Graphical Analysis of Social Group Dynamics

Identifying communities in social networks becomes an increasingly important research problem. Several methods for identifying such groups have been developed, however, qualitative analysis (taking into account the scale of the problem) still poses serious problems. This paper describes a tool for facilitating such an analysis, allowing to visualize the dynamics and supporting localization of different events (such as creation or merging of groups). In the final part of the paper, the experimental results performed using the benchmark data (Enron emails) provide an insight into usefulness of the proposed tool.Comment: Fourth International Conference on Computational Aspects of Social Networks, CASoN 2012, Sao Carlos, Brazil, November 21-23, 2012, pp. 41-46; IEEE Computer Society, 201

Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods). Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented

Building sentiment lexicons based on recommending services for the Polish language

Sentiment analysis has become a prominent area of research in computer science. It has numerous practical applications; e.g., evaluating customer satisfaction, identifying product promoters. Many methods employed in this task require language resources such as sentiment lexicons, which are unavailable for the Polish language. Such lexicons contain words annotated with their emotional polarization, but the manual creation of sentiment lexicons is very tedious. Therefore, this paper addresses this issue and describes a new method of building sentiment lexicons automatically based on recommending services. Next, the built lexicons were used in the task of sentiment classification

Identification of Group Changes in Blogosphere

The paper addresses a problem of change identification in social group evolution. A new SGCI method for discovering of stable groups was proposed and compared with existing GED method. The experimental studies on a Polish blogosphere service revealed that both methods are able to identify similar evolution events even though both use different concepts. Some differences were demonstrated as wellComment: The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, 2012, pp. 1233-123

Predicting Community Evolution in Social Networks

Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page

SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

This paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue summaries. We investigate the challenges it poses for automated summarization by testing several models and comparing their results with those obtained on a corpus of news articles. We show that model-generated summaries of dialogues achieve higher ROUGE scores than the model-generated summaries of news -- in contrast with human evaluators' judgement. This suggests that a challenging task of abstractive dialogue summarization requires dedicated models and non-standard quality measures. To our knowledge, our study is the first attempt to introduce a high-quality chat-dialogues corpus, manually annotated with abstractive summarizations, which can be used by the research community for further studies.Comment: Attachment contains the described dataset archived in 7z format. Please see the attached readme and licence. Update of the previous version: changed formats of train/val/test files in corpus.7

Hybrid neuro-fuzzy classifier based on NEFCLASS model Hybrydowy neuronowo-rozmyty klasyfikator oparty na modelu NEFCLASS /

Tyt. z nagłówka.Bibliogr. s. 133-135.Artykuł przedstawia zasadę działania oraz wyniki badań eksperymentalnych klasyfikatora opartego na hybrydzie sieci neuronowej z logiką rozmytą, bazujący na modelu NEFCLASS. Prezentacja struktury i działania klasyfikatora została zilustrowana wynikami eksperymentów porównawczych przeprowadzonych dla popularnych klasyfikatorów, takich jak perceptron wielowarstwowy k najbliższych sąsiadów. Skuteczność wprowadzonych modyfikacji do klasyfikatora została porównana z metodami używanymi w oryginalnym modelu NEFCLASS (metody uczenia). Jako dane benchmarkowe posłużyły wybrane bazy danych z UCI Machine Learning Repository (iris, wine, breast cancer wisconsin). Zaprezentowano również wpływ użycia metod klasyfikacji zbiorczej na efektywność klasyfikacji.The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which was modified. The presented classifier was compared to popular classifiers - neural networks and k-nearest neighbours. Efficiency of modifications in classifier was compared with methods used in original model NEFCLASS (learning methods). Accuracy of classifier was tested using 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wis-consin. Moreover, influence of ensemble classification methods on classification accuracy was presented.Dostępny również w formie drukowanej.SŁOWA KLUCZOWE: klasyfikatory neuronowo-rozmyte, NEFCLASS, sieci neuronowe, systemy rozmyte. KEYWORDS: neuro-fuzzy classifier, NEFCLASS, neural networks, fuzzy systems

The Comparison of Users Activity on the Example of Polish and American Blogosphere

Blogs are popular way to express opinions on the Internet. Due to their popularity and their public character blogs attract attention of many researchers. In this paper we compare two national blogospheres (Polish and American) from different angles such as characteristics of messages and interactions, structure of social groups, topics discussed in them, and the influence of real-world events on the behavior of such groups. In our approach we try to combine in advanced manner users activity on both the individual and community level. The comparison reveals some differences and various characters of both portals. Methods for analysis of groups dynamics, users roles, and topics in groups are presented

Combining Agent-Based and Social Network Analysis Approaches to Recognition of Role Influence in Social Media

International audienc

Achieving and maintaining important roles in social media

International audienceA significant problem common for di↵erent domains of applications is an issue of obtaining and keeping an influential position in the respective social media society. In this paper a new approach is proposed based on the analysis of roles of users in groups identified within society. Three di↵erent dimensions of user behavior are considered as key elements of these roles: their activity, influence and cooperativeness/competition. Taking into account measures describing these dimensions, a set of roles characterizing behaviors of users in groups is formulated. We propose an original set of roles with their justification in sociological models, develop an easy extendable model of a social system and conduct experiments to allow us to define patterns describing stability and variability of given roles as well as statistics of transitions in time between these considered roles. To define the roles, we took into account di↵erent features of user interactions, both quantitative and qualitative. We propose an integrated approach to the analysis of role changes in the context of group evolution. We consider behavior of users in groups with di↵erent sizes and di↵erences between them. We also analyse the stability of individual roles players by users in groups and often occurring transitions between individual roles. The obtained results, interpreted also from the sociological point of view, allow the formulation of general recommendations on which behaviors of users could ensure achieving and maintaining influential roles in social media. The most frequent patterns of transitions between roles are identified and significant similarities between them for two considered blog portals are described. The approaches and methods of analysis presented in the paper may be applied to support decisions leading to obtaining and maintaining influential positions in social media, which may be useful for the promotion of goods and services, leading business or political campaigns