The 'For You' Body-Image Survey
After a pastoral lesson on body image, the school council surveys 120 students aged 14-16. Results: 68% say they 'often compare their bodies' with people on TikTok; 22% say they have 'seriously considered' a cosmetic procedure; 41% say TikTok is 'just entertainment'. Several students say that after watching one cosmetic-clinic video, their For You page showed more clinic ads and 'before/after' clips. The survey was a self-selected Instagram form; 120 of 600 students responded. A parent demands the school block TikTok on all devices, saying it is 'causing eating disorders'. A teacher says the survey 'proves the algorithm is harming children'. The school has no access to students' platform data and cannot see their For You feeds.
1. Explain why the survey and the anecdotal reports cannot by themselves prove that TikTok's algorithm caused body dissatisfaction.
Read the worked answer
The survey is cross-sectional, self-selected and correlational. It cannot show causation: there is no pre/post measure, no control group, and no measure of prior body dissatisfaction or time spent on TikTok. Students who already feel dissatisfied may watch more cosmetic content, and the algorithm may amplify that existing interest rather than create it. The 120 out of 600 self-selected Instagram sample is skewed toward students who use social media and care about the topic. The anecdotal 'after one video' reports are plausible but subject to recall and confirmation bias; students cannot see the full ranking logic. The teacher's 'proves the algorithm is harming children' overstates the data. Algorithmic personalisation makes causality harder because each feed is different: there is no single 'TikTok exposure' to measure. The honest conclusion is that the survey identifies a pattern worth investigating, not a proven causal effect.
2. Apply two audience-reception ideas to interpret the 41% and 68% findings, and explain how algorithmic personalisation complicates those ideas.
Read the worked answer
Uses and gratifications would read the 41% 'just entertainment' as a claim to diversion, but the phrase may also be a protective account; it does not prove immunity. Social comparison theory fits the 68% comparison figure: viewers evaluate themselves against curated bodies, though the effect depends on prior self-esteem, peer talk and whether the bodies look achievable. Encoding/decoding adds that producers encode transformation, while viewers may accept, negotiate or oppose it. Algorithmic personalisation complicates all three. If the feed is tailored, the 'text' is not shared: one student's repeated clinic content is not another's. Uses and gratifications becomes harder because the platform shapes what is available to gratify. Social comparison is intensified by repetition and by 'before/after' formats that look like evidence. Decoding is constrained by the absence of context: users may not know why a clinic video appeared. The audience is active, but inside a designed environment.
3. Recommend a school response that addresses both critical data literacy and student wellbeing.
Read the worked answer
A school response should combine critical data literacy with wellbeing, not a blanket ban. In lessons, students can audit their own For You page without sharing private data: what signals does the app collect (watch time, likes, skips), what does a clinic ad look like, why does 'before/after' appear credible, and how can they use 'not interested' and ad-reporting tools? They can compare the platform's transparency report with their experience. In pastoral care, provide a confidential route to counselling and a body-neutral policy; avoid weighing or public body talk. For parents, explain that a ban on school devices does not change home feeds and may hide the issue. If the school restricts TikTok on Wi-Fi, it should say what is taught instead. The aim is to build critical autonomy and care, while acknowledging that individual resilience cannot fix a commercial design problem.