Social representations and multi-channel communication on crucial societal issues through the modelling approach

This perspective article addresses six key themes, starting from the need to overcome the unilinear model of communication, by recognising the circular dynamics between social representations and multi-channel communication. In particular, the ‘modelling approach’ to social representations (SR), beyond the purely cumulative logic of traditional multi-methods, asserts as essential the need to articulate various theories, multiple constructs, different methods and techniques, ad hoc designed, based on oral, textual, iconic, behavioural communicative multi-channels, recurring to qualitative-quantitative data strategy analyses, justified on the basis of selected theories/constructs and driven by specific hypotheses about their role in shaping research outcomes. Multi-years research background—documented by rich bibliography—includes diversified societal topics: madness and mental illness; the relationship between myth, science and SR; traumatic collective events, media communication and social memory; SR, and multi-dimensional identities; controversial societal issues and polemical SR and/of commercial fashion advertising; SR of beauty and aesthetic surgery in three EU countries, Brazil and China; SR and economically relevant issues in national-supranational institutional contexts; social movements and active minorities on environmental and economic polemical SR objects in local–global scenarios; polarised S.R. of (im)migrants through field and multimedia studies; SR of/ within Covid-19 pandemic; evolving communicative ecosystem and SR in the social networks and artificial intelligence era. This article illustrates the model of circular dynamics between social representations and multi-channel communication by multi-agents in the tourism sector. This crucial economic-cultural sector deserves further longitudinal investigations in the pre/post-pandemic world with persistent global challenges including (im/e)migration, environmental crises, peace-wars, machine learning applications.