Document controversy classification based on the Wikipedia category structure

Dispute and controversy are parts of our culture and cannot be omitted on the Internet (where it becomes more anonymous). There have been many studies on controversy, especially on social networks such as Wikipedia. This free on-line encyclopedia has become a very popular data source among many researchers studying behavior or natural language processing. This paper presents using the category structure of Wikipedia to determine the controversy of a single article. This is the first part of the proposed system for classification of topic controversy score for any given text

APPLICATION OF MULTI-CRITERIA ANALYSIS BASED ON THE INDIVIDUAL PSYCHOLOGICAL PROFILE FOR RECOMMENDER SYSTEMS

This paper presents a novel approach for user classification exploiting multicriteriaanalysis. This method is based on measuring the distance between anobservation and its respective Pareto front. The obtained results show that thecombination of the standard KNN classification and the distance from Paretofronts gives satisfactory classification accuracy – higher than the accuracy obtainedfor each of these methods applied separately. Conclusions from thisstudy may be applied in recommender systems where the proposed methodcan be implemented as the part of the collaborative filtering algorithm

INCREASING THE WILLINGNESS TO COLLABORATE ONLINE: AN ANALYSIS OF SENTIMENT-DRIVEN INTERACTIONS IN PEER CONTENT PRODUCTION

We investigate mechanisms that trigger collaborative work behavior in online peer communities. We regard the collaboration among Wikipedia editors as a social process influenced by specific communication practices. We analyze and quantify the way Wikipedia editors communicate their feedback and support towards each others’ work in form of sentiments and opinions, and explore to what extent this influences online trust among them. We show that peer content production in Wikipedia is influenced by sharing sentiments during discussions among editors. At the global level, sharing sentiments positively influences the level of online trust. We also find a significant difference in the amount of online trust among editors who share mainly positive or mainly negative sentiments. We further suggest that providing and receiving especially supportive feedback expressed in form of positive sentiments and opinions may be beneficial in terms of virtual teamwork

MEASURING THE INFLUNENCE OF PROJECT CHARACTERISTICS ON OPTIMAL SOFTWARE PROJECT GRANULARITY

With a well-structured design of reusable software, it is possible to save costs in future software development cycles and generate added value. Nowadays it is difficult to quantify which grade of project structuring enhances its reusability. This is the question this paper aims to examine, based on the financial Portfolio Selection theory and Value at Risk methods that can be adapted to the field of software project structuring. Using these methods, the value of the reusable source code can be evaluated and the risks of reimplementation be compared with alternative code structures. So we can quantify and measure the effects of the software project structure on the risks of reimplementation. This model is applied to 27 Github open source software projects and enables us to investigate the influences of the project characteristics on the best possible software project granularity

The Impact of Sentiment-driven Feedback on Knowledge Reuse in Online Communities

Knowledge reuse is of increasing importance for organizations. Despite the extant research, we still do not adequately understand the ways peers are motivated to reuse knowledge with the help of wiki technologies. In this paper, we study the motivation for knowledge reuse in a prominent instance of online social production: Wikipedia. Studying knowledge reuse in Wikipedia is important since Wikipedia has been able to leverage the benefits of efficient knowledge reuse to produce knowledge goods of relatively high quality. Specifically, we explore: 1) how Wikipedia editors (any peer who contributes to developing articles in Wikipedia) communicate their feedback toward each other’s work in peer conversations and 2) to what extent sentiment-driven feedback impacts the level of knowledge reuse in Wikipedia. The results show that displaying sentiment-driven feedback positively influenced the level of knowledge reuse. Our study further shows a significant difference in the level of knowledge reuse between editors who shared mainly positive or mainly negative sentiments. Specifically, displaying mainly positive feedback corresponded to a superior level of knowledge reuse than displaying mainly negative feedback. We contribute to the extant literature of online social production communities in general and Wikipedia in particular by providing a first building block for research on peer feedback’s role in developing and sustaining wiki-based knowledge reuse. We discuss our findings’ implications for theory and practice

What We Know About Wikipedia: A Review of the Literature Analyzing the Project(s).

This article proposes a review of the literature analyzing Wikipedia as a collective system for producing knowledge. JEL Classification: L39, L86, H41, D7