Toxic algorithms wouldn't let them go - now Australia wants an off switch

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   Toxic algorithms wouldn't let them go - now Australia wants an off switch

   BBC NewsPublished Oct 16, 2026Checked Oct 20, 2026

   “Watch: Many social platforms already have following-only feeds - here's how they work Warning: This article contains an account of living with an eating disorder Varsha Yajman was already self-conscious about the way she looked when she joined social media as a 13-year-old. Endless workout videos making impossible promises filled her feeds. Through the screen, girls she'd never met urged her to sign up to dangerous calorie challenges. Others proudly promoted their height and weight - even the number of times they had been sent to hospital - on their profiles in a form of twisted competition. Every night as she fell asleep, Varsha would watch a stranger on the internet diarising what they'd consumed that day. "I was like, 'I don't need to eat. I can just watch other people eat'," Varsha tells the BBC. "I would find that I'd be down these really deep rabbit holes... it was like no matter how hard you tried to get out, you couldn't." Now 24 and in recovery from anorexia, Varsha says she is still served weight loss content on social media - despite having every safety setting turned on, and an exhaustive list of banned words. Varsha Yajman says social media aggravated her eating disorder It is people like her the Australian government says it is trying to protect with a new suite of laws cracking down on big tech, which were introduced to parliament this week.”

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   Recommender system (Wikipedia)

   WikipediaChecked Oct 20, 2026

   “A recommender system, also called a recommendation engine or content discovery platform is a type of information filtering system that aims to suggest items most relevant for some input or to a particular user. In the context of social media, search engines, and other online services, a recommender system for a given service might sometimes informally but erroneously be referred to as the service's "algorithm". The use of recommender systems is pervasive, with commonly recognised examples including the generation of playlists for video and music streaming services, product recommendations for e-commerce platforms, and topics and individual items posted on social media platforms and the open web. Online services employ machine learning or specialized deep learning recommendation models (DLRMs) that analyze user behavior and preferences to generate personalized content feeds for millions of users at any moment. The value of these systems becomes particularly evident in scenarios where users must select from a large number of options, such as products, media, or content.”

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   TikTok and Eating Disorders: A Narrative Review.

   The International journal of eating disorders (Griffiths et al.)Published May 2, 2026Checked Oct 20, 2026

   “Algorithm-focused studies indicate that recommendation systems can disproportionately deliver such material to vulnerable users and may be more responsive to passive engagement signals (e.g., watch time) than to explicit actions (e.g., "liking"). Across study designs, greater exposure to and engagement with eating disorder-related content appears to be associated with eating disorder symptoms and related phenomena. Personalized feeds may function as treatment barriers that warrant assessment in clinical settings, and psychoeducation about how recommendation systems operate may help clients recognize and disrupt harmful exposure cycles.DiscussionTikTok's algorithmically structured environment may interact with vulnerability to reinforce maladaptive comparison and behavioral cycles. Further longitudinal and clinically focused research is needed to clarify causal pathways and inform evidence-based guidance for clinicians.”

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   Algorithmic recommendation and adolescent mental health: a grounded theory study of social and commercial determinants.

   BMC public health (Winstone et al.)Published Jun 24, 2026Checked Oct 20, 2026

   “Algorithms organised what content participants encountered, how frequently, and with what emotional intensity, within broader socioecological contexts. Three interlocking processes sustained exposure; algorithmic reinforcement of engagement, emotional feedback loops between mood and recommendations, and a persistent sense of limited control despite high awareness. Although participants developed adaptive strategies and algorithmic literacy, these were unevenly distributed and often insufficient within engagement-driven, profit-oriented systems.ConclusionsDigital mental health may be produced through interactions between individual capacities, social environments, and commercially driven platform design. Protecting population mental health therefore requires multi-level action, including developmentally appropriate social media and algorithmic literacy education alongside structural reforms to recommendation systems, transparency, and platform governance.”

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   Navigating Social Media: Balancing Connectivity With Media Literacy to Combat Misinformation and Protect Mental Well-Being.

   Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals (AlPublished Oct 1, 2026Checked Oct 20, 2026

   “The findings indicate that conventional media literacy approaches focused solely on fact-checking are insufficient. Instead, a critical digital literacy framework encompassing algorithmic awareness, data literacy, and emotional awareness is essential for building user resilience, with evidence from high-quality systematic reviews supporting this approach.ConclusionsNavigating the complexities of modern social media requires an integrated approach combining "pedagogies of play" for experiential skill development with advocacy for structural change (e.g., algorithmic transparency, well being by design principles). This dual strategy empowers individual users to critically engage with digital content while advocating for ethical platform design, thereby safeguarding both mental well-being and democratic discourse. Implications for educators, mental health professionals (including competencies for addressing patient encounters with psychiatric misinformation), policymakers, and platform designers are discussed.”