Anna Stavrianou

Roles in social networks: Methodologies and research issues

The expansion of web user roles is, nowadays, a fact due to the ability of users to interact, discuss, exchange ideas and opinions, and form social networks through the web. The interaction level among users leads to the appearance of several social roles which can be characterized as positions, behaviors, or virtual identities. These roles may be developed in social networks, and they keep changing and evolving over time. In this article, a survey of the state-of-the-art approaches is presented regarding the identification of roles within the context of a social network. It is shown that social roles exist as a function of each other; they appear and evolve through user interaction. Different approaches are analyzed and additional characteristics that should be taken into account during the role analysis are discussed.

Expert Recommendations Based on Opinion Mining of User-Generated Product Reviews

In this article, we introduce the idea of expert recommendations whose objective is to relate review comments with users’ tasks or expectations. We propose to use fine‐grained information such as opinions and suggestions extracted using natural language processing techniques from user reviews about products, to improve a recommendation system. While typical recommender systems compare a user profile with some reference characteristics to rate unseen items, they rarely make use of the content of reviews that users have provided on a given product. In this article, we present the application of an opinion extraction system to extract opinions and suggestions from the content of the reviews, the use of the results to compare other products with the reviewed one, and eventually the recommendation of better products to the user. The recommendations are given a confidence weight by using a trust social network.

Definition and Measures of an Opinion Model for Mining Forums

Online discussion systems in the form of forums have recently been analyzed by using graphs and social network techniques. Each forum is regarded as a social network and it is modeled by a graph whose vertices represent forum participants. In this paper, we focus on the structure and the opinion content of the forum posts and we are looking at the social network that is developed from a semantics point of view. We formally define an opinion-oriented model whose purpose is to provide complementary information to the knowledge extracted by the social network model. We define and present measures that can give important information regarding the opinion flow as well as the general attitude of users and towards users throughout the whole forum. Applying our model to a real forum found on the Web shows the additional information that can be extracted.

Extracting Celebrities from Online Discussions

Online discussions became increasingly widespread with the Web 2.0: no matter the distance, whether you know the person or not, you can discuss and exchange ideas with people all over the world through forums, blogs, and newsgroups. The news websites have extensively used forums in order to encourage the reader being a real participant in the information media. This paper aims at automatically extracting the celebrities from such discussions. We propose certain meta-criteria and we provide an evaluation on a dataset of 35,175 posts written by 14,443 users. The results show that one of the proposed meta-criteria succeeds in extracting celebrities and allows for further improvements.

PROG: A Complementary Model to the Social Networks for Mining Forums

Online discussion systems in the form of forums have been represented by graphs and analyzed through social network techniques. Each forum is regarded as a social network and it is modeled by a graph whose vertices represent forum participants. Here, we focus on the structure and the opinion content of the forum postings and we are looking at the social network that is developed from a semantics point of view. We formally define a model whose purpose is to provide complementary information to the knowledge extracted by the social network model. We present structure, opinion, temporal and topic-oriented measures that can be defined based on the new model, and we discuss how these measures facilitate the analysis of an online discussion. Applying our model to a real forum found on the Web shows the additional information that can be extracted.

A Content-Oriented Framework for Online Discussion Analysis

Mining and extracting quality knowledge from online discussions is significant for the industrial and marketing sector, as well as for e-commerce applications. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph. In this paper, we propose a new framework for discussion analysis. It is based on message-based graphs where each vertex represents amessage object and each edge points out which message the specific node replies to. The edges can be weighted by the keywords that characterize the exchanged messages. This model allows a content-oriented representation of the discussion and it facilitates the identification of discussion chains. We compare the two representations (user-based and message-based graphs) and we analyze the different information that can be extracted from them. Our experiments with real data validate the proposed framework and show the additional information that can be extracted from a message-based graph.