100Fluencers Study – “Analysis of stereotypes of ageing in digital media”

#100Fluencers is a pioneering initiative promoted by MatiaZaleak whose main objective is to raise awareness and transform the way ageing and older people are represented in digital media. It stems from concern about the impact of ageism—a form of age discrimination—on the collective imagination, especially in a media context where the dominant discourse tends to be reductionist, paternalistic, or invisible to the diversity of old age.

This proposal is developed from a critical and inclusive perspective that understands ageing as a complex, heterogeneous, and socially valuable process. The project combines rigorous research, technological tools and awareness-raising actions to generate knowledge, provide practical resources and promote cultural change in the media's treatment of older people.

Through the analysis of more than 1,200 digital news items, #100Fluencers identifies narrative and visual stereotypes, measures their symbolic impact and proposes alternatives to promote fairer communication. It also provides journalists, organisations and the general public with resources such as a bank of non-ageist images, a guide to AI prompts for detecting bias, and informative audiovisual content such as the #NoSonCosasDeLaEdad campaign.

With the support of public and private institutions, the project aims to establish itself as a benchmark in the fight against ageism in the digital sphere, promoting a more inclusive, pluralistic and respectful society at all stages of life.

Objectives

The overall purpose of the #100Fluencers study is to contribute to the transformation of media narratives on ageing through a systematic and critical analysis of how older people are represented in digital media. The project is based on the premise that the media not only reflect reality, but also construct it symbolically, generating social imaginaries that have a direct impact on attitudes, public policies and the self-perception of older people.

Faced with a media landscape dominated by ageist, stereotypical or outright invisible discourses on old age, #100Fluencers proposes an approach that integrates Artificial Intelligence tools with qualitative and critical interpretation to identify the most frequent narrative and visual patterns, as well as the biases and distortions that are reproduced in the representation of this population group.

The study is proposed as a diagnostic and action tool, not only to understand how social images of old age are constructed, but also to offer strategies that allow for a reframing of the presence of older people in the digital space from a perspective of rights, diversity and inclusion.

General objective:

• To analyse how older people are represented in digital media, looking at both textual content and images.

Specific objectives:

• To identify and classify the most common narrative approaches used in digital news stories that address issues related to older people, evaluating their tone (positive, negative, neutral or ambiguous) and their symbolic implications.
• To detect ageist stereotypes in both textual language and visual representations (images, photographs, graphics), analysing how these elements reinforce or contradict the content of the news stories.
• To evaluate the narrative coherence between text and image in the news, assessing whether the visual elements accompany the informative content in an appropriate and respectful manner or whether they reinforce visual stereotypes of passivity, fragility or dependence.
• To examine the diversity in the representation of older people based on variables such as gender, social context, functionality, cultural belonging or community participation.
• Analyse the role of the type of media (generalist, regional, independent) in shaping media discourse, observing how editorial frameworks influence the construction of the narrative on ageing.
• Formulate specific and applicable recommendations aimed at journalists, media outlets, institutional communicators, and social entities to improve media coverage of ageing, promoting ethical, inclusive, and pluralistic communication.
• Develop practical tools based on artificial intelligence and visual communication to facilitate critical analysis of content, such as a bank of non-ageist images and prompts to detect narrative and visual biases.