Contribution, Social networking, and the Request for Adminship process in Wikipedia

International audienceIn epistemic communities, people are said to be selected on their contribution in knowledge to the project (articles, codes, etc.). However, the socialization process is an important factor for inclusion, sustainability as a contributor, and promotion. Finally, what matters for being promoted? Being a good contributor? Being a good animator? Knowing the boss? We explore this question by looking at the election process for administrators in the English Wikipedia. We used the candidates' revisions and/or social attributes to construct a predictive model of promotion success, based on the candidates' past behavior and a random forest algorithm. Our model explains 78% of the results, which is better than the former models. It also helps to refine the explanation of the election process

Social Interactions vs Revisions, What is important for Promotion in Wikipedia?

In epistemic community, people are said to be selected on their knowledge contribution to the project (articles, codes, etc.) However, the socialization process is an important factor for inclusion, sustainability as a contributor, and promotion. Finally, what does matter to be promoted? being a good contributor? being a good animator? knowing the boss? We explore this question looking at the process of election for administrator in the English Wikipedia community. We modeled the candidates according to their revisions and/or social attributes. These attributes are used to construct a predictive model of promotion success, based on the candidates's past behavior, computed thanks to a random forest algorithm. Our model combining knowledge contribution variables and social networking variables successfully explain 78% of the results which is better than the former models. It also helps to refine the criterion for election. If the number of knowledge contributions is the most important element, social interactions come close second to explain the election. But being connected with the future peers (the admins) can make the difference between success and failure, making this epistemic community a very social community too

Vote Prediction Models for Signed Social Networks

Voting is an integral part of the decision-making mechanism in many communities. Voting decides which bills become laws in parliament or users become administrators on Wikipedia. Understanding a voter's behaviour and being able to predict how they will vote can help in selecting better and more successful policies or candidates. As votes tend to be for or against a particular agenda, they can be intuitively represented by positive or negative links respectively in a signed network. These signed networks allow us to view voting through the lens of graph theory and network analysis. Predicting a vote translates into predicting the sign of a link in the network. The task of sign prediction in signed networks is well studied and many approaches utilize social theories of balance and status in a network. However, most conventional methods are generic and disregard the iterative nature of voting in communities. Therefore this thesis proposes two new approaches for solving the task of vote prediction in signed networks. The first is a graph combination method that gathers features from multiple auxiliary graphs as well as encoding balance and status theories using triads. Then, it becomes a supervised machine learning problem which can be solved using any general linear model. Second, we propose a novel iterative method to learn relationships between users to predict votes. We quantify a network's adherence to status theory using the concept of agony and hierarchy in directed networks. Analogously, we use the spectral decomposition of the network to measure its balance. These measures are then used to predict the votes that comply the most with the social theories. We implement our approaches to predict votes in the elections of administrators in Wikipedia. Our experiments and results on the Wiki-RfA dataset show that the iterative models perform much better than the graph combination model. We analyse the impact of the voting order on the performance of these models. Furthermore, we find that balance theory represents votes in Wikipedia elections better than status theory

Reputation assessment in collaborative environments.

The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

Reputation assessment in collaborative environments.

The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

Reputation assessment in collaborative environments.

The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

The experience as a document: designing for the future of collaborative remembering in digital archives

How does it feel when we remember together on-line? Who gets to say what it is worth to be remembered? To understand how the user experience of participation is affecting the formation of collective memories in the context of online environments, first it is important to take into consideration how the notion of memory has been transformed under the influence of the digital revolution. I aim to contribute to the field of User Experience (UX) research theorizing on the felt experience of users from a memory perspective, taking into consideration aspects linked to both personal and collective memories in the context of connected environments.Harassment and hate speech in connected conversational environments are specially targeted to women and underprivileged communities, which has become a problem for digital archives of vernacular creativity (Burgess, J. E. 2007) such as YouTube, Twitter, Reddit and Wikipedia. An evaluation of the user experience of underprivileged communities in creative archives such as Wikipedia indicates the urgency for building a feminist space where women and queer folks can focus on knowledge production and learning without being harassed. The theoretical models and designs that I propose are a result of a series of prototype testing and case studies focused on cognitive tools for a mediated human memory operating inside transactive memory systems. With them, aims to imagine the means by which feminist protocols for UX design and research can assist in the building and maintenance of the archive as a safe/brave space.Working with perspectives from media theory, memory theory and gender studies and centering the user experience of participation for women, queer folks, people of colour (POC) and other vulnerable and underrepresented communities as the main focus of inquiring, my research takes an interdisciplinary approach to interrogate how online misogyny and other forms of abuse are perceived by communities placed outside the center of the hegemonic normativity, and how the user experience of online abuse is affecting the formation of collective memories in the context of online environments

The role of offline ties in online communities : the case of Wikipedia

This thesis investigates the role of offline ties in online communities, taking the online encyclopaedia Wikipedia as an example. It uses publicly available data collected from the German Wikipedia to assess whether offline meeting participation affects editors' behaviour in three different domains: 1) productivity and collaboration, 2) norm-relevant behaviour, and 3) election participation. Data was collected on over 4000 meetings covering the period between the creation of the German Wikipedia in 2001 to March 2020. In the first substantive chapter of this thesis, matching meetup attendees with a comparable control group and employing a difference-in-differences design, I find positive and significant effects of meetup attendance on productivity on Wikipedia, measured as the number of edits. In the second substantive chapter, I build upon the theoretical arguments put forward by Coleman (1990) and test whether offline network density influences norm-relevant behaviour. I find only limited importance of the offline network: those attending meetups tend to both experience and conduct fewer norm violations, and they give and receive generally more rewards. However, the density of the offline network does not play a noteworthy role in explaining online norm violation and norm enforcement, except that those in high-density off- line networks generally give fewer rewards. Lastly, for the third substantive chapter, I collected data on all elections for administrators on the German Wikipedia. Using hybrid multilevel random effects models, I find that offline participation measures influence whether one is successful as a candidate, and whether and how one votes. This highlights important processes in situations of public elections. This study is one of the first to bridge the gap between online and offline behaviour, using digital trace data and offline meeting data on a large scale. The findings emphasise how offline interactions in online communities can affect the community and the important role of social capital. They have implications for online communities and Wikimedia in regard to understanding the importance of meetups and (inequality in) access to meetings

Computational intelligent methods for trusting in social networks

104 p.This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network

Évaluation de la confiance dans la collaboration à large échelle

Large-scale collaborative systems wherein a large number of users collaborate to perform a shared task attract a lot of attention from both academic and industry. Trust is an important factor for the success of a large-scale collaboration. It is difficult for end-users to manually assess the trust level of each partner in this collaboration. We study the trust assessment problem and aim to design a computational trust model for collaborative systems. We focused on three research questions. 1. What is the effect of deploying a trust model and showing trust scores of partners to users? We designed and organized a user-experiment based on trust game, a well-known money-exchange lab-control protocol, wherein we introduced user trust scores. Our comprehensive analysis on user behavior proved that: (i) showing trust score to users encourages collaboration between them significantly at a similar level with showing nick- name, and (ii) users follow the trust score in decision-making. The results suggest that a trust model can be deployed in collaborative systems to assist users. 2. How to calculate trust score between users that experienced a collaboration? We designed a trust model for repeated trust game that computes user trust scores based on their past behavior. We validated our trust model against: (i) simulated data, (ii) human opinion, and (iii) real-world experimental data. We extended our trust model to Wikipedia based on user contributions to the quality of the edited Wikipedia articles. We proposed three machine learning approaches to assess the quality of Wikipedia articles: the first one based on random forest with manually-designed features while the other two ones based on deep learning methods. 3. How to predict trust relation between users that did not interact in the past? Given a network in which the links represent the trust/distrust relations between users, we aim to predict future relations. We proposed an algorithm that takes into account the established time information of the links in the network to predict future user trust/distrust relationships. Our algorithm outperforms state-of-the-art approaches on real-world signed directed social network datasetsLes systèmes collaboratifs à large échelle, où un grand nombre d’utilisateurs collaborent pour réaliser une tâche partagée, attirent beaucoup l’attention des milieux industriels et académiques. Bien que la confiance soit un facteur primordial pour le succès d’une telle collaboration, il est difficile pour les utilisateurs finaux d’évaluer manuellement le niveau de confiance envers chaque partenaire. Dans cette thèse, nous étudions le problème de l’évaluation de la confiance et cherchons à concevoir un modèle de confiance informatique dédiés aux systèmes collaboratifs. Nos travaux s’organisent autour des trois questions de recherche suivantes. 1. Quel est l’effet du déploiement d’un modèle de confiance et de la représentation aux utilisateurs des scores obtenus pour chaque partenaire ? Nous avons conçu et organisé une expérience utilisateur basée sur le jeu de confiance qui est un protocole d’échange d’argent en environnement contrôlé dans lequel nous avons introduit des notes de confiance pour les utilisateurs. L’analyse détaillée du comportement des utilisateurs montre que: (i) la présentation d’un score de confiance aux utilisateurs encourage la collaboration entre eux de manière significative, et ce, à un niveau similaire à celui de l’affichage du surnom des participants, et (ii) les utilisateurs se conforment au score de confiance dans leur prise de décision concernant l’échange monétaire. Les résultats suggèrent donc qu’un modèle de confiance peut être déployé dans les systèmes collaboratifs afin d’assister les utilisateurs. 2. Comment calculer le score de confiance entre des utilisateurs qui ont déjà collaboré ? Nous avons conçu un modèle de confiance pour les jeux de confiance répétés qui calcule les scores de confiance des utilisateurs en fonction de leur comportement passé. Nous avons validé notre modèle de confiance en relativement à: (i) des données simulées, (ii) de l’opinion humaine et (iii) des données expérimentales réelles. Nous avons appliqué notre modèle de confiance à Wikipédia en utilisant la qualité des articles de Wikipédia comme mesure de contribution. Nous avons proposé trois algorithmes d’apprentissage automatique pour évaluer la qualité des articles de Wikipédia: l’un est basé sur une forêt d’arbres décisionnels tandis que les deux autres sont basés sur des méthodes d’apprentissage profond. 3. Comment prédire la relation de confiance entre des utilisateurs qui n’ont pas encore interagi ? Etant donné un réseau dans lequel les liens représentent les relations de confiance/défiance entre utilisateurs, nous cherchons à prévoir les relations futures. Nous avons proposé un algorithme qui prend en compte les informations temporelles relatives à l’établissement des liens dans le réseau pour prédire la relation future de confiance/défiance des utilisateurs. L’algorithme proposé surpasse les approches de la littérature pour des jeux de données réels provenant de réseaux sociaux dirigés et signé