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
Given current legislative mandates to assess the safety of thousands of chemicals and the slow pace at which conventional testing proceeds, there is a need to accelerate chemical risk assessment. Governments and businesses are increasingly interested in new approach methodologies (NAMs) that promise to reduce costs and delays. We explore 5 sociological factors within the ecotoxicology community that can influence the perception of NAMs: 1) professional profile (educational cohort, employer), 2) internal science communication within professional forums, 3) concern for "error cost," 4) collaboration across stakeholders, and 5) fundamental beliefs regarding toxicology. We conducted an online survey (n = 171; 2018) asking participants about their experiences and perspectives at events of the Society of Environmental Toxicology and Chemistry (SETAC) to assess 1) how NAMs are discussed compared to conventional testing and 2) how respondents perceive their viability. We developed ordered logistic regression (OLR) models to understand the influence of exploratory variables (cohort, core views on toxicology, frequency of collaboration) on respondents' evaluation of the viability of different NAMs. Our results showed that 1) NAMs were more likely than conventional methods to be challenged in forum discussions, which may be fueled by concerns for error costs in regulatory decision making; 2) perceptions of the viability of NAMs tended to follow a "pattern of familiarity," whereby respondents that were more knowledgeable about a test method tended to find it more viable; 3) respondents who agreed with the Paracelsus maxim had a greater likelihood of finding conventional testing viable; and 4) the more a respondent reported collaborating with industry on alternative testing strategies, the more likely she or he was to report that NAMs were less viable. These results suggest that there are professional and organizational barriers to greater acceptance of NAMs that can be addressed through a social learning process within the professional community. Integr Environ Assess Manag 2020;16:269-281. © 2020 SETAC.