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With the increased use of online services, the issue of web accessibility has become ever present in society. In January 2021, there were 4.66 billion active Internet users online (Statista, 2021). Initially, the definition of web accessibility revolved around making the World Wide Web accessible to disabled users. However, Berners-Lee made the following famous statement: The power of the Web is in its universality. Access by everyone, regardless of disability, is an essential aspect. This idea set in motion the study and design of webpages that meet the needs of all users (W3C, 2020).
This concept was explored further by Petrie et al. (2015), who analysed 50 definitions of web accessibility. These definitions were taken from a variety of sources, such as standards, papers, guidelines, and online sources, from the period 1996–2014. The analysis highlighted six core concepts, namely, groups of users and their characteristics; what users should be able to do; the characteristics of the website; the technologies used; the design and development of the website; and the characteristics of the situations of use.
Several studies have explored social interactions on social media associated with the topic of web accessibility (Anthony et al., 2013; Keller et al., 2017; Morris et al., 2016; Seo & Jung, 2020; Zhang & Findlater, 2017). However, there is a lack of studies that explore the readability and the sentiments expressed in tweets associated with web accessibility.
Readability refers to the ease with which a reader can read and understand a piece of text. Additional research by Dale and Chall (1949) indicated that readers should be able to read the text at an optimal speed and find it interesting. Several studies (Dale & Chall, 1949; Davis et al., 2019; Firouzjaei & Özdemir, 2020; Hoedebecke et al., 2017; Leonhardt & Makienko, 2017) have investigated the readability of tweets in multiple areas such as politics, marketing, and health care.
Furthermore, Ahmed et al. (2022) extracted 10,000 tweets from U.S. senators and computed the Flesch–Kincaid readability scores of the tweets. The scores were then compared to a previous study that looked at the readability scores for average citizens in the U.S. The results highlighted that the readability scores of the senators were much higher and that educational attainment contributed towards readability.
On a larger scale, Firouzjaei and Özdemir (2020) investigated the effect of readability on positive user engagement for 80,000 political tweets. A combination of Twitter-specific features (e.g., the number of hashtags, the number of mentions, and the number of emoji in the tweet divided by the number of words in the tweet), stylistic features (the number of words in the tweet and the number of characters in the tweet divided by the number of words in the tweet), syntactic features (the number of nouns in the tweet divided by the number of verbs in the tweet), and readability formulas (the Flesch–Kincaid reading ease score, Dale–Chall readability score, and Coleman–Liau index) was used in regression techniques. The study highlighted that the use of these features leads to more accurate and robust predictions regarding readability.