RESUMO
Among many by-products of Web 2.0 come the wide range of potential image and text datasets within social media and content sharing platforms that speak of how people live, what they do, and what they care about. These datasets are imperfect and biased in many ways, but those flaws make them complementary to data derived from conventional social science methods and thus potentially useful for triangulation in complex decision-making contexts. Yet the online environment is highly mutable, and so the datasets are less reliable than censuses or other standard data types leveraged in social impact assessment. Over the past decade, we have innovated numerous methods for deploying Instagram datasets in investigating management or development alternatives. This article synthesizes work from three Canadian decision contexts - hydroelectric dam construction or removal; dyke realignment or wetland restoration; and integrating renewable energy into vineyard landscapes - to illustrate some of the methods we have applied to social impact assessment questions using Instagram that may be transferrable to other social media platforms and contexts: thematic (manual coding, machine vision, natural language processing/sentiment analysis, statistical analysis), spatial (hotspot mapping, cultural ecosystem modeling), and visual (word clouds, saliency mapping, collage). We conclude with a set of cautions and next steps for the domain.
Parmi les nombreux sous-produits du Web 2.0 figure un large éventail de données provenant d'images et de textes, de contenus de médias sociaux et de plateformes numériques, qui révèlent comment les gens vivent, ce qu'ils font et les questions qui les préoccupent. Ces ensembles de données sont imparfaits et biaisés à bien des égards, mais nombre de leurs lacunes les rendent complémentaires des informations collectées par les sciences sociales à l'aide de méthodes conventionnelles. D'où leur utilité potentielle pour la triangulation dans des contextes décisionnels complexes. Cet article synthétise le travail de trois études de cas menées au Canada pour illustrer certaines des méthodes que nous avons développées et qui pourraient être utiles à d'autres chercheurs en EIS: thématiques (codage, apprentissage automatique, analyse sémantique, association statistique), spatiales (cartographie des points chauds, modélisation du transfert des bénéfices) et visuelles (cartes de saillance, collage).
Entre los muchos subproductos de la Web 2.0 se encuentra una amplia gama de datos de imágenes y texto, contenidos en redes sociales y plataformas digitales, que hablan de cómo vive, qué hace y por qué cuestiones se preocupa la gente. Estos conjuntos de datos son imperfectos y sesgados en muchos sentidos, pero muchos de sus defectos los hacen complementarios a la información recogida por las ciencias sociales con métodos convencionales. De ahí su potencial utilidad para la triangulación en contextos complejos de toma de decisiones. Este artículo sintetiza el trabajo de tres estudios de caso llevados a cabo en Canadá para ilustrar algunos de los métodos que hemos desarrollado y pueden resultar útiles para otros investigadores en EIS: temáticos (codificación, machine learning, análisis semántico, asociación estadística), espaciales (mapeo de puntos críticos, modelización de transferencia de beneficios) y visuales (mapas de saliencia, collage).
RESUMO
Previous studies on consumer perceptions and behaviors of salmon have often neglected Indigenous rights within the Canadian salmon sector. This study innovatively addresses this gap by integrating Indigenous rights into the current analysis, alongside considerations of sustainability practices, socio-economic impacts, and consumer motivations. Our research objectives aim to fit three consumer perceptions-environmental sustainability, economic considerations, and Indigenous rights-and to evaluate their associations, alongside perception of a price increase, socio-demographics, and consumer motivation factors, with purchasing behaviors related to Canadian salmon products. Data for this study was collected from a nationwide online survey. Responses to Question 2 and Question 35 are encoded with numerical values ranging from 1 to 5, where larger numbers indicate stronger agreement with the statement. The inclusion of methodologies such as the Graded Response Model (GRM) and Cumulative Link Models (CLM) adds another innovative dimension to this study. Our findings demonstrate how consumer profiles are associated with these four perceptions and their underlying determinants. Furthermore, the study quantifies the influence of these four perceptions on each consumer purchase behavior. The implications of these findings extend to the realm of mathematical modeling in consumer decision-making processes, offering practical insights for businesses and marketers, and emphasizing the importance of implementing regulatory frameworks and initiatives that promote sustainability, safeguard Indigenous rights, and address socio-economic disparities.
