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Beyond Engagement: Why It’s Time to Rethink Social Media’s Addictive Algorithms

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September 22, 2025
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Beyond Engagement: Why It’s Time to Rethink Social Media’s Addictive Algorithms
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As social media continues to weave itself into the fabric of daily life, the algorithms that drive engagement have come under fire for their potential to foster addictive behaviours.

With research linking these algorithms to increased anxiety and feelings of inadequacy, the question arises: should we regulate their use? The Liberal Democrats are now calling for cigarette-style warnings on social media apps.

This conversation is not only vital for user well-being but also presents an opportunity for businesses to adopt responsible marketing practices that prioritise mental health. Mariangela Caineri Zenati, Marketing Manager at social media management platform Loomly, offers her expert insight on how championing transparency and promoting positive content will allow brands to engage their audiences ethically while navigating the complexities of the digital landscape.

“The debate surrounding the legality of addictive algorithms in social media has gained significant traction in recent years, particularly in light of their profound implications for mental health and overall well-being. As social media platforms increasingly rely on sophisticated algorithms to maximise user engagement, the potential for addictive behaviours has come under scrutiny.

“Research highlights that these algorithms can create dependency-like behaviours, reminiscent of substance addiction. A recent study revealed that the instant gratification derived from likes, shares and comments can trigger dopamine release, reinforcing compulsive behaviours among users. This is particularly alarming for younger demographics, who are often more susceptible to these influences.

“The Royal Society for Public Health’s #StatusofMind report underscores this concern, identifying platforms such as Instagram and Snapchat as being linked to increased feelings of inadequacy, anxiety, and loneliness among young users. This report indicates that these platforms rank as the most detrimental for mental health, highlighting the urgent need for more responsible practices.

“The pervasive nature of these algorithms can contribute to rising rates of anxiety and depression among users. The #StatusofMind report also calls for social media companies to implement educational warnings and promote healthier online interactions: this raises important questions about the ethical responsibilities of businesses that utilise social media marketing strategies.

“As businesses increasingly turn to social media for marketing, they have a unique opportunity to approach these platforms responsibly. Companies can prioritise user well-being by promoting positive content, fostering supportive online communities and ensuring transparency in their advertising practices; for instance, brands can engage in campaigns that encourage mental health awareness and provide resources for users facing challenges. This way, brands can align themselves with ethical marketing practices while simultaneously building trust and loyalty among their audience.

“Responsible social media marketing involves understanding the impact of algorithms on user behaviour. Businesses should be mindful of how their content may influence users and strive to create a balanced digital experience; this could involve diversifying content types, avoiding sensationalism and steering clear of tactics that exploit users’ vulnerabilities for engagement.

“The potential for addiction necessitates a critical examination of the legal and ethical frameworks surrounding social media algorithms. Businesses must play a proactive role in promoting responsible marketing practices, which can help mitigate the negative effects of these algorithms while enhancing user experience. Addressing these issues is vital for creating a more positive online landscape, ultimately benefitting both users and brands alike.”

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Beyond Engagement: Why It’s Time to Rethink Social Media’s Addictive Algorithms

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