Gender Disinformation on Twitter/X: Strategies and Mechanisms

Abstract

Objective: Identify the mechanisms and strategies used in the dissemination of gender disinformation, based on the interpretation of user/interactor elements, formats, and forms that constitute the context of gender disinformation..

Methods: Data collection began through advanced searches, using descriptors and the names of female politicians combined with the terms "fake news," linking "and" and "disinformation." This procedure was conducted from July 2022 to October 30, 2022. Additionally, fact-checking agencies such as Lupa and Aos Fatos were consulted to identify which disinformation was verified during this period.

Results: In summary, we identified several central points. Gender disinformation manifests in various formats, including manipulated images, distorted texts, and edited or cropped videos. It is observed that men predominated as actors in the dissemination of this disinformation. Pseudonyms are frequently used, providing a veil of anonymity to the perpetrators, making accountability processes difficult. The disinformation cycle involves several interconnected stages, beginning with the production of false content, followed by its diffusion through digital social networks. Initially, dissemination occurs in messaging apps such as WhatsApp, through audios, texts, and videos. Then, disinformation expands to platforms like Twitter/X, where it gains traction by adding links to fake websites, giving it an appearance of credibility. Subsequently, it reaches more visual digital social networks like YouTube, Instagram, and Facebook, where images and photos reinforce the misleading message, further expanding its reach and impact. Gender disinformation often questions the integrity and competence of women, as well as defaming and distorting their achievements. This insidious nature aims to undermine the image and credibility of women in the public sphere. The effects of gender disinformation include the propagation of harmful stereotypes, negatively affecting the public perception of women and their accomplishments.

Conclusions: Twitter/X facilitates political participation but faces challenges such as limited internet access in countries like Brazil. The research revealed difficulties due to changes in the platform, requiring manual data collection. Furthermore, the analysis shows that gender disinformation uses stereotypes to harm women, also affecting other groups like LGBTQIA+. Hate speech amplifies the negative effects on social networks. Algorithmic governmentality, based on big data, shapes behaviors and exacerbates social inequalities.