Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts

Redmiles, Elissa M.; Maszkiewicz, Lisa; Hwang, Emily; Kuchhal, Dhruv; Liu, Everest; Morales, Miraida; Peskov, Denis; Rao, Sudha; Stevens, Rock; Gligorić, Kristina; Kross, Sean; Mazurek, Michelle L.; Daumé, Hal

doi:10.18653/v1/d19-1489

Cited by 6 publications

(7 citation statements)

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“…Text structure. We first adopt three readability metrics that have been widely used by other studies [29,64] for measuring the document readability, namely Automated Readability Index (ARI), Flesch Readability Ease Score (FRES), and Laesbarheds Index (LIX). The first metric, i.e., ARI, is used to assess the required reader's education level to understand a document, and the other two, i.e., FRES and LIX, assess the difficulty level of the document based on its average number of syllables per word, total words, and total sentences.…”

Section: Readability Assessment Methodsmentioning

confidence: 99%

On the Quality of Privacy Policy Documents of Virtual Personal Assistant Applications

Yan,

Xie,

Meng

et al. 2024

PoPETs

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An app ecosystem built around virtual personal assistant (VPA) services becomes flourishing in recent years, fueled by the booming of the Internet of Things (IoT). A large number of functionality-rich VPA applications (or apps for short) have been released through app stores, and become easily-accessible by users through their smart speakers. In response to the increasingly stringent data protection regulations around the world, VPA service providers require app developers to include a privacy policy that declares their data handling practices. These privacy policies serve as the de facto agreement between developers and users, and may be taken as the basis in resolving conflicts in the event of a data breach. Therefore, it is essential that privacy policy documents are crafted in a clear, easy-to-understand, and unambiguous way. In this work, we conduct the first systematic study on the quality of privacy policies in the VPA app domain. Based on our review of literature and documents from standard working groups, we identify four metrics that enable the quality of the privacy policy to become measurable, including timeliness, availability, completeness and readability. We then develop QuPer, which extracts the meta features (e.g., update history) and linguistic features (e.g., sentence semantics) from privacy policies, and assesses their quality. Our analysis reveals that the status of the quality of privacy policies in the VPA app domain is concerning. For instance, only 1.17% of privacy policies completely cover all contents that are regarded as privacy concerns by legislation (e.g., GDPR article 13) and relevant literature. Our findings are expected to raise an alert among the VPA app developers and provide them with guidelines for creating high-quality privacy policy documents. We also encourage app store operators to implement a vetting process that ensures the quality of privacy policies before apps are released to the public.

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Section: Readability Assessment Methodsmentioning

confidence: 99%

On the Quality of Privacy Policy Documents of Virtual Personal Assistant Applications

Yan,

Xie,

Meng

et al. 2024

PoPETs

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show abstract

“…Since a single, “gold‐standard” technique is yet to be identified, the readability of documents such as privacy policies and medical documents is commonly measured using more than one scoring mechanism (Bowers et al, 2017). Readability scores can be calculated using Microsoft Word Office Package (Microsoft Corp, Redmond, WA), Readability Calculations (Micro Power & Light Co., Dallas, TX), Readability Studio 1.1 (Oleander Solutions, Vandalia, OH; Badarudeen & Sabharwal, 2010), and Python libraries like TextStat (Gao, 2020; Gao et al, 2020; Kumar et al, 2017; Park et al, 2017; Redmiles et al, 2018) and Readability (Lei & Yan, 2016; Su et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Since a single, "gold-standard" technique is yet to be identified, the readability of documents such as privacy policies and medical documents is commonly measured using more than one scoring mechanism (Bowers et al, 2017). Readability scores can be calculated (Gao, 2020;Gao et al, 2020;Kumar et al, 2017;Park et al, 2017;Redmiles et al, 2018) and Readability (Lei & Yan, 2016;Su et al, 2019).…”

Section: Readability Of Privacy Policiesmentioning

confidence: 99%

An analysis of privacy policies of publicCOVID‐19 apps: Evidence from India

Shukla

2021

Journal of Public Affairs

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Governments around the world are utilizing their digital ecosystems to respond to the COVID-19 pandemic. To increase awareness among the beneficiaries about privacy risks, they must proactively publish the data handling practices for their digital initiatives through appropriate privacy policies. This study analyzes the privacy policies of public COVID-support mobile applications (apps) in the context of India. We found a total of 63 government initiated COVID-support apps in India out of which 38% were found to have an app-specific privacy policy. These policies were analyzed further to assess their coverage of key principles, such as "Purpose," "Data Categories," and "Data Retention," derived from legal requirements. We also analyzed the extent of the specificity of policies with a high coverage. In India, only one nation-wide app stood out to have both considerable coverage of key principles as well as a high level of specificity. Other national/regional apps fail to display the desired levels of coverage and/or specificity. The broader policy recommendations of this study are that the government should better address privacy concerns regarding its existing and future disaster management apps as well as its other digital initiatives by (a) establishing and enforcing a comprehensive legislative framework for data protection and (b) increasing privacy awareness among the beneficiaries.

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“…Agar siswa mampu memperoleh pemahaman materi dan mencapai kompetensi, perlu memilih dan menyusun buku ajar yang sesuai dengan tingkat kelas (Nassiri, 2023). Tingkat keterbacaan teks bisa menjadi salah satu patokan untuk mengetahui kualitas buku ajar sebagai sumber informasi sesuai dengan tingkat kelas dan kemampuan membaca di setiap kelas (Alarcon, 2020;Gkikas, 2022;Martinc, 2021;Redmiles, 2019), khususnya pada tingkat kelas 9. Sebab, keterbacaan merupakan faktor penting yang berkaitan erat dengan pemahaman.…”

Section: Pendahuluanunclassified

Analisis Keterbacaan Teks Buku Ajar Bahasa Indonesia SMP Kelas 9 Menggunakan Formula Grafik Fry

Aliyah,

Fadila,

Nurjamilah

2024

ptk

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Tiap-tiap sekolah tentunya memiliki buku teks untuk menunjang dan melaksanakan kurikulum serta menjadi sumber dalam membagikan ilmu pengetahuan. Pemilihan dan persiapan buku teks sangat penting bagi guru dan praktisi pendidikan, terutama untuk mengukur tingkat keterbacaan suatu teks sebelum digunakan sebagai sumber pembelajaran. Penelitian ini bertujuan untuk memahami dan menjelaskan keterbacaan teks buku pelajaran Bahasa Indonesia SMP kelas 9 dengan grafik fry. Penelitian ini menggunakan metode deskriptif kualitatif dengan menggunakan teknik observasi, yaitu observasi langsung terhadap data tekstual. Teks yang dijadikan bahan penelitian ini adalah buku pelajaran Bahasa Indonesia kelas 9 kurikulum 2013 revisi 2018 terbitan Kementrian Pendidikan dan Kebudayaan serta buku ajar Bahasa Indonesia Kelas 9 Kurikulum Merdeka terbitan Kementrian Pendidikan, Kebudayaan, Riset, dan Teknologi. Sampel dalam penelitian ini adalah 12 teks, diambil satu teks untuk setiap bab dalam dua buku tersebut. Hasil analisis untuk Edisi Kurikulum 2013 didapatkan data yang sesuai sebesar 50% dan untuk Edisi Kurikulum Merdeka sebesar 83,34%.

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Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts

Cited by 6 publications

References 0 publications

On the Quality of Privacy Policy Documents of Virtual Personal Assistant Applications

On the Quality of Privacy Policy Documents of Virtual Personal Assistant Applications

An analysis of privacy policies of publicCOVID‐19 apps: Evidence from India

Analisis Keterbacaan Teks Buku Ajar Bahasa Indonesia SMP Kelas 9 Menggunakan Formula Grafik Fry

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