scholarly journals The dark and light sides of engagement: an analysis of user-generated content in wildlife trade online communities.

Author(s):  
Kim Feddema ◽  
Paul Harrigan ◽  
Shasha Wang

Recent research has focused on the role of user-generated content (UGC) in the dark side of engagement on social media. In this study, we apply this to the unique context of the online exotic wildlife trade, a critical area of research due its involvement in devastating global species loss as well as harms to human health and livelihoods. We first conduct qualitative analysis on a large data set of UGC with the automatic machine-learning lexical software Leximancer 4.5.1 to explore the discourse that occurs in comments of posts that promote behaviour change and demand reduction. Then, we complement this by testing an extended elaboration likelihood model to determine the nature of information processing that leads to positive comment valences. Our results show that motivation, opportunity and ability factors moderate the relationship between dual-processing routes and comment valence as well as influencing the likelihood of positive comment valences that indicate attitude change. This work extends the use of theory from Information Systems and Marketing to conservation and provides both conceptual and practical recommendations to encourage behaviour change and reduce the harmful effects of engagement.

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Kai Jiang ◽  
Like Liu ◽  
Rong Xiao ◽  
Nenghai Yu

Recently, many local review websites such as Yelp are emerging, which have greatly facilitated people's daily life such as cuisine hunting. However they failed to meet travelers' demands because travelers are more concerned about a city's local specialties instead of the city's high ranked restaurants. To solve this problem, this paper presents a local specialty mining algorithm, which utilizes both the structured data from local review websites and the unstructured user-generated content (UGC) from community Q&A websites, and travelogues. The proposed algorithm extracts dish names from local review data to build a document for each city, and appliestfidfweighting algorithm on these documents to rank dishes. Dish-city correlations are calculated from unstructured UGC, and combined with thetfidfranking score to discover local specialties. Finally, duplicates in the local specialty mining results are merged. A recommendation service is built to present local specialties to travelers, along with specialties' associated restaurants, Q&A threads, and travelogues. Experiments on a large data set show that the proposed algorithm can achieve a good performance, and compared to using local review data alone, leveraging unstructured UGC can boost the mining performance a lot, especially in large cities.


2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.


2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.


Author(s):  
Lior Shamir

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .


Genetics ◽  
1997 ◽  
Vol 146 (3) ◽  
pp. 995-1010 ◽  
Author(s):  
Rafael Zardoya ◽  
Axel Meyer

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.


2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson

Author(s):  
Johan Lundberg

AbstractTheories of inter-jurisdictional tax and yardstick competition assume that the tax decisions of one jurisdiction will influence the tax decisions of other jurisdictions. This paper empirically addresses the issue of horizontal dependence in local personal income tax rates across jurisdictions. Based on a large data set covering Swedish municipalities over a period of 14 years, we test for interactions across municipalities that share a common border, across municipalities within a distance of 100 km of each other, and across municipalities with similar political representation in the local council. We also test the hypothesis that the tax rate of relatively larger municipalities has a greater influence on their neighbors' tax rate compared to the influence of their smaller neighbors. Our results suggest that when lagged tax rates are controlled for, the horizontal correlation across municipalities that share a common border or are within a distance of 100 km from each other becomes insignificant. This result is of importance as it suggests that lagged tax rates should be included or at least tested for when testing for horizontal interactions or mimicking in local tax rates. However, our results support the hypothesis of horizontal interactions across municipalities that share a common border when the influence of neighboring municipalities is also weighted by their relative population size, i.e. relatively larger neighbors tend to have a greater impact on their neighbor's tax rates than their relatively smaller neighbors. This is of importance as it suggests that distance or proximity matters, although only in combination with the relative population size. We also find some evidence of horizontal dependence across municipalities with similar political preferences.


2021 ◽  
Vol 15 ◽  
pp. 174830262199962
Author(s):  
Patrick O Kano ◽  
Moysey Brio ◽  
Jacob Bailey

The Weeks method for the numerical inversion of the Laplace transform utilizes a Möbius transformation which is parameterized by two real quantities, σ and b. Proper selection of these parameters depends highly on the Laplace space function F( s) and is generally a nontrivial task. In this paper, a convolutional neural network is trained to determine optimal values for these parameters for the specific case of the matrix exponential. The matrix exponential eA is estimated by numerically inverting the corresponding resolvent matrix [Formula: see text] via the Weeks method at [Formula: see text] pairs provided by the network. For illustration, classes of square real matrices of size three to six are studied. For these small matrices, the Cayley-Hamilton theorem and rational approximations can be utilized to obtain values to compare with the results from the network derived estimates. The network learned by minimizing the error of the matrix exponentials from the Weeks method over a large data set spanning [Formula: see text] pairs. Network training using the Jacobi identity as a metric was found to yield a self-contained approach that does not require a truth matrix exponential for comparison.


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