Autocomplete feature may appear trivial, but on platforms like TikTok, where millions of users turn to search as a gateway to information, it plays a significant role in shaping what people ask and how they think. As the platform becomes increasingly central to digital culture and identity formation, the predictive suggestions it offers can reflect, reinforce, or challenge social biases. This thesis examines TikTok’s autocomplete system through the lens of toxic language, analysing over 225,000 suggestions generated from more than 3,000 identity-related prompts across ten countries. The aim is to understand whether the system amplifies toxic stereotypes around gender, race, and sexual orientation, and whether these patterns vary by geography or ranking.
To approach this, the study conceptualises algorithmic bias as a form of representational distortion, when identities are disproportionately linked to hostile, stereotypical, or derogatory language. Toxicity is used as a measurable proxy for this phenomenon, with scores derived from Google’s Perspective API and validated against a human-annotated subset. This framework allows the study to scale while maintaining conceptual clarity, offering a bridge between statistical insight and social meaning. The use of a cross-national dataset further adds depth, enabling the analysis to explore both universal and context-specific patterns of bias.
The findings show clear evidence that identity matters. Prompts referencing homosexual identities and Black identity terms consistently produced higher toxicity scores than their heterosexual and White counterparts. Gender-based disparities were also observed, though they were less pronounced. Interestingly, these patterns held steady across all ten countries studied. Despite cultural, regulatory, and linguistic differences, the toxicity distributions remained largely similar, suggesting that TikTok’s autocomplete system likely runs on a globally standardised model that does not meaningfully adapt to regional contexts. In contrast, the ranking of suggestions, where a toxic output appears within the top eight, had only a marginal impact on overall exposure. While some toxic completions did surface in mid-list positions, their distribution lacked a clear or consistent pattern.
Taken together, the research offers an empirical audit of TikTok’s search interface from a bias and fairness perspective. It shows that autocomplete, though often overlooked, can act as a subtle mechanism through which social hierarchies are reproduced. This insight carries implications for platform accountability and algorithmic design, especially in the context of ongoing policy efforts such as the EU AI Act, an emerging regulatory landscape that is likely to include systems like TikTok's autocomplete. By demonstrating that toxic associations are not isolated glitches but predictable patterns, the study highlights the limitations of moderation strategies that focus solely on removals or post hoc filtering. Ultimately, the thesis argues that mitigating algorithmic bias requires more than adjusting output rankings, it demands deeper attention to how predictive models are trained, evaluated, and governed. As TikTok continues to shape how a new generation accesses information, improving the social impact of features like autocomplete is not only a technical challenge but a public responsibility.