Visual Communication on Social Media: Data, Bias, and Ethical Visual Practice

Social media platforms enable the rapid creation and circulation of visual content. Increasingly, this includes a wide range of data-driven formats, including interactive maps, location-based imagery, spatial infographics, and algorithmically synthesized or AI-generated visuals, all built from and dependent on vast streams of data. Together, visual representation and the data behind it are shaping public debate on issues ranging from climate change and migration to war. Context (whether cultural, political, ideological, etc.) strongly influences how visual content is designed, shared, and interpreted, and how the underlying data is collected, curated, and used to construct visual narratives. This raises urgent ethical questions about whose perspectives become represented as truth, and how bias enters through data sources, classification choices, and visual design conventions. These questions cut across visual rhetoric, critical cartography, data ethics, and communication theory.This Research Topic examines how visual communication on social media crafts narratives that drive public discourse, often amplifying cultural biases and misinformation. Data is not a neutral substrate here: the chain from collection and classification to visualisation or algorithmic synthesis shapes what audiences see, believe, and act on. Key challenges therefore span two interconnected domains: ethically compromised data practices, such as unrepresentative training datasets and opaque classification systems; and culturally insensitive visual design, including biased symbology in maps, charts, and infographics. These risks begin in the data long before they appear on screen.This Research Topic will offer concepts, methods, and design guidance for ethical visual communication on social media, with data practices treated as inseparable from the visuals they produce. Themes of particular interest include, but are not limited to, the following:• the data practices behind social media visuals (including collection, classification, and curation) as sources of bias and inequitable representation, and the ethical frameworks needed to address them• cultural assumptions embedded in the visual design of social media content, including AI-generated imagery and data-driven visualisation, and their implications for whose perspectives are represented and whose are marginalised• cross-cultural interpretation of visual and data-driven content on social media, including how cultural background shapes audience readings of maps, infographics, and algorithmically produced imagery• the role of platform algorithms, data curation, content moderation, and interface design in determining which visual narratives reach which audiences• the data and design pathways through which false or misleading visual narratives are produced and amplified on social media• data-driven visual narratives about global challenges such as climate change, migration, and inequality on social media, and how the data and design choices behind them have the potential to distort or misrepresent complex realities.We particularly encourage Original Research, Systematic Reviews, and Methods articles that offer in-depth insight into the production, dissemination, perception, and interpretation of visual content in emerging technological settings.Information and Instructions for AuthorsManuscript summaries (Frontiers' name for abstracts) of up to 500 words will be considered until 30 September 2026. Authors are strongly encouraged to submit a manuscript summary before submitting a full manuscript. Topic editors: Francis Harvey, Zenonas Theodosiou Source: https://www.frontiersin.org/research-topics/78439/visual-communication-on-social-media-data-bias-and-ethical-visual-practice