Article
Agenda Divergence in A Developing Conflict: A Quantitative Evidence from A Ukrainian and A Russian TV Newsfeeds
Although conflict representation in media has been widely studied, few attempts have been made to perform large-scale comparisons of agendas in the media of conflicting parties, especially for armed country-level confrontations. In this paper, we introduce quantitative evidence of agenda divergence between the media of conflicting parties in the course of the Ukrainian crisis 2013–2014. Using 45,000 messages from the online newsfeeds of a Russian and a Ukrainian TV channels, we perform topic modeling coupled with qualitative analysis to reveal crisis-related topics, assess their salience and map evolution of attention of both channels to each of those topics. We find that the two channels produce fundamentally different agenda sequences. Based on the Ukrainian case, we offer a typology of conflict media coverage stages.
In this paper, we empirically test the dependence of the Russian stock market on the world stock market, world oil prices and Russian political and economic news during the period 2001–2010. We find that oil prices are not significant after 2006, and the Japan stock index is significant over the whole period, since it is the nearest market index in terms of closing time to the Russian stock index. We find that political news like the Yukos arrests or news on the Georgian war have a short-term impact, since there are many other shocks. These factors confirm the structural instability of the Russian financial market.
The goal of the conference is to help build cross-disciplinary networks of analysts, software specialists, and researchers to advance the use of textual information in multiple science, technology, and business development fields. Within this context, conference themes will include, but are not limited to:
Data
Sourcing, preparing, and interpreting data sources including patents, publications, webscraping, and other novel data sourcesText-mining tools and methods
Best practices in software-based topic modeling, clumping, association rules, term manipulation, text manipulation, etc. VisualizationApplied research
Future-Oriented Technology Analysis (FTA) Intelligence gathering to support decision-making in the private sector (e.g., Management of Technology)An important text mining problem is to find, in a large collection of texts, documents related to specific topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to find the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predefined sets of keywords (that define the topics researchers are interested in) are restricted to specific intervals of topic assignments.
The purpose of this research is to describe the agenda set by the Internet-active part of the Russian public in Russia’s leading blog platform LiveJournal. This is done through modelling the Livejournal’s topic structure viewed as a reflection of online public opinion. Topic modelling is performed automatically with a LDA algorithm, and complemented with hand labelling of topics. Data are collected by the original software to a relational database that houses all posts of Livejournal top users from three selected periods. The research finds that Livejournal top users share their attention evenly between social / political and private / recreational issues, the latter being very stable, while the influence of protests in 2011 is clearly visible in the political part of the blogs’ content. The group of topics centred around social issues demonstrates the biggest volatility and may serve as an online public opinion barometer that may be applied for proactive policy making.
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.
Tech Mining, a special form of “Big Data” analytics, aims to generate Competitive Technical Intelligence (CTI) using bibliometric and text-mining software (e.g., VantagePoint, TDA) as well as other analytical & visualization applications for analyses of Science, Technology & Innovation (ST&I) information resources. The goal of the conference is to ENGAGE cross-disciplinary networks of analysts, software specialists, researchers, policymakers, and managers toADVANCE the use of textual information in multiple science, technology, and business development fields. The conference program will address key CHALLENGES in:
Data
Sourcing, preparing, and interpreting data sources including patents, publications, webscraping, and other novel data sourcesText-mining tools and methods
Best practices in software-based topic modeling, clumping, association rules, term manipulation, text manipulation, etc. VisualizationApplied research
Future-Oriented Technology Analysis (FTA) Intelligence gathering to support decision-making in the private sector (e.g., Management of Technology)This conference is intended for researchers and students across multiple fields, especially Scientometrics, Public Policy, Management of Technology and Information Science.
An important text mining problem is to fi nd, in a large collection of texts, documents related to speci c topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to nd the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain prede ned sets of keywords (that de ne the topics researchers are interested in) are restricted to speci c intervals of topic assignments. We present a case study on a Russian LiveJournal dataset aimed at ethnicity discourse analysis.
In this paper we introduce a generalized learning algorithm for probabilistic topic models (PTM). Many known and new algorithms for PLSA, LDA, and SWB models can be obtained as its special cases by choosing a subset of the following “options”: regularization, sampling, update frequency, sparsing and robustness. We show that a robust topic model, which distinguishes specific, background and topic terms, doesn’t need Dirichlet regularization and provides controllably sparse solution.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.