Sports spectatorship is fundamentally affective, and the rise of fan-generated data has enabled large-scale computational analysis of fan sentiment and emotion. This paper synthesises 64 studies (2000–2025) that infer fan affect from (i) digital text and online discourse, (ii) broadcast-linked second-screen interaction, and (iii) in-venue or non-textual signals such as crowd audio, video, and wearable sensing. We organise the literature into three modality-based categories: text-centric discourse analytics, second-screen/social-TV behaviour, and in-venue or multimodal sensing. Across studies, a consistent empirical pattern is event-driven synchrony: aggregate affective signals shift rapidly around salient match events and controversy. However, three structural limitations constrain behavioural inference and generalisability: strong platform dependence (especially Twitter/X), overreliance on coarse polarity sentiment, and conceptual slippage between affective expression and behaviour. Research on harmful expressions (e.g., toxicity, hate speech) is expanding and behaviourally relevant, but introduces methodological and ethical challenges. Overall, the literature is effective at detecting affective expression but weaker in linking affect to explicit behavioural outcomes or integrating evidence across modalities. We highlight directions for behaviour-centred, multimodal fan analytics, including improved construct validity, clearer outcome definitions, cross-modality integration, and stronger cross-platform and ethical considerations.
Ziaee et al. (Mon,) studied this question.