Towards a Model of Understanding Social Search

Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process even implicitly shared information may be valuable to individual searchers.Comment: Presented at 1st Intl Workshop on Collaborative Information Seeking, 2008 (arXiv:0908.0583

V.I. Vernadsky and the noosphere concept: Russian understandings of society-nature interaction

Recent Russian legislative and policy documentation concerning national progress towards sustainable development has suggested that the attainment of such a state would represent the first stage in the development of the noosphere as outlined by the Russian scientist Vladimir Ivanovich Vernadsky (1863–1945). This paper explores Vernadsky’s model of evolutionary change through a focus on his work on the biosphere and noosphere in an attempt to further understanding of the way in which Russia is approaching the concept of sustainable development in the contemporary period. It is argued that the official Russian interpretation of the noosphere idea tends to obscure the evolutionary and materialist foundations of Vernadsky’s biosphere–noosphere conceptualisation. At the same time, the concluding section of the paper suggests that the scope of Vernadsky’s work can be used to stimulate the search for a more coherent approach to work in areas of sustainable development and sustainability across the span of the social and physical sciences

Opinion Formation and the Collective Dynamics of Risk Perception

The formation of collective opinion is a complex phenomenon that results from the combined effects of mass media exposure and social influence between individuals. The present work introduces a model of opinion formation specifically designed to address risk judgments, such as attitudes towards climate change, terrorist threats, or children vaccination. The model assumes that people collect risk information from the media environment and exchange them locally with other individuals. Even though individuals are initially exposed to the same sample of information, the model predicts the emergence of opinion polarization and clustering. In particular, numerical simulations highlight two crucial factors that determine the collective outcome: the propensity of individuals to search for independent information, and the strength of social influence. This work provides a quantitative framework to anticipate and manage how the public responds to a given risk, and could help understanding the systemic amplification of fears and worries, or the underestimation of real dangers

Managers' brokerage for business model innovation : A case study

Business model innovation is recognized as a key process for strengthening firms' performance in situations of strong competitive pressure and environmental changes. This process is driven by intra-organizational advice networks between managers, which exchange different types of advice based on organizational learning mechanisms such as cognitive search (how to conceptualize and create a novel business model) and experiential learning (how to adapt and experiment a novel business model. Investigating what are the key figures emerging from such network is essential for an in-depth understanding of the business model innovation process. By focusing on a multi-unit firm operating in the personal care service industry, we use Social Network Analysis (SNA) to examine the brokerage role of managers when sharing different types of advice towards a novel business model. Our results show that middle-level managers connect different managerial groups in different networks; however, differences exist between groups of middle managers, confirming their peculiar nature within organizations.Peer reviewe

Crime in Philadelphia: Bayesian Clustering with Particle Optimization

Accurate estimation of the change in crime over time is a critical first step towards better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled "sharing of information" between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly-smooth parameter estimates, which can ultimately produce miscalibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. Supplementary materials for this article are available online

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017