Conciliação de metas em buscas orgânicas no Google: análise das interações usuário-sistema

• Autor(a) principal: Caldeira, Fátima Hassan
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Universitário da Ânima (RUNA)
• Texto Completo: https://repositorio.animaeducacao.com.br/handle/ANIMA/3316
• topic: Pragmática cognitiva; Teoria de conciliação de metas; Teoria da relevância; Interação usuário-sistema; Pragmática; Relevância; Google; Interação homem-máquina; Lingüística

In this thesis, we verified if the concept of hetero-conciliation goals is applicable to usersystem interactions in organic searches on Google. For this purpose, we qualitatively explore a case of consultation on the location of a restaurant from a pragmatic-cognitive bias guided by goals conciliation notions (Rauen, 2013, 2014) and relevance (Sperber, Wilson 1986, 1995). The results suggest that user-Google interaction should be regarded as a collaborative activity in which both participants conciliate goals. If the notion of relevance sought by the user in the system, in terms of Yus (2012a), describes and explains how the user heteroconciliates the search results with their communicational, informational and practical goals; .the notion of relevance sought by the system to the user describes and explains how the search engine hetero-conciliates its results with the goals of users. Organic searches of this kind can be modeled in the context of an intentional action plan in which the user formulates a query (communicative intention) to obtain certain information (informative intention) motivated by achieving a practical goal.In this process, the user monitors the search results with his goals and proceeds to make corrective actions according to his preferences and abilities. Triggered by the query, the system provides a set of responses that aims to meet the informational goal and, hypothetically, the practical goal of user. In this process, Google identifies the user and monitors his browsing to collect personalization signals and to individualize their his results; it provides tools that help the user to formulate the query; it shows alternative results, leaving the user free to meet their his search intent; it anticipates possible queries, increasing cognitive effects and decreasing processing efforts of the user; and as the session lasts, it adjusts its results. All these features suggest that the system response to the user is simulating, each time with greater efficiency, the interaction between humans. If, in the current technological stage, it is still not the case that the system may emulate the plasticity as humans map contexts and provide more accurate answers with a relative degree of accuracy, it is possible to check that, by providing a massive range of response options, the system is obtaining success in scale, since, by hypothesis, some of these alternatives help the user to conciliate the goal in question.