Radoslaw Nielek

Learning About the Quality of Teamwork from Wikiteams

This paper describes an approach to evaluating teams of contributors in Wikipedia based on social network analysis. We present the idea of creating an implicit social network based on characteristics of pages’ edit history and collaboration between contributors. This network consists of four dimensions: trust, distrust, acquaintance and knowledge. Trust and distrust are based on content modifications (copying and deleting respectively), acquaintance is based on the amount of discussion on articles’ talk pages between a given pair of authors and knowledge is based on the categories in which an author typically contributes. Our social network is based on the entire Wikipedia edit history, and therefore is a summary of all recorded author interactions. This social network can be used to assess the quality of a team of authors and consequently, to recommend good teams. The social network can also be used by Wikipedia authors and editors as an additional tool that allows to improve the author’s collaboration, as it expresses each author’s social environment and can be navigated to discover new projects that an author can participate in, or to recommend new collaborators.

WikiTeams: How Do They Achieve Success?

Web 2.0 technology and so-called social media are among the most popular (among users and researchers alike) Internet technologies today. Among them, Wiki technology - created to simplify HTML editing and enable open, collaborative editing of pages by ordinary Web users - occupies an important place. Wiki is increasingly adopted by businesses as a useful form of knowledge management and sharing, creating “corporate Wikis.” However, the most widely known application of Wiki technology - Wikipedia - is, according to many analysts, more than just an open encyclopedia that uses Wiki.

Spiral of hatred: social effects in Internet auctions. Between informativity and emotion

An auction platform is a dynamic environment where a rich variety of social effects can be observed. Most of those effects remain unnoticed or even hidden to ordinary users. The in-depth studies of such effects should allow us to identify and understand the key factors influencing users’ behaviour. The material collected from the biggest Polish auction house has been analyzed. NLP algorithms were applied to extract sentiment-related content from collected comments and to measure informativity. Emotional distance between negative, neutral and positive comments has been calculated. The obtained results confirm the existence of the spiral-of-hatred effect but also indicate that much more complex patterns of mutual relations between sellers and buyers exist. The last section contains a several suggestions which can prove useful to improve trustworthiness of users’ reports and security of an auction platform in general.

Predicting Controversy of Wikipedia Articles Using the Article Feedback Tool

Different points of view, opinions and controversies constitute the inherent part of modern society. Early detection of controversy is crucial for increasing productivity in peer production systems. The paper presents novelty approach to detecting controversial articles on the Wikipedia based on users ratings from Article Feedback Tool. The performance of proposed approach is on par with state-of-the-art solutions, but may also be applied outside Wikipedia-like systems. Additionally, emotion polarity measures can be used to locate controversial parts of articles, based on talk pages sections. With help of proposed algorithms, all articles in English Wikipedia have been tagged as either controversial or non-controversial. The dataset has been published and can be used by other researchers.

A Location-Based Game for Two Generations: Teaching Mobile Technology to the Elderly with the Support of Young Volunteers

This paper presents a cooperative location-based game for the elderly with the use of tablets equipped with mobile application. The game was designed to tackle at once several crucial topics related to the issue of aging, namely the social inclusion, education in the field of modern technology, motivation for learning as well as physical activity. Mixed-aged teams consisting of two players: a junior and a senior took part in the game. The preliminary results suggest that the game can successfully address a number of issues including improving the elderly technical skills, increasing the elderly physical activity as well as positive intergenerational interaction. The paper describes the game setup in details and presents some initial data gathered during the gameplay.

Using Stereotypes to Identify Risky Transactions in Internet Auctions

Encountering unknown sellers is very common in online auction sites. In such a scenario, a buyer can not estimate trustworthiness of the unknown seller based on the sellers past behavior. The buyer is thus exposed to the risks of being cheated. In this paper we describe a stereotypes based mechanism to determine the risk of a potential transaction even if the seller is personally unknown to not only the buyer but also to the rest of the system. Specifically, our approach first identifies discriminating attributes which are capable of distinguishing successful transactions from unsuccessful ones. A buyer can use its own past transactions (with other sellers) to form such stereotypes. Alternatively, the communitys collective knowledge can also be used to build such stereotypes. When posed to a potential transaction with an unknown seller, buyers can estimate trustworthiness (and thus the risk) by combining the corresponding stereotypes. We report experiments over real auction data collected from Allegro, a leading auction site in Eastern Europe. Data driven simulation results show that by setting suitable thresholds our approach can effectively detect (predict) frauds, i.e., has low false positive, with flagging very few successful transactions, that is, it has very low false negative. We also observe from these experiments that local knowledge derived stereotypes are the most accurate, since it is personalized for individual buyers. However, community knowledge derived stereotypes are particularly useful for inexperienced buyers that dominate online auction sites, though there is slight decrease in accuracy. We leverage such analytics to provide a browser (Firefox) based tool to guide buyers during live auctions.

Predicting webpage credibility using linguistic features

The article focuses on predicting trustworthiness from textual content of webpages. The recent work Olteanu et al. proposes a number of features (linguistic and social) to apply machine learning methods to recognize trust levels. We demonstrate that this approach can be substantially improved in two ways: by applying machine learning methods to vectors computed, using psychosocial and psycholinguistic features and in a high-dimensional bag-of-words paradigm of word occurrences. Following Olteanu et al., we test the methods in two classification settings, as a 2-class and 3-class scenario, and in a regression setting. In the 3-class scenario, the features compiled by Olteanu et al. achieve weighted precision of 0.63, while the methods proposed in our paper raise it to 0.66 and 0.70. We also examine coefficients of the models in order to discover words associated with low and high trust.

LivingLab PJAIT: towards better urban participation of seniors

In this paper we provide a brief summary of development LivingLab PJAIT as an attempt to establish a comprehensive and sustainable ICT-based solution for empowerment of elderly communities towards better urban participation of seniors. We report on our various endeavors for better involvement and participation of older adults in urban life by lowering ICT barriers, encouraging social inclusion, intergenerational interaction, physical activity and engaging older adults in the process of development of ICT solutions. We report on a model and assumptions of the LivingLab PJAIT as well as a number of activities created and implemented for LivingLab participants: from ICT courses, both traditional and e-learning, through on-line crowdsourcing tasks, to blended activities of different forms and complexity. We also provide conclusions on the lessons learned in the process and some future plans, including solutions for better senior urban participation and citizen science.

Temporal, Cultural and Thematic Aspects of Web Credibility

Is trust to web pages related to nation-level factors? Do trust levels change in time and how? What categories (topics) of pages tend to be evaluated as not trustworthy, and what categories of pages tend to be trustworthy? What could be the reasons of such evaluations? The goal of this paper is to answer these questions using large scale data of trustworthiness of web pages, two sets of websites, Wikipedia and an international survey.

Understanding and predicting Web content credibility using the Content Credibility Corpus

Abstract The goal of our research is to create a predictive model of Web content credibility evaluations, based on human evaluations. The model has to be based on a comprehensive set of independent factors that can be used to guide user’s credibility evaluations in crowdsourced systems like WOT, but also to design machine classifiers of Web content credibility. The factors described in this article are based on empirical data. We have created a dataset obtained from an extensive crowdsourced Web credibility assessment study (over 15 thousand evaluations of over 5000 Web pages from over 2000 participants). First, online participants evaluated a multi-domain corpus of selected Web pages. Using the acquired data and text mining techniques we have prepared a code book and conducted another crowdsourcing round to label textual justifications of the former responses. We have extended the list of significant credibility assessment factors described in previous research and analyzed their relationships to credibility evaluation scores. Discovered factors that affect Web content credibility evaluations are also weakly correlated, which makes them more useful for modeling and predicting credibility evaluations. Based on the newly identified factors, we propose a predictive model for Web content credibility. The model can be used to determine the significance and impact of discovered factors on credibility evaluations. These findings can guide future research on the design of automatic or semi-automatic systems for Web content credibility evaluation support. This study also contributes the largest credibility dataset currently publicly available for research: the Content Credibility Corpus (C3).