Analyzing public sentiments online: combining human- and computer-based content analysis

Recent technological developments have created novel opportunities for analyzing and identifying patterns in large volumes of digital content. However, many content analysis tools require researchers to choose between the validity of human-based coding and the ability to analyze large volumes of content through computer-based techniques. This study argues for the use of supervised content analysis tools that capitalize on the strengths of human- and computer-based coding for assessing opinion expression. We begin by outlining the key methodological issues surrounding content analysis as performed by human coders and existing computational algorithms. After reviewing the most popular analytic approaches, we introduce an alternative, hybrid method that is aimed at improving reliability, validity, and efficiency when analyzing social media content. To demonstrate the usefulness of this method, we track nuclear energy- and nanotechnology-related opinion expression on Twitter surrounding the Fukushima Daiichi accident to examine the extent to which the volume and tone of tweets shift in directions consistent with the expected external influence of the event. Our analysis revealed substantial shifts in both the volume and tone of nuclear power-related tweets that were consistent with our expectations following the disaster event. Conversely, there was decidedly more stability in the volume and tone of tweets for our comparison issue. These analyses provide an empirical demonstration of how the presented hybrid method can analyze defined communication sentiment and topics from large-scale social media data sets. The implications for communication scholars are discussed.     

Being of two minds: Ultimatum offers under cognitive constraints

We experimentally investigate how proposers in the Ultimatum Game behave when their cognitive resources are constrained by time pressure and cognitive load. In a dual-system perspective, when proposers are cognitively constrained and thus their deliberative capacity is reduced, their offers are more likely to be influenced by spontaneous affective reactions. We find that under time pressure proposers make higher offers. This increase appears not to be explained by more reliance on an equality heuristic. Analysing the behaviour of the same individual in both roles leads us to favour the strategic over the other-regarding explanation for the observed increase in offers. In contrast, proposers who are under cognitive load do not behave differently from proposers who are not.     

Minimum d-blockers and d-transversals in graphs

We consider a set V of elements and an optimization problem on V: the search for a maximum (or minimum) cardinality subset of V verifying a given property ℘. A d-transversal is a subset of V which intersects any optimum solution in at least d elements while a d-blocker is a subset of V whose removal deteriorates the value of an optimum solution by at least d. We present some general characteristics of these problems, we review some situations which have been studied (matchings, s–t paths and s–t cuts in graphs) and we study d-transversals and d-blockers of stable sets or vertex covers in bipartite and in split graphs.     

La firme locale-globale : Lyonnaise des Eaux (1980-2004)

This study of the policy of internationalization conducted by Lyonnaise des Eaux (renamed Suez) and, in particular, of its internal transformations during the period from 1980 to 2004 focuses on the stages in these changes, the organizational characteristics and the principles that were applied. One argument is to consider that this company has designed a new type of organization which combines a deep center and the operation of many different services in a large number of countries. It has thus become a « local global” firm. Different from existing types (H, U, M, J) of firms, this new form has four characteristics: the central role played by local units, procedures for reporting toward a compact center, assistance from the center, and direct exchanges between local units following a “principle of connectivity ”. Questions are raised about the capacity for change of this type of firm, which combines organizational dimension and institutional characteristics. Being in the market as well as in the production of the legal environment gives this firm some specific properties in the way to conduct a strategy. — Special issue: New patterns of institutions.     

Wavelet deconvolution in a periodic setting

Summary.  Deconvolution problems are naturally represented in the Fourier domain, whereas thresholding in wavelet bases is known to have broad adaptivity properties. We study a method which combines both fast Fourier and fast wavelet transforms and can recover a blurred function observed in white noise with O { n    log ( n )²} steps. In the periodic setting, the method applies to most deconvolution problems, including certain 'boxcar' kernels, which are important as a model of motion blur, but having poor Fourier characteristics. Asymptotic theory informs the choice of tuning parameters and yields adaptivity properties for the method over a wide class of measures of error and classes of function. The method is tested on simulated light detection and ranging data suggested by underwater remote sensing. Both visual and numerical results show an improvement over competing approaches. Finally, the theory behind our estimation paradigm gives a complete characterization of the 'maxiset' of the method: the set of functions where the method attains a near optimal rate of convergence for a variety of L ^p loss functions.     

Analysis of band‐recovery data in a multistate capture‐recapture framework

Dead recoveries of marked animals are commonly used to estimate survival probabilities. Band‐recovery models can be parameterized either by r (the probability of recovering a band conditional on death of the animal) or by f (the probability that an animal will be killed, retrieved, and have its band reported). The T parametrization can be implemented in a capture‐recapture framework with two states (alive and newly dead), mortality being the transition probability between the two states. The authors show here that the f parametrization can also be implemented in a multistate framework by imposing simple constraints on some parameters. They illustrate it using data on the mallard and the snow goose. However, they mention that because it does not entirely separate the individual survival and encounter processes, the f parametrization must be used with care on reduced models, or in the presence of estimates at the boundary of the parameter space. As they show, a multistate framework allows the use of powerful software for model fitting or testing the goodness‐of‐fit of models; it also affords the implementation of complex models such as those based on mixture of information or uncertain states     

Prediction of Chaotic Time Series in the Presence of Measurement Error: the Importance of Initial Conditions

In this paper we argue that even if a dynamic relationship can be well described by a deterministic system, retrieving this relationship from an empirical time series has to take into account some, although possibly very small measurement error in the observations. Therefore, measuring the initial conditions for prediction may become much more difficult since one now has a combination of deterministic and stochastic elements. We introduce a partial smoothing estimator for estimating the unobserved initial conditions. We will show that this estimator allows to reduce the effects of measurement error for predictions although the reduction may be small in the presence of strong chaotic dynamics. This will be illustrated using the logistic map.