Методы построения социо-демографических профилей пользователей сети Интернет
he paper is devoted to methods for construction of socio-demographic profile of Internet users. Gender, age, political and religion views, region, relationship status are examples of demographic attributes. This work is a survey of methods that detect demographic attributes from user’s profile and messages. The most of surveyed works are devoted to gender detection. Age, political views and region are also interested researches.
The most popular data sources for demographic attributes extraction are social networks, such as Facebook, Twitter, Youtube.
The most of solutions are based on supervised machine learning. Machine learning allows to find target values (demographic attributes) dependencies from input data and use them to predict the value of the target attribute for the new data. The following problem solving steps are surveyed in the paper: feature extraction, feature selection, model training, evaluation.
Researches use different kind of data to predict demographic attributes. The most popular data source is text. Words sequences (n-grams), parts of speech, emoticons, features specific to particular resources (eg, @ mentions and # Hashtags on Twitter) are extracted and used as input for machine learning algorithms. Social graphs are also used as source data. Communities of users that are automatically extracted from social graph are user as features for attributes prediction.
Text data produces a lot of features. Feature selection algorithms are needed to reduce feature space.
The paper surveys feature selection, classification and regression algorithms, evaluation metrics.
By analyzing the logs of corporate e-mail networks we found a number of patterns, showing how the size of ego-networks of individual employees changes on a day by day basis. We proposed a simple model that adequately describes the observed time dependence of an employee's "social circle". Comparison of experimental data with the theoretical model showed that employees are divided into two groups - with fast and slow changes in their social circles, respectively. We believe that the presence of these groups reflects both project-type and process-type of employees' activities. Comparison of data obtained before and during the global economic crisis has shown that the crisis led to an actual reduction in project-type activities.
The volume contains the abstracts of the 12th International Conference "Intelligent Data Processing: Theory and Applications". The conference is organized by the Russian Academy of Sciences, the Federal Research Center "Informatics and Control" of the Russian Academy of Sciences and the Scientific and Coordination Center "Digital Methods of Data Mining". The conference has being held biennially since 1989. It is one of the most recognizable scientific forums on data mining, machine learning, pattern recognition, image analysis, signal processing, and discrete analysis. The Organizing Committee of IDP-2018 is grateful to Forecsys Co. and CFRS Co. for providing assistance in the conference preparation and execution. The conference is funded by RFBR, grant 18-07-20075. The conference website http://mmro.ru/en/.
In an effort to make reading more accessible, an automated readability formula can help students to retrieve appropriate material for their language level. This study attempts to discover and analyze a set of possible features that can be used for single-sentence readability prediction in Russian. We test the influence of syntactic features on predictability of structural complexity. The readability of sentences from SynTagRus corpus was marked up manually and used for evaluation.
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.
The article discusses the phenomenon of interconnected glocal hospitality communities which have recently spread over the world in the context of the internet development and cultural globalization processes. It focuses on a typical community of users of CouchSurfi ng.org, a major social hospitality network in St. Petersburg. The author argues that, in the framework of this web service, there occurs a transformation of virtual groups of users localized in various spots of the globe into actual interconnected glocal communities which shape shared identities, norms, values, and practices among its members.
There have been implemented engineering and development of multi-agent recommender system «EZSurf» that performs analysis of interests and provides recommendations for the social network «VKontakte» users based on the data from profile of particular user. During the work process different methods and technological solutions have been analyzed with examination of their advantages and disadvantages. Besides of that the comparative analysis of analogous products has been held where the most similar is Russian start-up service - Surfingbird. Based on this analysis the decision of recommender system implementation and integration has been accepted. The feature of this system is that it uses social network “VKontakte” profile for user’s data collection and API of third-party services (LastFM, TheMovieDB) for an extraction of information about similar objects. Such an approach contributes into optimization of recommender system, because it does not require creation of its own object classification system and objects database. The functionality of multi-agent system was separated between three agents. First agent (Collector) collects user data from “VKontakte” profile using VK API. Second agent (Analyzer) collects similar objects from databases of thitd-party services (LastFM, TheMovieDB) that will be the criteria for further search of recommendatory content. For search and selection of information an agent (Recommender) that works as web-crawler has been implemented. System «EZSurf» can be exploited by the users of social network “VKontakte” in everyday life for time economy on web-surfing process. At the same time they will get recommendations on content that are filtered depending on preferences of every particular user.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
It is well-known that the class of sets that can be computed by polynomial size circuits is equal to the class of sets that are polynomial time reducible to a sparse set. It is widely believed, but unfortunately up to now unproven, that there are sets in EXPNP, or even in EXP that are not computable by polynomial size circuits and hence are not reducible to a sparse set. In this paper we study this question in a more restricted setting: what is the computational complexity of sparse sets that are selfreducible? It follows from earlier work of Lozano and Torán (in: Mathematical systems theory, 1991) that EXPNP does not have sparse selfreducible hard sets. We define a natural version of selfreduction, tree-selfreducibility, and show that NEXP does not have sparse tree-selfreducible hard sets. We also construct an oracle relative to which all of EXP is reducible to a sparse tree-selfreducible set. These lower bounds are corollaries of more general results about the computational complexity of sparse sets that are selfreducible, and can be interpreted as super-polynomial circuit lower bounds for NEXP.