Devereux Readability Index

Smith, Edgar A.

doi:10.1080/00220671.1961.10882728

Cited by 18 publications

(16 citation statements)

References 1 publication

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“…The total number of words in a firm's CSR report after removing unprintable characters, normalizing formatting, and removing uninformative content S Lu Syntactic Complexity (Lu 2010) Total number of sentences across all pages word_length_mean Flesch (1948); Smith (1961) Sum of the lengths of all words on a page, averaged across all pages Mean AoA Gilhooly and Logie (1980) The average age-of-acquisition for words, averaged across all pages Mean Flesch-Kinkaid grade level Kincaid et al (1975) The…”

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confidence: 99%

A Textual Analysis of US Corporate Social Responsibility Reports

Clarkson

Ponn²,

Richardson

et al. 2020

Abacus

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We employ computer‐based textual analysis to examine disclosure patterns for a sample of US corporate social responsibility (CSR) reports from the period 2002–2016. Starting from 466 features commonly used in computational linguistics, our results show that the linguistics or disclosure patterns in CSR reports can be used to accurately predict the actual CSR performance type of CSR reporters. Specifically, we find that the two most commonly used disclosure characteristics, number of words and number of sentences, alone can be used to predict reporting firms’ CSR performance type with 81% accuracy. The accuracy of prediction increases to 96% when the top 50 linguistics features most relevant to firms’ CSR performance are included in the prediction model. In addition, we find that the linguistic features of CSR disclosure identified by our study are incrementally value relevant to investors even after controlling for the actual CSR performance score from the professional CSR rating agencies. This finding suggests that the linguistic features of CSR disclosure can be an important venue for capital market participants in evaluating firms’ CSR performance type, especially when professional CSR performance ratings are not available.

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confidence: 99%

A Textual Analysis of US Corporate Social Responsibility Reports

Clarkson

Ponn²,

Richardson

et al. 2020

Abacus

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“…Currently, the assessment of the quality of health resources for readers is based on established readability tools and formula including Flesch Reading Ease Score [ 17 ], Gunning Fog [ 18 ], Flesch-Kincaid Grade Level, Coleman-Liau Index [ 19 ], SMOG Index [ 20 , 21 ], Automated Readability Index [ 22 ], Linseed Write Formula. The advantages of these tools consist in their convenience and rapid assessment of the suitability of the written materials for people with varying educational levels.…”

Section: Methodsmentioning

confidence: 99%

Assessing Communicative Effectiveness of Public Health Information in Chinese: Developing Automatic Decision Aids for International Health Professionals

Bodomo

Xie

et al. 2021

IJERPH

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Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities and inequalities in developed and developing countries. Health information communication from the World Health Organization is governed by key principles including health information relevance, credibility, understandability, actionability, accessibility. Multilingual health information developed under these principles provide valuable benchmarks to assess the quality of health resources developed by local health authorities. In this paper, we developed machine learning classifiers for health professionals with or without Chinese proficiency to assess public-oriented health information in Chinese based on the definition of effective health communication by the WHO. We compared our optimized classifier (SVM_F5) with the state-of-art Chinese readability classifier (Chinese Readability Index Explorer CRIE 3.0), and classifiers adapted from established English readability formula, Gunning Fog Index, Automated Readability Index. Our optimized classifier achieved statistically significant higher area under the receiver operator curve (AUC of ROC), accuracy, sensitivity, and specificity than those of SVM using CRIE 3.0 features and SVM using linguistic features of Gunning Fog Index and Automated Readability Index (ARI). The statistically improved performance of our optimized classifier compared to that of SVM classifiers adapted from popular readability formula suggests that evaluation of health communication effectiveness as defined by the principles of the WHO is more complex than information readability assessment. Our SVM classifier validated on health information covering diverse topics (environmental health, infectious diseases, pregnancy, maternity care, non-communicable diseases, tobacco control) can aid effectively in the automatic assessment of original, translated Chinese public health information of whether they satisfy or not the current international standard of effective health communication as set by the WHO.

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“…Consequently, we used document readability as another linguistic feature to identify suspicious stock recommendations. We measured text readability by calculating the automated readability index (ARI), the Flesch Reading Ease Score (Flesch), and the Fog Index (Fog), which are provided by Equations ( 2), (3), and (4), respectively (Hu, Bose, Koh, & Liu, 2012;Loughran & McDonald, 2010;Smith & Senter, 1967). The ARI, as calculated by Equation (2) 2 below, has been used in the context of manipulation detection (Hu et al, 2012): We examined the sentiments expressed in stock recommendations using an unsupervised, dictionarybased approach (Zhou & Chaovalit, 2008).…”

Section: Design Feature Df1b: Document Transformation With Linguistic...mentioning

confidence: 99%

Design Principles for Robust Fraud Detection: The Case of Stock Market Manipulations

Siering¹,

Muntermann²,

Grčar³

2021

JAIS

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We address the challenge of building an automated fraud detection system with robust classifiers that mitigate countermeasures from fraudsters in the field of information-based securities fraud. Our work involves developing design principles for robust fraud detection systems and presenting corresponding design features. We adopt an instrumentalist perspective that relies on theory-based linguistic features and ensemble learning concepts as justificatory knowledge for building robust classifiers. We perform a naive evaluation that assesses the classifiers' performance to identify suspicious stock recommendations, and a robustness evaluation with a simulation that demonstrates a response to fraudster countermeasures. The results indicate that the use of theory-based linguistic features and ensemble learning can significantly increase the robustness of classifiers and contribute to the effectiveness of robust fraud detection. We discuss implications for supervisory authorities, industry, and individual users.

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Devereux Readability Index

Cited by 18 publications

References 1 publication

A Textual Analysis of US Corporate Social Responsibility Reports

A Textual Analysis of US Corporate Social Responsibility Reports

Assessing Communicative Effectiveness of Public Health Information in Chinese: Developing Automatic Decision Aids for International Health Professionals

Design Principles for Robust Fraud Detection: The Case of Stock Market Manipulations

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