat, cow, dog, horse and sheep. The descriptors of different objects can be compared to give a measurement of their similarity. Image similarity is an important concept in many applications. This paper is focused on the measure of similarity in the computer science domain, more specifically information retrieval and data mining. Our approach uses 64 descriptors for each image belonging to the training and test set, therefore the number of symbols is 64. The data of our information source are different from a finite memory source (Markov), where its output depends on a finite number of previous outputs. When dealing with large volumes of data, an effective approach to increase the Information Retrieval speed is based on using Neural Networks as an artificial intelligent technique.

EXPLORING THE USE OF GRAMMATICAL METAPHOR IN PAKISTANI ESL LEARNERS’ ACADEMIC WRITING: A FOCUS ON NOMINALIZATION

This article aims to explore the use of nominalization in Pakistani ESL learners’ academic writing. Following descriptive analytic approach, a general distribution pattern of nominalization is investigated within the context of disciplinary variation. Based on this general distribution pattern, the use of different features of nominalization is investigated in hundred academic texts (problem-solution essays) produced by the students of humanities and sciences at undergraduate level. From the field of humanities, students of BS English are selected while students of BS Engineering represent the field of science. An average word length is 600 words each text. Contrary to the existing literature on GM use, the analysis reveals that the use of nominalization is not in strong relation with scientific writing only. On one hand, the findings confront with the proposed view of He and Yang (2018) that the use of nominalization is not discipline sensitive. While on the other side, the results show weak agreement with them reporting that nominalized construction cannot be taken as an indicator for technicality of any text. The current study is of implication to discipline-based training of Pakistani ESL learners. Moreover, it signifies that the importance of grammatical metaphor (of which nominalization is the most common feature) in academic texts arises the need to focus on its varied forms and functions in L2 instruction. Keywords: nominalization, grammatical metaphor, academic writing, Pakistani ESL learners, disciplinary variation

Text complexity and linguistic features: Their correlation in English and Russian

Text complexity assessment is a challenging task requiring various linguistic aspects to be taken into consideration. The complexity level of the text should correspond to the reader’s competence. A too complicated text could be incomprehensible, whereas a too simple one could be boring. For many years, simple features were used to assess readability, e.g. average length of words and sentences or vocabulary variety. Thanks to the development of natural language processing methods, the set of text parameters used for evaluating readability has expanded significantly. In recent years, many articles have been published the authors of which investigated the contribution of various lexical, morphological, and syntactic features to the readability level. Nevertheless, as the methods and corpora are quite diverse, it may be hard to draw general conclusions as to the effectiveness of linguistic information for evaluating text complexity due to the diversity of methods and corpora. Moreover, a cross-lingual impact of different features on various datasets has not been investigated. The purpose of this study is to conduct a large-scale comparison of features of different nature. We experimentally assessed seven commonly used feature types (readability, traditional features, morphological features, punctuation, syntax frequency, and topic modeling) on six corpora for text complexity assessment in English and Russian employing four common machine learning models: logistic regression, random forest, convolutional neural network and feedforward neural network. One of the corpora, the corpus of fiction literature read by Russian school students, was constructed for the experiment using a large-scale survey to ensure the objectivity of the labeling. We showed which feature types can significantly improve the performance and analyzed their impact according to the dataset characteristics, language, and data source.

Readability of COVID-19 vaccine information for the general public

BackgroundMore than 130 million individuals in the United States have now received at least one dose of a COVID-19 vaccine. Currently, all adults in the Unites States now have access to one of three COVID-19 vaccines. As part of the vaccination procedure, Emergency Use Authorization (EUA) fact sheets, which contain information regarding the vaccine, are provided. The purpose of this study was to analyze the ease of reading (i.e., readability) of the EUA-approved fact sheets for the vaccines currently available in the United States, the V-Safe adverse event survey script, and the Centers for Disease Control and Prevention (CDC) website information on COVID-19 vaccines designed for the general public in the United States. MethodsWe acquired the Pfizer, Moderna, and Janssen EUA fact sheets, as well as the V-Safe survey script and the CDC website information regarding COVID-19 vaccines. These documents were analyzed for their complexity regarding the following readability factors: average length of paragraphs, sentences, and words; font size and style; use of passive voice; the Gunning-Fog index; the Flesch Reading Ease index; and the Flesch-Kincaid Grade Level index. ResultsOnly the V-Safe adverse-event survey script met readability standards for adequate comprehension. The mean readability scores of the EUA fact sheets and the CDC website were as follows: Flesch Reading Ease score (44.35 avg); Flesch-Kincaid Grade Level (10.48 avg); and Gunning-Fog index (11.8 avg).These scores indicate that at least a 10th-grade level education would be required to understand these reading materials. ConclusionThe average person in the United States would have difficulty understanding the information provided in the EUA fact sheets and CDC COVID-19 vaccine website documents; however, the V-Safe survey was written at an adequate reading level. To ensure that the general public fully understands information regarding COVID-19 vaccines, greater care and effort should be given to the development of simplified information material.

USING CLOZE PASSAGES TO ESTIMATE READABILITY FOR RUSSIAN UNIVERSITY STUDENTS: PRELIMINARY STUDY

This preliminary study examines the relationships between each of six first language (L1) readability indexes and the cloze passage mean performances of Russian EFL students. The cloze passages were created by randomly selecting 50 text passages from an American public library and deleting every 12th word in each passage to create a 30-item cloze procedure. The participants were 5170 EFL students from 38 universities in theRussian Federation. Each student was randomly assigned to take one of the 30-item cloze passages. The L1 readability indexes calculated for each of the 50 passages were the Flesch, Flesch-Kincaid, Fry, Gunning, Fog, and modified Gunning-Fog indexes. The preliminary results indicate that the L1 readability indexes were moderately to highly correlated with each other, but only somewhat correlated with the mean performances of Russian university students on cloze versions of those same passages. These results are discussed in terms of why the L1 readability indexes are moderately to highly correlated with each other but only somewhat correlated to the Russian EFL means. The authors also explain what they are planning in terms of further linguistic analyses (e.g., of variables like average word length, percent of function words, number of syllables per sentence, number of words per paragraph, frequencies of words in the passages, and so forth) and statistical analyses (including at least factor analysis, multiple regression analyses, and structural equation modeling) of these data.

Russian Text Author’s Gender Identification in Forensic Examination: Probability-and-Statistics Method

The article discusses intermediate research results in the development and improvement of a computerized model of Russian texts authorization, which is based on complex application of probabilistic-and-statistical methods. The study aims to describe the new capabilities of the created system in the aspect of its application to diagnostic examinations in text authorization for detection of the gender of the alleged author of the text. The work presents the next stage of fine-tuning and testing of the improved version of the computer program "CTA" (computerized text authorization), which at this stage was adapted for the task of determining and comparing stable relative frequencies of correlation coefficients (the ratio of specified linguistic phenomena of different levels of the language system) in the texts, the authors of which are men and women. The research material is the continuously updated primary bases of literary texts of the 19 th and 21 st centuries (4 bases, respectively). The work shows that for the texts written by men and women, significant differences can be noted in such correlation coefficients as average word length, average sentence length, objectivity coefficient, quality coefficient, activity coefficient, dynamism coefficient, connectivity coefficient, etc. Verification of the results obtained experimentally has demonstrated that the accuracy of gender determining at this stage of the study is approximately 65%. This indicator can be significantly exceeded with an increase in the volume and quality specification of databases and/or when using new models for calculating the correlation coefficients (Spearman's model, etc.).

Comparison of Style Features for the Authorship Verification of Literary Texts

The article compares character-level, word-level, and rhythm features for the authorship verification of literary texts of the 19th-21st centuries. Text corpora contains fragments of novels, each fragment has a size of about 50 000 characters. There are 40 fragments for each author. 20 authors who wrote in English, Russian, French, and 8 Spanish-language authors are considered.The authors of this paper use existing algorithms for calculation of low-level features, popular in the computer linguistics, and rhythm features, common for the literary texts. Low-level features include n-grams of words, frequencies of letters and punctuation marks, average word and sentence lengths, etc. Rhythm features are based on lexico-grammatical figures: anaphora, epiphora, symploce, aposiopesis, epanalepsis, anadiplosis, diacope, epizeuxis, chiasmus, polysyndeton, repetitive exclamatory and interrogative sentences. These features include the frequency of occurrence of particular rhythm figures per 100 sentences, the number of unique words in the aspects of rhythm, the percentage of nouns, adjectives, adverbs and verbs in the aspects of rhythm. Authorship verification is considered as a binary classification problem: whether the text belongs to a particular author or not. AdaBoost and a neural network with an LSTM layer are considered as classification algorithms. The experiments demonstrate the effectiveness of rhythm features in verification of particular authors, and superiority of feature types combinations over single feature types on average. The best value for precision, recall, and F-measure for the AdaBoost classifier exceeds 90% when all three types of features are combined.

Improving reading rate prediction with word length information: Evidence from Dutch.

Previous research in English has suggested that reading rate predictions can be improved considerably by taking average word length into account. In this study, we investigated whether the same regularity holds for Dutch. The Dutch language is very similar to English, but words are on average half a letter longer: 5.1 letters per word (in non-fiction) instead of 4.6. We collected reading rates of 62 participants reading 12 texts with varying word lengths and examined which change in the English equation accounts for the Dutch findings. We observed that predictions were close to the best-fitting curve as soon as the average English word length was replaced by the average Dutch word length. The equation predicts that Dutch texts with an average word length of 5.1 letters will be read at a rate of 238 words per minute (wpm). Texts with an average word length of 4.5 letters will be read at 270 wpm, and texts with an average word length of 6.0 letters will be read at a rate of 202 wpm. The findings are in line with the assumption that the longer words in Dutch do not slow down silent reading relative to English and that the word length effect observed in each language is due to word processing effort and not to low-level visual factors.

한국어 분석 도구를 활용한 국어교과서 텍스트의 장르 비교 분석

This study aimed to investigate the characteristics of three different genres, including argumentative, expository, and narrative texts that were selected from high school Korean textbooks revised by the 2015 National Curriculum with Auto-Kohesion, a Korean text analysis tool. The indices used for the current study included basic measures (the number of words, the number of sentences, the average sentence length, the average word length), word frequency (word frequency for contents words and all words), lexical diversity (type-token ratio for content words), personal pronouns (the first person pronouns, the second person pronouns, the third person pronouns, all pronouns), connectives (causal, additive, and all connectives), syntactic complexity (the ratio of modifiers, the ratio of constituents), and reference cohesion measures. The analysis demonstrated that the argumentative and expository texts were not significantly different in all measures. However, the differences between the two genres versus narratives were substantial; a greater use of personal pronouns and lower frequency words as well as more words and sentences was identified in the narrative texts, whereas the argumentative and expository texts included the higher text cohesion and the sentence constituent ratio along with the longer average words and sentences. The argumentative texts have been shown to include more personal pronouns than the other two genres. The current study provides some implications for designing Korean textbooks and analyzing the characteristics of different genres.

A comparison of clinician assessment of speech versus automated speech analysis in mild cognitive impairment and Alzheimer’s dementia

AbstractBackgroundLanguage impairment is an important marker of neurocognitive disorders. Despite this, there is no universal system of terminology used to describe speech impairment and large inter‐rater variability can exist between clinicians assessing speech. The role of automated speech analysis is emerging as a novel and potentially more objective method to assess speech in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). No studies have analyzed whether variables extracted through automated speech analysis can also be correlated to speech characteristics identified by a clinician. We sought to: (1) investigate whether clinician‐identified speech characteristics could be correlated with acoustic and linguistic variables identified through automated speech analysis, and (2) through automated speech analysis, identify novel acoustic and linguistic variables that may be associated with MCI or AD.MethodUsing the DementiaBank speech corpus (Cookie Theft picture description task), audio recordings from patients with possible/probable AD (n=10), MCI (n=10), and controls (n=10) were rated by clinicians. Four characteristics of speech were rated by clinicians, including: word‐finding difficulty, coherence, perseveration, and speech errors. Clinicians were blinded to each other’s scoring. Where scores differed, a consensus rating was established. The speech recordings were then transcribed, and linguistic and acoustic variables were extracted through automated speech analysis. The correlation between clinician‐identified speech characteristics and the acoustic and linguistic variables were then compared.ResultA significant correlation (p < 0.05) was found between clinician‐identified speech characteristics and variables extracted through automated analysis. Average word length was correlated with word‐finding difficulty (ρ = 0.74); changes in syntactic construction (use of past tense, third person singular verbs, and subordinate clauses) were correlated with coherence (ρ = 0.51) and errors in speech (ρ = 0.58); similarity between consecutive utterances was correlated to perseveration (ρ = 0.68).ConclusionIn this exploratory study, variables extracted through automated acoustic and linguistic analysis of MCI and AD speech were strongly correlated to clinician‐identified speech characteristics. Our results suggest further investigation for using an automated, data‐driven approach to define and monitor subjective clinical speech descriptors in neurocognitive disorders.

Метод оценки дидактической сложности школьных учебников по различным дисциплинам

The article discusses the problem of evaluating the differential didactic complexity (DDC) of educational texts, which characterizes the difficulty of their perception and assimilation by pupil. It is shown that DDC is determined by: 1) the density of semantic information, depending on the abstraction degree of the terms used and their presence in the pupil’s thesaurus; 2) the complexity level of mathematical, chemical and other formulas; 3) the structural complexity of the text, depending on the average length of its constituent words and sentences. Multiplying the DDC of the text by its volume, you can find the integral didactic complexity of the text. For the evaluation of the textbook DDC expert selects one page fragments of text randomly, identifies the key concepts, “measures” their average information content, determines the share of formulas and their average complexity. In this case, the classification of concepts according to the abstraction degree is used, which takes into account the occurrence of a particular word in the thesaurus of a preschool, fifth-grader, ninth-grader and school graduate. The structural complexity of the text is also taken into account, depending on the average length of words and sentences. The analysis of textbooks for school graduates has shown that the most difficult disciplines to understand are biology, physics, chemistry, mathematics. As a result of evaluating computer science textbooks for 3rd, 5th, 9th and 11th grades it was found that their semantic information density and differential semantic complexity monotonically increase from 5.3 to 8.1 and from 5.7 to 10.4 respectively.

한국어 분석 시스템 Auto-Kohesion을 활용한 고등학교 국어교과서에 수록된 서사문과 논설문 비교 분석

This study aims to analyze the linguistic and psycholinguistic differences between narrative and argumentative materials of high school Korean textbooks with Auto-Kohesion, a computer program designed to investigate Korean texts by a wide range of measures. The results show that the higher proportion of modifiers and the use of more pronouns (the second and the third person pronouns) are noticeable in the narrative texts. For all word frequency measures, the average word frequency in the narrative texts is lower than in the argumentative texts. Also, the reference cohesion (noun overlap for adjacent sentences and total sentences), the syntactic complexity, the average sentence length, and the average word length are relatively higher in the narrative texts. These results have several pedagogical implications for the development of Korean textbooks and Korean language teaching materials.

Phonotactic Complexity and Its Trade-offs

We present methods for calculating a measure of phonotactic complexity—bits per phoneme— that permits a straightforward cross-linguistic comparison. When given a word, represented as a sequence of phonemic segments such as symbols in the international phonetic alphabet, and a statistical model trained on a sample of word types from the language, we can approximately measure bits per phoneme using the negative log-probability of that word under the model. This simple measure allows us to compare the entropy across languages, giving insight into how complex a language’s phonotactics is. Using a collection of 1016 basic concept words across 106 languages, we demonstrate a very strong negative correlation of − 0.74 between bits per phoneme and the average length of words.

AVERAGE WORD LENGTH AND TEXT REDUNDANCY VARIABILITY: FRENCH TEXTS CASE STUDY

The redundancy and average word length correlation in French texts have been researched. This correlation has been evaluated on the basis of analysis of entropy, redundancy and average word length for literary, scientific, and publicistic texts. It has been revealed that the variability of text redundancy correlates well with the variability of average word length, if calculating the average word length of an individual text we exclude the length of words belonging to the exponential tail of entropy curve. In this regard it is proposed to distinguish between two average word lengths of text: the average length of a word belonging to the exponentially decaying tail of entropy and the average length of a word not belonging to the exponential tail of entropy.

Об истоках анонимной русской версии Марша шпионов Р. Киплинга

The purpose of the article is to try to clarify the genesis of the Russian anonymous version of The Spies’ March (hereinafter, the A-version), circulating along with its translations by Ada Onoshkovich-Yatsyna (hereinafter, AOY version) and Alla Sharapova, as well as textual variations on the theme of The Spies’ March by L. Kolosov and V. Kovenatsky. The textual problem is solved in close connection with the composition (consisting in comparing discourse schemes), translation, linguistic-statistical analysis of complete versions and fragments fr om them. It has been established that the A-version was not a remake of Kipling's original, but was based on Onoshkovich-Yatsyna’s translation. The purpose of the anonymous author-translator was to harmonize the content of The Spies’ March with its name, that is, to bring the theme of the glorification of real spies to the fore, to send them on a mission with a Proud flag in their hands, eliminating all semantic allusions to doctors (that is fighters against epidemics), which were implied by Kipling. Comparison of the A-version discursive scheme with that of AOY version shows that the uniqueness of its content and communicative attitude are achieved through a complex scheme of permutations, involving the new sequence of 33 lines out of 81 that constitute the poetic part of the AOY version. Herewith, all words (= text forms) of the A-version, with the exception of two, and those of the AOY version are identical. In other words, a cardinal semantic difference is created through manipulations, mainly at the discursive level, but not lexically. In parallel, the cardinal issue of the socio-cultural conditioning of this kind of reception is considered, the terminological signals of which in the article are refraction, adaptation, domestication and appropriation. Acting in the field of the receiving culture as a single complex of factors, they inevitably lead to the emergence of translation derivatives like a purely “spy” A-version of The Spies’ March, which break away from the original culture, from the literary environment, from the original text and are included in such a rearranged form in a new cultural mentality. It is even indicative that The Spies’ March for some mysterious reasons attracted the attention of a young Soviet woman-translator in 1922 and contrary to any “objective” logic: it did not enjoy increased attention in English-speaking countries and, as Thomas Pinney noted later, did not enter any of the authoritative anthologies of R. Kipling’s poetry. However, in Soviet Russia and later, his Spies’ March has given rise to a number of free, wild or professional variations all of which are meant eventually to bring tribute to this Rogue Bard (if not an Odd Duck) of the British culture sometimes accused of being more a writer of verses than a true Poet. The article is a direct continuation of the previous one, Bring us deliverance, spy, the title of which is a quote from The Spies’ March; reading them together provides a deeper understanding of the author’s intention. The paper includes three major parts: 1) Comparative textual analysis of the A-version with the complete AOYversion on which the first one is considered to be based since it “quotes” about 35 % of its lines, rearranging them in an extremely sophisticated way; 2) Comparative analysis of the versions’ language statistics including the number of words, number of characters with and without spaces, average word length, two indexes of text size conversion, namely, compression and expansion; 3) Discussion section which dwells on Kipling’s comparative place and reputation in the English and Russian (Soviet) culture. His artistic expression, in particular, his poetry has been and still is valued much more in this country than in England wh ere he is now considered, first of all, to be an “imperialist and racist”.

Structure-based or dynamic usage‑based instruction

This classroom study aims to explore the instructional effects of structure-based (SB) or dynamic usage-based (DUB) instruction with free response, communicative writing tasks after three years of L2-French instruction on linguistic complexity measures in (morpho)syntax and lexicon. We investigated data from forty-three young high school beginner learners of L2-French after three years of instruction with similar amounts of L2 exposure. The SB treatment included a traditional focus on explicit grammar; the DUB group was taught using the Accelerated Integrated Method, a highly communicative, meaningfocused method without explicit instruction, but with a great deal of exposure and repetition to induce frequency effects. Results after three years show that DUB instruction leads to more linguistic complexity in terms of various (morpho)syntactic and some lexical measures (multi-word sequences coverage). On other lexical measures (such as Guiraud index and average word length), no differences were found. The results are discussed using insights from the dynamic usage-based perspective.

Automated Essay Scoring using Ontology Generator and Natural Language Processing with Question Generator based on Blooms Taxonomy’s Cognitive Level

Essay writing examination is commonly used learning activity in all levels of education and disciplines. It is advantageous in evaluating the student’s learning outcomes because it gives them the chance to exhibit their knowledge and skills freely. For these reasons, a lot of researchers turned their interest in Automated essay scoring (AES) is one of the most remarkable innovations in text mining using Natural Language Processing and Machine learning algorithms. The purpose of this study is to develop an automated essay scoring that uses ontology and Natural Language Processing. Different learning algorithms showed agreeing prediction outcomes but still regression algorithm with the proper features incorporated with it may produce more accurate essay score. This study aims to increase the accuracy, reliability and validity of the AES by implementing the Gradient ridge regression with the domain ontology and other features. Linear regression, linear lasso regression and ridge regression were also used in conjunction with the different features that was extracted. The different features extracted are the domain concepts, average word length, orthography (spelling mistakes), grammar and sentiment score. The first dataset used is the ASAP dataset from Kaggle website is used to train and test different machine learning algorithms that is consist of linear regression, linear lasso regression, ridge regression and gradient boosting regression together with the different features identified. The second dataset used is the one extracted from the student’s essay exam in Human Computer Interaction course. The results show that the Gradient Boosting Regression has the highest variance and kappa scores. However, we can tell that there are similarities when it comes to performances for Linear, Ridge and Lasso regressions due to the dataset used which is ASAP. Furthermore, the results were evaluated using Cohen Weighted Kappa (CWA) score and compared the agreement between the human raters. The CWA result is 0.659 that can be interpreted as Strong level of agreement between the Human Grader and the automated essay score. Therefore, the proposed AES has 64-81% reliability level.

Alphabet usage pattern, word lengths, and sparsity in seven Indo-European languages

Abstract An empirical study on about 1.7 million dictionary words from seven languages viz. English, French, Dutch, Spanish, Italian, Hindi, and German has been conducted. Three intriguing characteristic features have been analyzed. First, the alphabet usage pattern in a language was determined which can be used to give an idea on how alphabets have been employed. For instance, the alphabet ‘e’ is highly used in English, while ‘q’ is least used. Second, the average and range of word lengths in the languages were computed and seen to vary from 1 to 37. Average word lengths were computed in the range (6.665–11.14). For comparison, word lengths have been fitted using Gaussian distribution. Third, a new measure was derived; which we termed ‘Language Sparsity’; computed as one minus ratio of number of words of a particular length already existing to the total number of possible words that can be formed. Sparsity hence gives a measure of the scope of fruition in languages. Two such measures have been defined: a weighted and a nonweighted sparsity. Nonweighted sparsity was found to be minimum (0.877) for English and maximum (0.982) for Dutch. The results obtained can play a significant role in propagating the synergy of language evolution.

Readability characteristics of texts in middle school Turkish textbooks

Turkish textbooks are important tools for gaining skills and competencies identified in curriculum. Textbooks should be designed to contribute to students’ basic language skills as well as high level thinking skills in alignment with curriculum. The texts to be selected for textbooks should be prepared in alignment with the targeted skills and learning outcomes in terms of visuals and content. Textbooks kids encounter during elementary education play an important role in their upbringing as individuals with reading habits in later phases of their education. Therefore, texts to be included in textbooks should be selected carefully to reflect the best examples of text types, to be compatible with the child’s language and meaning universe, and should be understandable. In this study, the readibility levels of texts in Turkish course textbooks were analyzed. Readibility levels reveal the comprehensibleness of texts. Readability depends on the number of words in a sentence and syllables in a word. First the average word and sentence length were calculated and then the readibility scores of texts were calculated. Two formulas adapted to Turkish were used in identifying the readibility levels of texts. The findings were analyzed comparatively. The results showed that the readability levels of narrative texts are easier compared to informing texts.

How character limit affects language usage in tweets

In November 2017 Twitter doubled the available character space from 140 to 280 characters. This provided an opportunity for researchers to investigate the linguistic effects of length constraints in online communication. We asked whether the character limit change (CLC) affected language usage in Dutch tweets and hypothesized that there would be a reduction in the need for character-conserving writing styles. Pre-CLC tweets were compared with post-CLC tweets. Three separate analyses were performed: (I) general analysis: the number of characters, words, and sentences per tweet, as well as the average word and sentence length. (II) Token analysis: the relative frequency of tokens and bigrams; (III) part-of-speech analysis: the grammatical structure of the sentences in tweets (i.e., adjectives, adverbs, articles, conjunctives, interjections, nouns, prepositions, pronouns, and verbs); pre-CLC tweets showed relatively more textisms, which are used to abbreviate and conserve character space. Consequently, they represent more informal language usage (e.g., internet slang); in turn, post-CLC tweets contained relatively more articles, conjunctions, and prepositions. The results show that online language producers adapt their texts to overcome limit constraints.