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Register a new userComputational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics
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Added by
Xianlei Dong
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nations citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.
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Humans excel at detecting interesting patterns in images, for example those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowd-sourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earths radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish and Gravel. On cloud labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowd-sourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggests promising research questions. Further, this study illustrates that crowd-sourcing and deep learning complement each other well for the exploration of image datasets.
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos. To model spatial-temporal dependencies of human mobility, we propose a multi-focus Gaussian neighbor attention (GNA), which can effectively exploit long-range correspondences while maintaining the spatial topological structure of the input videos. In particular, our GNA can also capture the scale variation of human heads well using the equipped multi-focus mechanism. Based on the multi-focus GNA, we develop a unified neural network called GNANet to accurately locate head centers in video clips by fully aggregating spatial-temporal information via a scene modeling module and a context cross-attention module. Moreover, to facilitate future researches in this field, we introduce a large-scale crowded video benchmark named SenseCrowd, which consists of 60K+ frames captured in various surveillance scenarios and 2M+ head annotations. Finally, we conduct extensive experiments on three datasets including our SenseCrowd, and the experiment results show that the proposed method is capable to achieve state-of-the-art performance for both video crowd localization and counting. The code and the dataset will be released.
We describe Space Warps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowd-sourced visual inspection. Carefully produced colour composite images are displayed to volunteers via a web- based classification interface, which records their estimates of the positions of candidate lensed features. Images of simulated lenses, as well as real images which lack lenses, are inserted into the image stream at random intervals; this training set is used to give the volunteers instantaneous feedback on their performance, as well as to calibrate a model of the system that provides dynamical updates to the probability that a classified image contains a lens. Low probability systems are retired from the site periodically, concentrating the sample towards a set of lens candidates. Having divided 160 square degrees of Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging into some 430,000 overlapping 82 by 82 arcsecond tiles and displaying them on the site, we were joined by around 37,000 volunteers who contributed 11 million image classifications over the course of 8 months. This Stage 1 search reduced the sample to 3381 images containing candidates; these were then refined in Stage 2 to yield a sample that we expect to be over 90% complete and 30% pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the SpaceWarps system to the wide field survey era, based on our projection that searches of 10$^5$ images could be performed by a crowd of 10$^5$ volunteers in 6 days.
Searching for concepts in science and technology is often a difficult task. To facilitate concept search, different types of human-generated metadata have been created to define the content of scientific and technical disclosures. Classification schemes such as the International Patent Classification (IPC) and MEDLINEs MeSH are structured and controlled, but require trained experts and central management to restrict ambiguity (Mork, 2013). While unstructured tags of folksonomies can be processed to produce a degree of structure (Kalendar, 2010; Karampinas, 2012; Sarasua, 2012; Bragg, 2013) the freedom enjoyed by the crowd typically results in less precision (Stock 2007). Existing classification schemes suffer from inflexibility and ambiguity. Since humans understand language, inference, implication, abstraction and hence concepts better than computers, we propose to harness the collective wisdom of the crowd. To do so, we propose a novel classification scheme that is sufficiently intuitive for the crowd to use, yet powerful enough to facilitate search by analogy, and flexible enough to deal with ambiguity. The system will enhance existing classification information. Linking up with the semantic web and computer intelligence, a Citizen Science effort (Good, 2013) would support innovation by improving the quality of granted patents, reducing duplicitous research, and stimulating problem-oriented solution design. A prototype of our design is in preparation. A crowd-sourced fuzzy and faceted classification scheme will allow for better concept search and improved access to prior art in science and technology.
With increased frequency and intensity due to climate change, wildfires have become a growing global concern. This creates severe challenges for fire and emergency services as well as communities in the wildland-urban interface (WUI). To reduce wildfire risk and enhance the safety of WUI communities, improving our understanding of wildfire evacuation is a pressing need. To this end, this study proposes a new methodology to analyze human behavior during wildfires by leveraging a large-scale GPS dataset. This methodology includes a home-location inference algorithm and an evacuation-behavior inference algorithm, to systematically identify different groups of wildfire evacuees (i.e., self-evacuee, shadow evacuee, evacuee under warning, and ordered evacuee). We applied the methodology to the 2019 Kincade Fire in Sonoma County, CA. We found that among all groups of evacuees, self-evacuees and shadow evacuees accounted for more than half of the evacuees during the Kincade Fire. The results also show that inside of the evacuation warning/order zones, the total evacuation compliance rate was around 46% among all the categorized people. The findings of this study can be used by emergency managers and planners to better target public outreach campaigns, training protocols, and emergency communication strategies to prepare WUI households for future wildfire events.
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