Linguistic diversity on the Internet is a global and multilingual phenomenon (Danet & Herring, 2007; Cremades et al., 2021) that has been enhanced by the generalization of social networks and instant messaging applications on smartphones (Yus, 2022). Interactive digital communication has favored the creation of a new written norm that has been called digitalk (Turner, 2010; Verheijen & Spooren, 2021). In fact, digital communication in social networks has shown its potential to raise awareness of situations related to equality (Bates et al., 2020).

In this regard, ageist discriminatory language used in social networks often remains unnoticed even by the seniors (Sánchez-Izquierdo & Fernández-Mayoralas, 2024), although it causes serious harm to this group (Bowman & Lim, 2021). It is precisely in social networks and the media where the proliferation of ageist stereotypes and discourses is detected (Makita et al, 2019), a trend that has been notably increased since COVID-19 (Bravo-Segal & Villar, 2020) and that is characterized by designating seniors with discriminatory words and expressions that infantilize, depersonalize or dehumanize them, as well as by specific sexist expressions to designate senior women. The use of new inclusive language that avoids ageist discrimination is undoubtedly an objective that must be addressed (Gendron et al., 2016).

In the educational context, the UN Sustainable Development Goals expressly call for “ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all” (SDG 4). The education sector is required to achieve this. The solution to the problem of linguistic ageism implies the generation of training proposals that serve to raise awareness both among the seniors themselves and the rest of society, favoring the use of a new inclusive language, as a promotion of lifelong learning opportunities. Digital resources, specifically social networks, are seen as mediating tools to help this (Sánchez-Román et al, 2022).

This paper describes the initial results of a project that aims to generate knowledge about a very significant and ongoing problem: the study and prevention of ageist language on social networks. The solution to the problem is summarized in the creation of a guide of non-ageist inclusive language for digital communication, in the development of a training and sensibilization plan on inclusive digital communication for the senior population, and in the development of an educational application Ed@ling for smartphones, which will contribute to the achievement of a more inclusive society in the context of social networks.

To implement this project, a mixed methodology based on public data mining and sequential explanatory design is being conducted (Ivankova, Creswell & Stick, 2006). This methodology involves the use of digital tracing data to more effectively collect, organize, and analyze generalizable samples of data representing individuals in virtual communication environments (Kimmons & Veletsianos, 2018). In Phase 1, the analysis of discriminatory uses in the digital communication of the seniors is carried out through the selection of influential senior users in social networks and the extraction of a corpus for the analysis of the digital communication of seniors in Spanish language. A catalog of hashtags related to seniors in social networks is elaborated from which the corpus 2 is extracted for the analysis of ageism in digital communication. For this purpose, keywords are identified with Sketch Engine (SE), which is considered suitable for its ability to process natural languages and its exploratory nature (Del Olmo & Arias, 2021). Specifically, the keyness parameter is used to measure the relevance of the topics in each of the two corpora (Firoozeh et al., 2020). A content analysis is also performed (Neuendorf, 2017), which will allow the description of the linguistic and multimodal elements used by older people in social networks. In phase 2, based on the categories and items established in the previous phase, a proposal for a new inclusive language is made. For phase 3, the design of the training proposal is carried out with the aim of raising awareness about ageist discriminatory language in digital communication. The previous phases will feed back into the final phase of the research (phase 4), the creation of an educational application for the prevention of linguistic ageism and the promotion of inclusive language (Ed@ling). Regarding the educational application, the implementation of the methodology would consist of the development of a system, based on machine learning and artificial intelligence techniques, of recommendation included within a keyboard for Android or iPhone devices.

The first results of the project point to a stereotypical representation of the senior citizens in social networks that must be addressed through educational efforts. First of all, the data show that almost 20% of the messages on Twitter that referred to this group contained ageist language. From 2021 onwards, coinciding with the end of COVID-19, there is a notable increase in ageist language on Twitter, which exceeds 25% of the tweets in the sample in 2021, 2022 and 2023. One of the conclusions of this research is that the pandemic favoured the use of ageist language in Spanish on social networks, and that this trend continues today. Ageism on Twitter in Spain is associated with words like grandpa, bored, nursing home, grandpa’ stories, nonsense, bus, excursion, bingo, pétanque, card games, crochet, curmudgeonly, ridiculous, senile, gone a bit doolally, pensioner, Birdie Song, pill, medication, synthrom, viagra, tension, rheuma, prostate. Secondly, ageist language on Twitter is fundamentally based on depersonalisation mechanisms that represent older people as a homogeneous, unproductive and idle group (‘Let’s see, the Inserso, who give you a free sandwich, a free bus and a walk with a banner . . . Go piss and shit your pants ’.). We also identified dehumanisation based on ageist insults, the restriction of older people’s autonomy and the denial of their rights as one of the characteristics of ageism in social networks (‘74 years old . . . His job is to watch plays, play petanque and travel around, so he can stop being a pain in the ass’.). Finally, our results confirm that there is double sexist and ageist discrimination in the tweets analysed, since the language used on Twitter is sexist, ageist and discriminatory when representing older women.

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