Dynamics of conflicts in Wikipedia

In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.Comment: Supporting information adde

Expressing Trust with Temporal Frequency of User Interaction in Online Communities

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gain trust in a system such as phone numbers, reviews, and ratings. Recently, a novel model that tries to overcome this issue was presented: the Dynamic Interaction-based Reputation Model (DIBRM). This approach to trust considers only implicit information automatically deduced from the interactions of users within an online community. In this primary research study, the Reddit and MathOverflow online social communities have been selected for testing DIBRM. Results show how this novel approach to trust can mimic behaviors of the selected reputation systems, namely Reddit and MathOverflow, only with temporal information

An Exploratory Study of Values Alignments in a Teacher Professional Development Digital Badge System

This exploratory study used case study methods to identify whether value alignments between users and system features could be detected in an online digital badge system and learning environment, and if so, whether those value alignments could be said to affect use of the system. Values are “guiding principles of what people consider important in life” (Cheng & Fleischmann, 2010, n.p.) and are believed to have explanatory power in predicting behaviors and attitudes (Schwartz, 2007). A value sensitive design research method had to be devised anew to address the research questions and is arguably the major contribution of this study. First, a self-report scale (Portrait Values Questionnaire-RR) developed by Schwartz et al. (2012) was used to categorize the pragmatic values of teachers and administrators using the online VIF Learning Center badging into four higher order values: self-transcendence, conservation, openness to change, and self-enhancement. Statistically significant differences were found between male and female teachers, but not between teachers and administrators, nor between teachers mandated to use the system and those for whom use was optional. Second, the 19 values of Schwartz’s revised and refined theory of basic human values were used to assign human values to 11 feature-action pairs identified in the VIF Learning Center’s digital platform. Usage of the feature-action pairs was sparse, and data were spread unevenly, suggesting possible data loss or an indication that technical affordances were weak drivers of participation and engagement.Doctor of Philosoph

Expressing Trust with Temporal Frequency of User Interaction in Online Communities

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gain trust in a system such as phone numbers, reviews, and ratings. Recently, a novel model that tries to overcome this issue was presented: the Dynamic Interaction-based Reputation Model (DIBRM). This approach to trust considers only implicit information automatically deduced from the interactions of users within an online community. In this primary research study, the Red-dit and MathOverflow online social communities have been selected for testing DIBRM. Results show how this novel approach to trust can mimic behaviors of the selected reputation systems, namely Reddit and MathOverflow, only with temporal information

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

Generating quality open content: A functional group perspective based on the time, interaction, and performance theory

We applied the Input-Process-Output approach and Time, Interaction, and Performance theory to examine the input factors (organisational, group-related, and individual) and process factors (group production, group well-being, and member support) that yield group effectiveness, measured as high-quality articles in Wikipedia. The results provided evidence of the positive effects of: group size and shared experience on both group process variables and group effectiveness; group heterogeneity on group production; organisational support and member activeness on group well-being; member activeness on member support; and organisational support and member activeness on group effectiveness

Social Structure and Mechanisms of Collective Production: Evidence from Wikipedia

In my dissertation I propose three counterintuitive social mechanisms to alleviate the risk that collective production will fail to maintain participant involvement and respond to demand. My first study, based on a panel dataset of edits and views of articles in the English Wikipedia, shows that, although collective production lacks a price-like mechanism to estimate demand for the goods it produces, consumers’ contributions act as such a signal to expert producers. In the second paper I examine the theory that collective production participation is greatest when social norms of collaboration are obeyed. Using a large panel dataset of production networks and normrelated behavior in Wikipedia, I show that social norm infringement is not completely detrimental to participation because norm enforcement increases the likelihood that the beneficiary producer continues participating. In my third paper, I rely on interviews with experienced Wikipedia producers to examine whether producers’ ties to non-participants in collective production increase the likelihood of turnover, and whether producers’ embeddedness in collective production reduces turnover risk. Surprisingly, I find that producers with networks rich in ties to non-producers and with a task-oriented approach to collective production are those least likely to stop participating