Readability Metrics Research Articles

Short Poem Generation (SPG): A Performance Evaluation of Hidden Markov Model based on Readability Index and Turing Test

We developed a Hidden Markov Model (HMM) that automatically generates short poem. The HMM was trained using the forward-backward algorithm also known as Baum Welch algorithm. The training process was exhausted by a hundreds of iterations through recursion method. Then we used the Viterbi algorithm to decode all the best possible hidden states to predict the next word, and from the previous predicted word, it will generate another word, then another word until it reaches the desire word length that was set in the program. Afterwards, the model was evaluated using several kinds of readability metrics index which measure the reading difficulty and comprehensiveness of the generated poem. Then, we performed a Turing Test, which participated by 75 college students, who are well versed in poetry. They determined if the generated poems was created by a human or a machine. Based from the evaluation results, the highest readability score index of the generated short poem is in the grade 16th level. While 69.2% of the participants in the Turing Test, agreed that most of the machine generated poems were likely created by some well-known poets and writers.

Will the Effect of New Federal Breast Density Legislation Be Diminished by Currently Available Online Patient Educational Materials?

To evaluate readability of commonly accessed online patient educational materials (OPEM) on breast density in setting of recently passed federal breast density legislation. The term "breast density" was queried using an online search engine to identify the top 50 commonly accessed websites based on order of search results on December 15, 2018. Location, cookies, and user account information were disabled prior to our query. Only websites with OPEM, defined as any educational material on breast density targeted towards the general public, were evaluated in our study. Sponsored hits and research journal articles were excluded. Available patient-directed information from websites meeting inclusion criteria was then downloaded. Grade-level readability was then determined from formatted content using generalized estimating equations, with observations nested within readability metrics from each website. Results were compared to American Medical Association recommended readability parameters (sixth-grade reading level). All interval estimates were calculated for 95% confidence. Fouty-one websites met inclusion criteria representing patient-directed OPEM on breast density. Average grade-level readability of health information on breast density in our study ranged from 8.5-16.5 years with an average grade reading level of 11.1 years across all websites. Of websites fitting into a specific category, academic websites had the highest average grade reading level (12.0), while nonprofit websites had the lowest average grade reading level (10.4). Nearly half (19/41) of all websites in our study had diagrams to aid in patient comprehension, while few websites (2/41; 4.8%) displayed videos in addition to written content. The website with the lowest average grade reading level was WebMD, which had an average reading level of 8.5. No individual website in our study met American Medical Association recommended parameters of a sixth-grade reading level CONCLUSION: Readability of currently available OPEM on breast density may be written at a level too difficult for the general public to comprehend, which may represent a barrier to educational goals of newly passed federal breast density legislation.

Improving State-Mandated Breast Density Notifications.

Effective written communication directly affects health care outcomes. Since 2016, the complex language of state-mandated breast density notifications (BDNs) has been challenged, because it is perceived to be beyond the comprehension of most patients. The aim of this study was to assess whether a revised BDN written at a lower reading grade level improves understanding compared with the current state-mandated BDN. A revised notification with similar content to the current state-mandated BDN was developed. Both notifications were presented to patients for direct comparison, using a paper survey asking questions that evaluated patients' perceptions and convictions associated with breast density. Surveys were distributed at four outpatient imaging centers to screening mammography patients. The current BDN's mean readability metric was 13.4, and that of the revised BDN was 6.6. Five hundred surveys were analyzed. Survey data demonstrated that 56.6% of all women perceived that dense breast tissue results indicated a "high" associated lifetime breast cancer risk from the current state-mandated BDN compared with only 2.2% with the revised notification (P < .001). Nearly all women were more likely to initiate discussions with their providers regarding their breast tissue density after reading the revised notification (96.0%) as opposed to the current state-mandated BDN (32.8%; P < .001). A significant portion of women misinterpret the intended messages of the current state-mandated BDN. Thus, a revised notification at a lower reading grade level may improve understanding of breast density, leading to improved individualized breast cancer screening for women with dense breasts.

Why does decomposed audit proposal readability differ by audit firm size? A Coh-Metrix approach

Purpose This paper aims to introduce the emerging artificial-intelligence-based readability metrics (Coh-Metrix) to examine the effects of firm size on audit proposal readability. Design/methodology/approach Coh-Metrix readability measures use emerging computation linguistics technology to better assess document readability. These metrics measure co-relations of words, sentences and paragraphs on multi-dimensions rather than adopting the unidimensional “bag of words” approach that examines words in isolation. Using eight Coh-Metrix orthogonal principal component factors, the authors analyze the Chang and Stone (2019) data set comprised of 370 hand-collected audit proposals submitted by audit firms for the US state and local governments’ audit service contracts. Findings Audit firm size has a significant impact on the readability of audit proposals. Specifically, as measured by the traditional readability metric, the proposals from smaller firms are more readable than those submitted by larger firms. Furthermore, decomposed readability metrics indicate that smaller firm proposals evidence stronger (deep) text cohesion, whereas larger firm proposals evidence a stronger narrative structure and higher connectivity (relational indicators) among proposal elements. Unlike the traditional readability metric, however, the emergent readability metrics are uncorrelated with auditor selection. Research limitations/implications Work remains to develop and validate Coh-Metrix measures that are specific to the context of accounting and auditing practice. Future research can use emerging readability measures to examine various textual features (e.g. text cohesion) in finance or accounting related documents. Practical implications The results provide practitioners with insight into the proposal writing strategies and practices of larger and smaller firms. In addition, the results highlight the differing audit firm selection outcomes from traditional and Coh-Metrix readability metrics. Originality/value This study introduces new data and holistic readability measures to the auditing literature.

Proposal readability, audit firm size and engagement success

Purpose This paper aims to examine the effects of firm size on audit proposal readability and audit proposal readability on auditor selection using readability metrics. Design/methodology/approach Adopting the Flesch reading ease readability formula, the authors analyze the readability of 370 hand-collected audit proposals submitted by audit firms for US state and local governments’ audit service contracts. Findings The authors find differences in readability across audit firm size, specifically the proposals written by smaller firms are more readable than those submitted by larger firms. The results further indicate that readability metrics correlate with auditor selection, i.e. an increase in audit proposal readability from the first to third quartile improves the likelihood of a firm winning the engagement by about 6 per cent, ceteris paribus. In addition, while audit fees and an existing auditor–client relationship are associated with engagement success, proxies for audit quality (i.e. audit firm size, audit experience of lead partner) are not. Research limitations/implications The Flesch reading ease measure is a simple linear combination of text attributes, which assumes that readability is a single, unidimensional construct. Simple readability metrics, such as the Flesch reading ease, may confound environmental complexity with readability. Practical implications Readability improves audit proposal success. Originality/value The results provide insight to accounting stakeholders regarding the potential influence of readability on audit firm selection. In short, readability matters.

CAPS: a supervised technique for classifying Stack Overflow posts concerning API issues

The design and maintenance of APIs (Application Programming Interfaces) are complex tasks due to the constantly changing requirements of their users. Despite the efforts of their designers, APIs may suffer from a number of issues (such as incomplete or erroneous documentation, poor performance, and backward incompatibility). To maintain a healthy client base, API designers must learn these issues to fix them. Question answering sites, such as Stack Overflow (SO), have become a popular place for discussing API issues. These posts about API issues are invaluable to API designers, not only because they can help to learn more about the problem but also because they can facilitate learning the requirements of API users. However, the unstructured nature of posts and the abundance of non-issue posts make the task of detecting SO posts concerning API issues difficult and challenging. In this paper, we first develop a supervised learning approach using a Conditional Random Field (CRF), a statistical modeling method, to identify API issue-related sentences. We use the above information together with different features collected from posts, the experience of users, readability metrics and centrality measures of collaboration network to build a technique, called CAPS, that can classify SO posts concerning API issues. In total, we consider 34 features along eight different dimensions. Evaluation of CAPS using carefully curated SO posts on three popular API types reveals that the technique outperforms all three baseline approaches we consider in this study. We then conduct studies to find important features and also evaluate the performance of the CRF-based technique for classifying issue sentences. Comparison with two other baseline approaches shows that the technique has high potential. We also test the generalizability of CAPS results, evaluate the effectiveness of different classifiers, and identify the impact of different feature sets.

National society-supplied patient materials on prostate cancer screening for the general public: A readability analysis.

e23188 Background: Recent data suggest that prostate cancer mortality may be increasing for the first time in 20 years, possibly related to a decrease in PSA screening. Most professional societies recommend men aged 55 to 69 make screening decisions using a shared-decision making (SDM) model. However the quality and time of SDM can vary significantly, often leaving patients uncertain about the implications of pursuing or foregoing screening. In this setting of individualized decisions, patients may seek supplemental information. However, little is known about the accuracy, breadth and readability of available education materials, which if designed optimally could improve SDM. Methods: For 11 predetermined professional societies (ACP, ACPM, ACS, AFP, AMA, ASCO, AUA, CDC, NCI, NIH, USPSTF), patient education materials designed for prostate cancer screening were queried. Materials were analyzed using seven well-validated readability metrics and evaluated for educational content. For content comparison, 10 materials from academic cancer centers and independent organizations were identified using top public search engine results. Results: We identified 12 professional society materials and 20 from popular non-society sources. Mean readability level for society materials ranged from 9.7 to 11.3 grade, well above AMA and NIH recommended 6th and 8th grade reading levels (P &lt; 0.0001). In comparison to general materials, professional society materials more often discussed the role of risk factors in making screening decisions (70% vs 100%, P = 0.03). However, society materials did often omit important guideline-based content, including statements that decisions should involve patient values and preferences (42%) and can vary by patient age (35%). Conclusions: Widely available prostate cancer screening education materials are written at inappropriately high reading levels and often omit important content. Improvement of such materials would encourage SDM for patients considering screening.[Table: see text]

Readability of Online Patient Educational Materials Related to Breast Lesions Requiring Surgery

Purpose To evaluate readability of websites that are commonly accessed for information on breast lesions requiring surgery. Materials and Methods An internet search using three malignant and eight nonmalignant breast lesions that traditionally require lumpectomy or excisional biopsy as search terms was conducted to identify websites commonly accessed for patient information on breast lesions requiring surgery. Nine websites with information on breast diagnoses were identified based on search engine results for each breast lesion queried. Available patient-directed information was downloaded for each lesion from each website on May 15, 2018. Grade-level readability of downloaded content for each lesion was then determined by using generalized estimating equations, with observations nested within readability metrics from each website. Readability of associated terms breast biopsy, breast cancer, and breast surgery was also evaluated with the same method. Results were compared with American Medical Association (AMA) recommended readability parameters (sixth-grade reading level). All interval estimates were calculated for 95% confidence. Results Average grade level readability score of health information on breast lesions requiring surgery was 11.7, which exceeded the AMA parameters. Information on Wikipedia was written at the highest reading level (grade level readability score, 14.2), while information on the National Institutes of Health website ( http://cancer.gov ) was written at the lowest reading level (grade level readability score, 9.7). Educational materials on malignant breast lesions (grade level readability score, 12.3) were written at a higher reading level than were those on nonmalignant breast lesions (grade level readability score,11.4). Information on the terms breast biopsy (grade level readability score, 10.9), breast cancer (grade level readability score, 10.6), and breast surgery (grade level readability score, 11.1) were all written above a sixth-grade reading level. Conclusion Readability of current online resources on breast biopsy lesions traditionally requiring surgery may be too complex for the general public to comprehend, leading to misinformation and confusion. © RSNA, 2019 See also the editorial by Haygood in this issue.

Breast Density Notification Letters and Websites: Are They Too “Dense”?

PurposeTo evaluate grade-level readability of dense breast notification letters (DBNs) and popular websites. MethodsHIPAA-compliant, institutional review board–exempt study. As of April 2018, letter characteristics and grade-level readability were evaluated from states with mandated text using five readability metrics, one of which was the Flesch-Kincaid Grade Level. For states that had mandated DBNs in 2016, the 2016 data were compared with 2018. Readability was also assessed for common websites about dense breasts. ResultsThirty states had mandated text for DBNs. All were written above a Flesch-Kincaid sixth-grade level. Eight state DBNs were around or below a Flesch-Kincaid eighth-grade level. Connecticut was the highest (19.4) and Alabama and New York lowest (both at 7.2). For all states, the mean readability score using the five metrics exceeded an eighth-grade level. Of states that had updated DBNs since 2016, only one state significantly improved readability (Missouri 13.1 to 8.5). All DBNs discussed that breast density may mask cancer on a mammogram, 20 discussed the association with increased risk of breast cancer, and 23 discussed supplemental screening. For websites, the range of Flesch-Kincaid grade-level readability was 6 to 11.3. The lowest was the American Cancer Society dense breast website (6.0) followed by ACR dense breast patient pamphlet (7.2). ConclusionAs of 2018, the mean readability score using five metrics for all state-mandated DBNs exceeded an eighth-grade reading level. Compared with 2016, only one state significantly decreased DBN grade-level readability. Publicly available websites performed relatively better.

Quality of Patient Education Materials on Safe Opioid Management in the Acute Perioperative Period: What Do Patients Find Online?

Guidelines on postoperative pain management recommend inclusion of patient and caregiver education on opioid safety. Patient education materials (PEMs) should be written at or below a sixth grade reading level. We designed this study to compare the readability of online PEMs related to postoperative opioid management produced by institutions with and without a regional anesthesiology and acute pain medicine (RAAPM) fellowship. With institutional review board exemption, we constructed our cohort of PEMs by searching RAAPM fellowship websites from North American academic medical centers and identified additional websites using structured Internet searches. Readability metrics were calculated from PEMs using the TextStat 0.4.1 textual analysis package for Python 2.7. The primary outcome was the Flesch-Kincaid Grade Level (FKGL), a score based on words per sentence and syllables per word. We also compared fellowship-based and nonfellowship PEMs on the presence or absence of specific content-related items. PEMs from 15 fellowship and 23 nonfellowship institutions were included. The mean (SD) FKGL for PEMs was grade 7.84 (1.98) compared with the recommended sixth grade level (P < 0.001) and was not different between groups. Less than half of online PEMs contained explicit discussion of opioid tapering or cessation. Disposal and overdose risk were addressed more often by nonfellowship PEMs. Available online PEMs related to opioid management are beyond the recommended reading level, but readability metrics for online PEMs do not differ between fellowship and nonfellowship groups. More than two-thirds of RAAPM fellowship programs in North America are lacking readable online PEMs on safe postoperative opioid management.

Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach.

Existing graph layout algorithms are usually not able to optimize all the aesthetic properties desired in a graph layout. To evaluate how well the desired visual features are reflected in a graph layout, many readability metrics have been proposed in the past decades. However, the calculation of these readability metrics often requires access to the node and edge coordinates and is usually computationally inefficient, especially for dense graphs. Importantly, when the node and edge coordinates are not accessible, it becomes impossible to evaluate the graph layouts quantitatively. In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images. A convolutional neural network architecture is proposed and trained on a benchmark dataset of graph images, which is composed of synthetically generated graphs and graphs created by sampling from real large networks. Multiple representative readability metrics (including edge crossing, node spread, and group overlap) are considered in the proposed approach. We quantitatively compare our approach to traditional methods and qualitatively evaluate our approach by showing usage scenarios and visualizing convolutional layers. This paper is a first step towards using deep learning based methods to quantitatively evaluate images from the visualization field.

Semantic based neural model approach for text simplification

The machine translation systems affect by various difficulties like long-distance dependency and long sentences having complex syntax. Text Summarization (TeSu) and Text Simplification (TeSi) are the important ways of simplifying the text for users who are having the poor reading capability, including non-native speakers, functionally illiterate and children. TeSu produce a brief summary of the main ideas of the text, while TeSi aims to reduce the linguistic complexity of the text and retain the original meaning. In many text generation tasks, sequence-to-sequence model depends upon approaches of TeSu and TeSi achieves more success, recently. Text data have low Semantic Relevance (SR), but the Simplified Text (SiTs) which generate from the Source Text (SoTs) are more similar. The goal of the paper is to work for TeSu and TeSi, for improving the SR between the original texts and the modified texts. The proposed method encouraging high semantic similarity between texts and summaries by implementing SR based Neural model (SRN). The encoder represents the SoT, whereas, the decoder produced the summary representation. During training, the representation provides maximum Similarity Score (SS) and the experi-ments conducted on the approach using two benchmark datasets. The experimental results showed that SNR approach provided better per-formance compared to the existing method in terms of metrics such as readability metrics, human-sentence level evaluation, and Post Editing (PE) evaluation.

Sentiments and Semantics: A Review of the Content Analysis Literature in the Era of Big Data

Drilling into data to understand property as a financial investment is one of the primary considerations of real estate research. This paper is designed to shed light on some of the newer and more innovative studies by highlighting the use qualitative data and content analysis throughout the literature to enhance our understanding of property. Particular attention is placed on linguistics research including readability metrics, semantic analysis, and sentiment analysis. Ocular tracking is also addressed as a way to evaluate unstructured forms of data. These methods are rigorous enough to be used on their own or as supplements to conventional pricing models, subject to the requirements of the research.

A Normal Thyroid by Any Other Name: Linguistic Analysis of Statements Describing a Normal Thyroid Gland from Noncontrast Chest CT Reports

PurposeTo determine the variability and readability of language used in chest CT reports to describe a “normal” thyroid gland. MethodsUsing a semi-automated process, we identified unique sentences or phrases describing a “normal” thyroid gland in 11,357 noncontrast chest CT reports. Readability metrics were computed for each descriptor, including sentence length and grade-level indices quantifying the education necessary for text comprehension. Grade-level indices included the Flesch-Kincaid (FK) grade level, Gunning Fog index (GF), Coleman-Liau (CL) index, automated readability index (ARI), and a computed composite grade level (CGL) calculated as (FK + GF + CL + ARI)/4. ResultsThere were 342 unique “normal” thyroid descriptors identified among 6,957 noncontrast chest CT reports characterizing a normal thyroid gland. For these 342 unique descriptors, sentence length varied 23-fold, with a mean sentence length of 8.3 ± 5.1 words. CGL varied 4-fold, with a mean of 16.4 ± 4.5, suggesting that descriptors for a normal thyroid gland, on average, require an advanced college-level education for comprehension. ConclusionsThe language used by radiologists to describe a normal thyroid gland in chest CT reports is variable and complex. The linguistic characteristics observed herein may be a surrogate for the broader readability of radiology reports. With the growing role of the radiology report in patient communication, further linguistic analysis of reporting language may provide valuable insight for optimizing radiology communication.

An assessment into the characteristics of award winning papers at CHI

The overall readability of CHI publications is not known. In addition, little is understood about what lexical or demographic characteristics are unique to award winning papers at CHI and if they are significantly different from non award winning papers. We therefore carry out an exploration and assessment into the readability metrics as well as a meta analysis of 382 full papers and 54 notes from the 2014, 2015, 2016 and 2017 editions at CHI. Our results illustrate that notes did not have any significant trends whatsoever. On the other hand, award winning full papers were shown to have lower readability as compared to non award winning full papers. The type of research contribution played an important role; such that award winning full papers were significantly more likely to have a theoretical contribution as compared to non award winning full papers and full papers that presented an artifact as their contribution were more readable than other full papers. Our demographic analysis of authors indicated that the experience of authors nor their region of affiliation were not associated with the likelihood of their full paper being awarded. The experience of authors did not effect the overall readability of full papers however the region of affiliation did have a significant influence on the overall readability of full papers. In conclusion, we speculate on our obtained results through linkages with prior work in readability analysis.

Harnessing Reddit to Understand the Written-Communication Challenges Experienced by Individuals With Mental Health Disorders: Analysis of Texts From Mental Health Communities

BackgroundMental disorders such as depression, bipolar disorder, and schizophrenia are common, incapacitating, and have the potential to be fatal. Despite the prevalence and gravity of mental disorders, our knowledge concerning everyday challenges associated with them is relatively limited. One of the most studied deficits related to everyday challenges is language impairment, yet we do not know how mental disorders can impact common forms of written communication, for example, social media.ObjectiveThe aims of this study were to investigate written communication challenges manifest in online mental health communities focusing on depression, bipolar disorder, and schizophrenia, as well as the impact of participating in these online mental health communities on written communication. As the control, we selected three online health communities focusing on positive emotion, exercising, and weight management.MethodsWe examined lexical diversity and readability, both important features for measuring the quality of writing. We used four well-established readability metrics that consider word frequencies and syntactic complexity to measure writers’ written communication ability. We then measured the lexical diversity by calculating the percentage of unique words in posts. To compare lexical diversity and readability among communities, we first applied pairwise independent sample t tests, followed by P value adjustments using the prespecified Hommel procedure to adjust for multiple comparison. To measure the changes, we applied linear least squares regression to the readability and lexical diversity scores against the interaction sequence for each member, followed by pairwise independent sample t tests and P value adjustments. Given the large sample of members, we also report effect sizes and 95% CIs for the pairwise comparisons.ResultsOn average, members of depression, bipolar disorder, and schizophrenia communities showed indications of difficulty expressing their ideas compared with three other online health communities. Our results also suggest that participating in these platforms has the potential to improve members’ written communication. For example, members of all three mental health communities showed statistically significant improvement in both lexical diversity and readability compared with members of the OHC focusing on positive emotion.ConclusionsWe provide new insights into the written communication challenges faced by individuals suffering from depression, bipolar disorder, and schizophrenia. A comparison with three other online health communities suggests that written communication in mental health communities is significantly more difficult to read, while also consisting of a significantly less diverse lexicon. We contribute practical suggestions for utilizing our findings in Web-based communication settings to enhance members’ communicative experience. We consider these findings to be an important step toward understanding and addressing everyday written communication challenges among individuals suffering from mental disorders.

Privacy Policies for Apps Targeted Toward Youth: Descriptive Analysis of Readability.

BackgroundDue to the growing availability of consumer information, the protection of personal data is of increasing concern.ObjectiveWe assessed readability metrics of privacy policies for apps that are either available to or targeted toward youth to inform strategies to educate and protect youth from unintentional sharing of personal data.MethodsWe reviewed the 1200 highest ranked apps from the Apple and Google Play Stores and systematically selected apps geared toward youth. After applying exclusion criteria, 99 highly ranked apps geared toward minors remained, 64 of which had a privacy policy. We obtained and analyzed these privacy policies using reading grade level (RGL) as a metric. Policies were further compared as a function of app category (free vs paid; entertainment vs social networking vs utility).ResultsAnalysis of privacy policies for these 64 apps revealed an average RGL of 12.78, which is well above the average reading level (8.0) of adults in the United States. There was also a small but statistically significant difference in word count as a function of app category (entertainment: 2546 words, social networking: 3493 words, and utility: 1038 words; P=.02).ConclusionsAlthough users must agree to privacy policies to access digital tools and products, readability analyses suggest that these agreements are not comprehensible to most adults, let alone youth. We propose that stakeholders, including pediatricians and other health care professionals, play a role in educating youth and their guardians about the use of Web-based services and potential privacy risks, including the unintentional sharing of personal data.

Investigating the use of readability metrics to detect differences in written productions of learners: a corpus-based study

This paper deals with the use of readability metrics as indices of learmers' linguistic features in a written corpus of Spanish learners of English L2. Seventeen measures of readability are presented and computed for 200 samples of written argumentative essays extracted from the corpus NOCE (Díaz-Negrillo, 2007). Support Vector Machines (SVM) are used in order to detect which are the metrics that perform better at detecting differences in learners’ productions belonging to students enrolled in the first or in the second year of an English major. Metrics based on sentence length, number of sentences, and number of polysyllabic words are reported to be the most accurate ones for the classification of learners' linguistic features.

Validating Automated Measures of Text Complexity

AbstractAutomated text complexity measurement tools (also called readability metrics) have been proposed as a way to help teachers, textbook publishers, and assessment developers select texts that are closely aligned with the new, more demanding text complexity expectations specified in the Common Core State Standards. This article examines a critical element of the validity arguments presented in support of proposed metrics: the claim that criterion text complexity scores developed from students’ responses to reading comprehension test items are reflective of the difficulties actually experienced by students while reading. Evidence that fails to support this assertion is examined, and implications relative to the goal of obtaining valid, unbiased evidence about the measurement properties of proposed readability metrics are discussed.

Readability, content analysis, and racial/ethnic diversity of online living kidney donation information.

More than three-fourths of adults in the USA use the Internet to access health-related information. Adults exploring the possibility of living donation should have access to online content that is readable and comprehensive. We simulated a search of online information about living kidney donation and evaluated readability, topics covered, and racial/ethnic diversity of 21 websites meeting inclusion criteria (eg, hosted by a nonprofit or patient advocacy organization, English content, based in USA). Using standard readability metrics, 62% of sites were classified as "Difficult to read" and none achieved the recommended reading level of sixth grade. On average, websites covered 18.5 (62%) of 30 recommended information topics (range: 7 to 28) and only 2.1 (23%) of 9 racial/ethnic diversity items (range: 0 to 6). Overall, the most common nonprofit or patient advocacy organization websites do not meet the readability standards established by the National Institutes of Health and the American Medical Association, many lack fundamental information about living kidney donation, and most are not racially/ethnically diverse. We encourage the transplant community to consider playing a more active role in improving the overall quality of online information disseminated to the general public. Further, there is a need to more critically examine the accuracy of online living donation content in future investigations.