Anna Zygmunt

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 well.

Different approaches to community evolution prediction in blogosphere

Predicting the future direction of community evolution is a problem with high theoretical and practical significance. It allows to determine which characteristics describing communities have importance from the point of view of their future behaviour. Knowledge about the probable future career of the community aids in the decision concerning investing in contact with members of a given community and carrying out actions to achieve a key position in it. It also allows to determine effective ways of forming opinions or to protect group participants against such activities. In the paper, a new approach to group identification and prediction of future events is presented together with the comparison to existing method. Performed experiments prove a high quality of prediction results. Comparison to previous studies shows that using many measures to describe the group profile, and in consequence as a classifier input, can improve predictions.

Models of social groups in blogosphere based on information about comment addressees and sentiments

This work concerns the analysis of number, sizes and other characteristics of groups identified in the blogosphere using a set of models identifying social relations. These models differ regarding identification of social relations, influenced by methods of classifying the addressee of the comments (they are either the post author or the author of a comment on which this comment is directly addressing) and by a sentiment calculated for comments considering the statistics of words present and connotation. The state of a selected blog portal was analyzed in sequential, partly overlapping time intervals. Groups in each interval were identified using a version of the CPM algorithm, on the basis of them, stable groups, existing for at least a minimal assumed duration of time, were identified.

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 3–5 last periods.

Analysis of Roles and Groups in Blogosphere

In the paper different roles of users in social media, taking into consideration their strength of influence and different degrees of cooperativeness, are introduced. Such identified roles are used for the analysis of characteristics of groups of strongly connected entities. The different classes of groups, considering the distribution of roles of users belonging to them, are presented and discussed.

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.

Finding Influential Bloggers

Blogging is a popular way of expressing opinions and discussing topics. Bloggers demonstrate different levels of commitment and most interesting are influential bloggers. Around such bloggers, the groups are forming, which concentrate users sharing similar interests. Finding such bloggers is an important task and has many applications e.g. marketing, business, politics. Influential ones affect others which is related to the process of diffusion. However, there is no objective way to telling which blogger is more influential. Therefore, researchers take into consideration different criteria to assess bloggers (e.g. SNA centrality measures). In this paper we propose new, efficient method for influential bloggers discovery which is based on relation of commenting in bloggers thread and is defined on bloggers level. Next, we compare results with other, comparative method proposed by Agarwal et al. called iFinder which is based on links between posts.

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

These days, different forms of social media play a significant role in the functioning of individuals and society, and social network analysis methodology ensures a better understanding of the structure and behaviour of societies forming in such environments. Including an agent–based approach to such analyses allows a more complete understanding of the specificity of given users as well as the local interactions between them. In this paper we introduce a multi–agent model of user organisations in social media, and analyse roles in social organisations based on distinguishing features of user behaviour such as activity, cooperativeness and group formation. We also analyse the range of influence of users playing given roles in the society, taking into consideration the consequences of removal of users with specific roles and carry out several experiments with data from the political blogosphere.

Analysis of Content of Posts and Comments in Evolving Social Groups

Data reflecting social and business relations has often form of network of connections between entities (called social network). In such network important and influential users can be identified as well as groups of strongly connected users. Finding such groups and observing their evolution becomes an increasingly important research problem. Analyzing the evolution of communities is useful in many applications such as marketing, politics or public security domains. One of the significant problems is to develop method incorporating not only information about connections between entities but also information obtained from text written by the users. Method presented in this chapter combine social network analysis and text mining in order to understand groups evolution. Presented approach to the group evolution process takes many aspects of the group analysis into consideration. Due to proposed method the subjects discussed within the groups are known. We noticed that subjects discussed within groups play significant roles in group evolutions.

Agent-based Modelling of Social Organisations

In the paper, the model of the society represented by a social network and the model of a multi-agent system built on the basis of this, is presented. The particular aim of the system is to predict the evolution of a society and an analysis of the communities that appear, their characteristic features and reasons for coming into being. As an example of application, an analysis was made of a social portal which makes it possible to offer and reserve places in rooms for travelling tourists.