The risk of stigmatization and racialization through images and videos

Learning objectives

In this module you will learn to identify manipulated, misleading, or fake visual content, trace its sources, and understand the role of visual framing in shaping public perception.

By the end of this module, learners will be able to:

• Understand how images and videos are used to disinform about migration
• Recognise visual strategies that stigmatise and racialise migrants
• Identify fake, manipulated, misleading, or out-of-context visual content
• Trace the origin and circulation of visual material
• Understand how AI tools can reproduce or amplify bias and stereotypes

Introduction

Images and videos play a central role in how migration is represented and understood in the public debate. More than written words, visual content is often perceived as direct evidence of reality. A photograph or a video clip can create an immediate impression of truth, even when it is misleading, incomplete, or strategically reframed.

Because of this perceived authenticity, visuals are among the most powerful tools used in disinformation. However, an image does not have to be fake to be deceptive; often, the way a real image is framed, through specific camera angles, tight cropping, or emotional triggers is what distorts the message.

This module explores how visual content is used to manipulate public perception and how "visual framing" contributes to the deeper processes of stigmatisation and racialisation. The urgent need to develop a more critical view of visual information will grant learners the ability to produce and share content that is accurate, responsible, and respectful of human dignity.

The psychology of the Image - why our brains skip the fact-check phase

Visual disinformation does not always rely on entirely fake content. Very often, it uses real images that are reused, mislabelled, or reframed to convey a distorted message. Images are especially effective because they don't just evoke emotions; they trigger confirmation bias.

When we see a high-arousal image, one that sparks fear or anger, our brains often skip the fact-check phase because the image validates our pre-existing worldviews. This "cognitive ease" makes visual disinformation feel like common sense.

In the context of migration, this is often exploited through visual framing. For example, a tight crop on a migrant's iPhone can be a status profiling, used to suggest a "fake refugee", while the invasive angle, a picture of migrants taken from a hidden perspective such as from a car, through a fence, or down from a balcony can suggest that they are in a clandestine meeting or are loitering threat.

Once these framed narratives are repeated, the human brain begins to process this distorted perspective as an absolute truth.

The sociology of the gaze and the power of framing - The relationship between the video/photographer, the subject, and the viewer

A key aspect of visual disinformation is the process of stigmatisation and racialisation, often achieved through the gaze. The way migrants are visually represented is rarely neutral.

Experts distinguish between the distanced gaze and eye-level agency. News footage often uses high angles, which reduce individuals to anonymous, faceless masses, what sociologists call "dehumanisation by perspective." Conversely, eye-level photography restores human agency and forces an interpersonal connection.

Recurrent patterns such as migrants protesting, queuing at the hospitals, banks, bus stops, etc. suggest they are taking up space or consuming resources that belong to locals.

Over time, these visual choices normalise biased representations. To counter this, it is essential to move beyond the image itself and practice reverse image search to verify the context, timeline, and framing across multiple reputable sources using tools like TinEye, Google Lens and many other such tools available for free.

The visual AI dilemma - the AI stereotype mirror and algorithmic stigmatisation

In recent years, artificial intelligence has added a new layer to visual manipulation. AI-generated images are not neutral windows into reality; they are mirrors of the data they were trained on. Because AI systems are trained on existing internet archives, they often reproduce and even exaggerate the dominant stereotypes and biases found there.

When prompts related to migration are used, AI systems may generate images that amplify status profiling or surveillance gaze, reinforcing the very narratives used in traditional disinformation. Understanding that AI is a "stereotype mirror" is essential for journalists and creators. Without critical awareness, AI tools risk amplifying harmful representations at an industrial scale, giving biased narratives a false coating of technological neutrality.

Sin una conciencia crítica, las herramientas de IA corren el riesgo de amplificar representaciones dañinas, otorgando a narrativas sesgadas una falsa apariencia de neutralidad tecnológica.

Real or Fake? Test your ability to distinguish between authentic photos and AI-generated content

In the era of AI, we must move from asking "Is this real" to "why was this generated?"

• The convenience trap: Was this AI image used because a real photo wasn't available, or because the AI image better suited a specific political narrative?
• The neutrality myth: Don't assume that because a computer made it, it is objective. A computer is a mirror of the humans who programmed it and the internet data it consumes.