For Whom the Bot Tolls: A Neural Networks Approach to Measuring Political Orientation of Twitter Bots in Russia
Computational propaganda and the use of automated accounts in social media have recently become the focus of public attention, with alleged Russian government activities abroad provoking particularly widespread interest. However, even in the Russian domestic context, where anecdotal evidence of state activity online goes back almost a decade, no public systematic attempt has been made to dissect the population of Russian social media bots by their political orientation. We address this gap by developing a deep neural network classifier that separates pro-regime, anti-regime, and neutral Russian Twitter bots. Our method relies on supervised machine learning and a new large set of labeled accounts, rather than externally obtained account affiliations or orientation of elites. We also illustrate the use of our method by applying it to bots operating in Russian political Twitter from 2015 to 2017 and show that both pro- and anti-Kremlin bots had a substantial presence on Twitter.
Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process. This volume contains the contributions to CDUD 2011, the International Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow. The main goal of this workshop was to provide a forum for researchers and developers of data mining instruments working on issues with analyzing unstructured data. We are proud that we could welcome 13 valuable contributions to this volume. The majority of the accepted papers described innovative research on data discovery in unstructured texts. Authors worked on issues such as transforming unstructured into structured information by amongst others extracting keywords and opinion words from texts with Natural Language Processing methods. Multiple authors who participated in the workshop used methods from the conceptual structures field including Formal Concept Analysis and Conceptual Graphs. Applications include but are not limited to text mining police reports, sociological definitions, movie reviews, etc.
Software system Cordiet-FCA is presented, which is designed for knowledge discovery in big dynamic data collections, including texts in natural language. Cordiet-FCA allows one to compose ontology-controlled queries and outputs concept lattice, implication bases, association rules, and other useful concept-based artifacts. Efficient algorithms for data preprocessing, text processing, and visualization of results are discussed. Examples of applying the system to problems of medical diagnostics, criminal investigations are considered.
This volume brings together twenty four articles by eminent historians from around Europe, presented in form of papers at the international conference on the Crimean War (1853-1856) held in Warsaw in 2007.
Proceeding of the 15th International Conference on Artificial Intelligence: Methodology, Systems, Applications , AIMSA 2012, Varna, Bulgaria, September 12-15, 2012.
We examine the questions of applying large pyramidal neural (intellectual neuron) networks to solve equipment object control problems. We consider the description of a system for dynamic planning of mobile robot behavior, constructed based on a network of similar elements.
This paper is an overview of the current issues and tendencies in Computational linguistics. The overview is based on the materials of the conference on computational linguistics COLING’2012. The modern approaches to the traditional NLP domains such as pos-tagging, syntactic parsing, machine translation are discussed. The highlights of automated information extraction, such as fact extraction, opinion mining are also in focus. The main tendency of modern technologies in Computational linguistics is to accumulate the higher level of linguistic analysis (discourse analysis, cognitive modeling) in the models and to combine machine learning technologies with the algorithmic methods on the basis of deep expert linguistic knowledge.
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 consider opinion word extraction, one of the key problems in sentiment analysis. Sentiment analysis (or opinion mining) is an important research area within computational linguistics. Opinion words, which form an opinion lexicon, describe the attitude of the author towards certain opinion targets, i.e., entities and their attributes on which opinions have been expressed. Hence, the availability of a representative opinion lexicon can facilitate the extraction of opinions from texts. For this reason, opinion word mining is one of the key issues in sentiment analysis. We designed and implemented several methods for extracting opinion words. We evaluated these approaches by testing how well the resulting opinion lexicons help improve the accuracy of methods for determining the polarity of the reviews if the extracted opinion words are used as features. We used several machine learning methods: SVM, Logistic Regression, Naive Bayes, and KNN. By using the extracted opinion words as features we were able to improve over the baselines in some cases. Our experiments showed that, although opinion words are useful for polarity detection, they are not su fficient on their own and should be used only in combination with other features.
The article deals with the processes of building the information society and security in the CIS in accordance with modern conditions. The main objective is to review existing mechanisms for the formation of a common information space in the Eurasian region, regarded as one of the essential aspects of international integration. The theoretical significance of the work is to determine the main controls of the regional information infrastructure, improved by the development of communication features in a rapid process.The practical component consists in determining the future policies of the region under consideration in building the information society. The study authors used historical-descriptive approach and factual analysis of events having to do with drawing the contours of today's global information society in the regional refraction.
The main result is the fact that the development of information and communication technologies, and network resources leads to increased threats of destabilization of the socio-political situation in view of the emergence of multiple centers that generate the ideological and psychological background. Keeping focused information policy can not be conceived without the collective participation of States in the first place, members of the group leaders of integration - Russia, Belarus and Kazakhstan. Currently, only produced a comprehensive approach to security in the information field in the Eurasian region, but the events in the world, largely thanks to modern technology, make the search for an exit strategy with a much higher speed. The article contributes to the science of international relations, engaging in interdisciplinary thinking that is associated with a transition period in the development of society. A study of current conditions in their relation to the current socio-political patterns of the authors leads to conclusions about the need for cooperation with the network centers of power in the modern information environment, the formation of alternative models of networking, especially in innovation and scientific and technical areas of information policy, and expanding the integration of the field in this region on the information content.
This special publication for the 2012 New Delhi Summit is a collection of articles by government officials from BRICS countries, representatives of international organizations, businessmen and leading researchers.
The list of Russian contributors includes Sergei Lavrov, Foreign Minister of Russia, Maxim Medvedkov, Director of the Trade Negotiations Department of the Russian Ministry of Economic Development, Vladimir Dmitriev, Vnesheconombank Chairman, Alexander Bedritsky, advisor to the Russian President, VadimLukov, Ambassador-at-large of the Russian Foreign Affairs Ministry, and representatives of the academic community.
The publication also features articles by the President of Kazakhstan NursultanNazarbayev and internationally respected economist Jim O’Neil, who coined the term “BRIC”. In his article Jim O’Neil speculates about the future of the BRICS countries and the institution as a whole.
The publication addresses important issues of the global agenda, the priorities of BRICS and the Indian Presidency, the policies and competitive advantages of the participants, as well as BRICS institutionalization, enhancing efficiency and accountability of the forum.