Detecting interethnic relations with the data from social media
The ability of social media to rapidly disseminate judgements on ethnicity and to influence offline ethnic relations creates demand for the methods of automatic monitoring of ethnicity-related online content. In this study we seek to measure the overall volume of ethnicity-related discussion in the Russian-language social media and to develop an approach that would automatically detect various aspects of attitudes to those ethnic groups. We develop a comprehensive list of ethnonyms and related bigrams that embrace 97 Post-Soviet ethnic groups and obtain all messages containing one of those words from a two-year period from all Russian-language social media (N=2,660,222 texts). We hand-code 7,181 messages where rare ethnicities are over-represented and train a number of classifiers to recognize different aspects of authors’ attitudes and other text features. After calculating a number of standard quality metrics, we find that we reach good quality in detecting intergroup conflict, positive intergroup contact, and overall negative and positive sentiment. Relevance to the topic of ethnicity and general attitude to an ethnic group are least well predicted, while some aspects such as calls for violence against an ethnic group are not sufficiently present in the data to be predicted.
The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object involves here the apparatus of Formal Concept Analysis. We explain the principle of the algorithm on a toy example and describe experiments with real-world datasets.
The social and community driven aspects of our digital lives continue to rapidly increase, resulting in transformative behaviors and, significantly, publishing and distributing huge amounts of fascinating data. The seventh meeting of the International AAAI Conference on Weblogs and Social Media (ICWSM-13) held in Cambridge, Massachusetts, promised to be a benchmark year for ICWSM. Thanks to the enthusiastic participation of our community, we received a record number of submissions, with a growth of 50 percent over the previous year. More than the quantity, however, the high quality of the submitted papers is the truest evidence that ICWSM is maturing in its role as a premier venue for social media research.
A unified methodology for categorizing various complex objects is presented in this book. Through probability theory, novel asymptotically minimax criteria suitable for practical applications in imaging and data analysis are examined including the special cases such as the Jensen-Shannon divergence and the probabilistic neural network. An optimal approximate nearest neighbor search algorithm, which allows faster classification of databases is featured. Rough set theory, sequential analysis and granular computing are used to improve performance of the hierarchical classifiers. Practical examples in face identification (including deep neural networks), isolated commands recognition in voice control system and classification of visemes captured by the Kinect depth camera are included. This approach creates fast and accurate search procedures by using exact probability densities of applied dissimilarity measures.
This book can be used as a guide for independent study and as supplementary material for a technically oriented graduate course in intelligent systems and data mining. Students and researchers interested in the theoretical and practical aspects of intelligent classification systems will find answers to:
- Why conventional implementation of the naive Bayesian approach does not work well in image classification?
- How to deal with insufficient performance of hierarchical classification systems?
- Is it possible to prevent an exhaustive search of the nearest neighbor in a database?
Compared with the area of spatial relations force interactions haven’t been in the limelight of attention of ontologists working on natural language processing. This article gives an example of text meaning representation based on the ontology and the lexicon of force interactions.
In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.
We propose extensions of the classical JSM-method andtheNa ̈ıveBayesianclassifierforthecaseoftriadicrelational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification precision and recall we also evaluated the time performance of the proposed methods.
This article touches upon some problems in building up a lexicon for the part of universal ontology which accounts for force interactions. We have chosen certain semantic features in the lexical description as dominant ones and conducted a small survey among native speakers of Russian to prove the results.
In 2006, Russia amended its competition law and added the concepts of ‘collective dominance’ and its abuse. This was seen as an attempt to address the common problem of ‘conscious parallelism’ among firms in concentrated industries. Critics feared that the enforcement of this provision would become tantamount to government regulation of prices. In this paper we examine the enforcement experience to date, looking especially closely at sanctions imposed on firms in the oil industry. Some difficulties and complications experienced in enforcement are analysed, and some alternative strategies for addressing anticompetitive behaviour in concentrated industries discussed.
This article is talking about state management and cultural policy, their nature and content in term of the new tendency - development of postindustrial society. It mentioned here, that at the moment cultural policy is the base of regional political activity and that regions can get strong competitive advantage if they are able to implement cultural policy successfully. All these trends can produce elements of new economic development.