A Computational Linguistic Approach towards Understanding Wikipedia\u27s Article for Deletion (AfD) Discussions

With the thriving of online deliberation, Wikipedia\u27s Article for Deletion (AfD) discussion has drawn a number of researchers\u27 attention in the past decade. In this thesis we aim to solve two main problems: 1) how to help new users effectively participate in the discussion; and 2) how to make it efficient for administrators to make decision based on the discussion. To solve the first problem, we obtain a knowledge repository for new users by recognizing imperatives. We propose a method to detect imperatives based on syntactic analysis of the texts. And the result shows a good precision and reasonable recall. To solve the second problem, we propose a decision making support system that provides administrators with an reorganized overview of a discussion. We first divide the arguments in the discussion into several groups based on similarity; then further divide each group into subgroups based on sentiment (positive, neutral and negative). In order to classify sentiment polarity, we propose a recursive algorithm based on the dependency structure of the text. Comparing with the state of the art sentiment analysis tool by Stanford, our algorithm shows a promising result of 3-categories classification without requiring a large training dataset

Social Capital in Online Temporary Organizations: Addressing Critical, Complex Tasks through Deliberation

Temporary organizations—small, task-focused, time-bound, agile groups—exist in mass collaborations to address tasks outside of existing procedures. Given that mass collaborations are informal and voluntary, this study explores the impact of social network attributes (cohesion and diversity) in temporary organizations on task completion. We suggest that participants’ prior shared experience and demonstrated knowledge of the larger organization in online temporary organizations, traits of cohesion, and working less often with the same people, evidence of diversity, lead to greater likelihood of successful task completion. Contrary to predictions, however, the less consistent the participant contributions, the lower the likelihood of successful task completion

Refugee or migrant crisis?

In recent years, increasing attention has been dedicated to the hazardous and volatile situation in the Middle East, a crisis which has pushed many to flee their countries and seek refuge in neighboring countries or in Europe. In describing or discussing these tragic events, labels such as “European migrant crisis” and “European refugee crisis” started being widely used by the media, politicians, and the online world alike. The use of such labels has the potential to dictate the ways in which displaced people are received and perceived. With this study, we investigate label use in social media (specifically YouTube), the emergent patterns of labeling that can cause further disaffection and tension or elicit sympathy, and the sentiments associated with the different labels. Our findings suggest that migration issues are being framed not only through labels characterizing the crisis but also by their describing the individuals themselves. Using topic modeling and sentiment analysis jointly, our study offers valuable insights into the direction of public sentiment and the nature of discussions surrounding this significant societal crisis, as well as the nature of online opinion sharing. We conclude by proposing a four-dimensional model of label interpretation in relation to sentiment—that accounts for perceived agency, economic cost, permanence, and threat, and identifies threat and agency to be most impactful. This perspective reveals important influential aspects of labels and frames that may shape online public opinion and alter attitudes toward those directly affected by the crisis

Our Space: Being a Responsible Citizen of the Digital World

Our Space is a set of curricular materials designed to encourage high school students to reflect on the ethical dimensions of their participation in new media environments. Through role-playing activities and reflective exercises, students are asked to consider the ethical responsibilities of other people, and whether and how they behave ethically themselves online. These issues are raised in relation to five core themes that are highly relevant online: identity, privacy, authorship and ownership, credibility, and participation.Our Space was co-developed by The Good Play Project and Project New Media Literacies (established at MIT and now housed at University of Southern California's Annenberg School for Communications and Journalism). The Our Space collaboration grew out of a shared interest in fostering ethical thinking and conduct among young people when exercising new media skills

50 whys to look for genes: Pros and complications

“Treating the audience as capable of thinking about the complexities that surround the application of genetic knowledge” was the tagline of a series of daily blog posts made over seven weeks in the fall of 2014, posts that included extended quotes from the recently published Nature-Nurture? No (Taylor 2014). This working paper is a compilation of those posts

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

Knowing together

In den letzten Jahren sind eine Reihe neuer Anwendungen im Internet entstanden, die zumeist als Web2.0 oder social software bezeichnet werden. Viele dieser Anwendungen sind gekennzeichnet durch die Einbindung mehrerer Agenten in Prozesse zur Verbreitung, Organisation und Herstellung von Wissen. Das Ziel der vorliegenden Dissertation besteht in der Analyse der epistemologischen Relevanz dieser epistemischen social software Anwendungen. Da die Kommunikation und Interaktion zwischen mehreren Agenten deren Schlüsselmerkmal darstellt, bildet die Soziale Erkenntnistheorie als philosophische Disziplin, welche die Weisen untersucht, in denen Wissen sozial bedingt ist, die theoretische Grundlage für die Analyse der epistemischen Prozesse innerhalb dieser Systeme. Weil bisher keine soziale Erkenntnistheorie eine ausreichende Theorie für die Analyse epistemischer social software zur Verfügung stellen konnte, habe ich die Grundlagen für ein neues sozio-epistemisches Model entwickelt, welches zwar im sozio-epistemologischen Diskurs verankert ist, jedoch um Erkenntnisse aus dem Feld der Science and Technology Studies (STS) erweitert wurde. Dieses Model gründet in der Klassifikation von sozio-technischen epistemischen Systemen anhand unterschiedlicher Mechanismen der Schließung, welche zur Beendigung sozio-epistemischer Prozesse verwendet werden. Diese Klassifikation anhand der drei Schließungsmechanismen Integration, Aggregation und Selektion zielt nicht auf die Einebnung der Differenzen zwischen sozio-technischen epistemischen Systemen, vielmehr liegt ihr Wert in ihrer heuristischen Fruchtbarkeit, darin Differenzen aufzumachen. Systeme, welche unterschiedliche Schließungsmechanismen nutzen, sind gebunden an unterschiedliche soziale, technische und epistemische Voraussetzungen, sie haben unterschiedliche Stärken und Schwächen und eignen sich daher für unterschiedliche epistemische Aufgaben. Das von mir entwickelte Modell lenkt dabei die Aufmerksamkeit auf ein bislang weitgehend in der sozialen Erkenntnistheorie vernachlässigtes Thema: das Technische und seine Beziehung zum Sozialen und zum Epistemischen. Da die meisten epistemischen Praktiken heute durchdrungen sind von Technologie, ist deren Berücksichtigung von entscheidender Bedeutung für jede soziale Erkenntnistheorie, die beansprucht, nicht nur normativ angemessen, sondern auch empirisch adäquat zu sein.In recent years new applications emerged on the Web which received the labels Web2.0 or social software. In many of these applications people are engaged in epistemic activities, such as the dissemination, organization or creation of knowledge. The goal of this thesis is to analyze the epistemological relevance of such epistemic social software. Because communication and interaction between multiple agents seems to be the key to understand the epistemic processes within such systems, social epistemology, the philosophical discipline exploring the ways and the extent to which knowledge is social, was chosen as a theoretical framework. However, none of the existing comprehensive social epistemologies delivers a sufficient framework to analyze epistemic social software. Therefore, I have developed a new socio-epistemological framework to analyze epistemic social software which is rooted in socio-epistemological discourse, but amends it with insights from the field of Science and Technology Studies (STS). My framework is founded on a tripartite classification of socio-technical epistemic system based on the mechanisms they employ to close socio-epistemic processes. These three mechanisms are integration, aggregation and selection. With this classification I do not aim at reducing the differences between systems to their mechanisms of closure. However, I argue that the classification based on this indicator is heuristically fruitful. Systems employing different mechanisms of closure depend on different social, technical and epistemic prerequisites, have different strengths and weaknesses and are optimal for different epistemic tasks. My model puts a fact into the focus that has been neglected so far in social epistemology: the technical and its relationship to the social and the epistemic. Since most epistemic practices are nowadays pervaded by technologies, such a consideration of the role of technologies in these practices seems to be indispensable for any social epistemology that aims at being not only normatively appropriate, but also empirically adequate

Data science methods for the analysis of controversial social dedia discussions

Social media communities like Reddit and Twitter allow users to express their views on topics of their interest, and to engage with other users who may share or oppose these views. This can lead to productive discussions towards a consensus, or to contended debates, where disagreements frequently arise. Prior work on such settings has primarily focused on identifying notable instances of antisocial behavior such as hate-speech and “trolling”, which represent possible threats to the health of a community. These, however, are exceptionally severe phenomena, and do not encompass controversies stemming from user debates, differences of opinions, and off-topic content, all of which can naturally come up in a discussion without going so far as to compromise its development. This dissertation proposes a framework for the systematic analysis of social media discussions that take place in the presence of controversial themes, disagreements, and mixed opinions from participating users. For this, we develop a feature-based model to describe key elements of a discussion, such as its salient topics, the level of activity from users, the sentiments it expresses, and the user feedback it receives. Initially, we build our feature model to characterize adversarial discussions surrounding political campaigns on Twitter, with a focus on the factual and sentimental nature of their topics and the role played by different users involved. We then extend our approach to Reddit discussions, leveraging community feedback signals to define a new notion of controversy and to highlight conversational archetypes that arise from frequent and interesting interaction patterns. We use our feature model to build logistic regression classifiers that can predict future instances of controversy in Reddit communities centered on politics, world news, sports, and personal relationships. Finally, our model also provides the basis for a comparison of different communities in the health domain, where topics and activity vary considerably despite their shared overall focus. In each of these cases, our framework provides insight into how user behavior can shape a community’s individual definition of controversy and its overall identity.Social-Media Communities wie Reddit und Twitter ermöglichen es Nutzern, ihre Ansichten zu eigenen Themen zu äußern und mit anderen Nutzern in Kontakt zu treten, die diese Ansichten teilen oder ablehnen. Dies kann zu produktiven Diskussionen mit einer Konsensbildung führen oder zu strittigen Auseinandersetzungen über auftretende Meinungsverschiedenheiten. Frühere Arbeiten zu diesem Komplex konzentrierten sich in erster Linie darauf, besondere Fälle von asozialem Verhalten wie Hassrede und "Trolling" zu identifizieren, da diese eine Gefahr für die Gesprächskultur und den Wert einer Community darstellen. Die sind jedoch außergewöhnlich schwerwiegende Phänomene, die keinesfalls bei jeder Kontroverse auftreten die sich aus einfachen Diskussionen, Meinungsverschiedenheiten und themenfremden Inhalten ergeben. All diese Reibungspunkte können auch ganz natürlich in einer Diskussion auftauchen, ohne dass diese gleich den ganzen Gesprächsverlauf gefährden. Diese Dissertation stellt ein Framework für die systematische Analyse von Social-Media Diskussionen vor, die vornehmlich von kontroversen Themen, strittigen Standpunkten und Meinungsverschiedenheiten der teilnehmenden Nutzer geprägt sind. Dazu entwickeln wir ein Feature-Modell, um Schlüsselelemente einer Diskussion zu beschreiben. Dazu zählen der Aktivitätsgrad der Benutzer, die Wichtigkeit der einzelnen Aspekte, die Stimmung, die sie ausdrückt, und das Benutzerfeedback. Zunächst bauen wir unser Feature-Modell so auf, um bei Diskussionen gegensätzlicher politischer Kampagnen auf Twitter die oben genannten Schlüsselelemente zu bestimmen. Der Schwerpunkt liegt dabei auf den sachlichen und emotionalen Aspekten der Themen im Bezug auf die Rollen verschiedener Nutzer. Anschließend erweitern wir unseren Ansatz auf Reddit-Diskussionen und nutzen das Community-Feedback, um einen neuen Begriff der Kontroverse zu definieren und Konversationsarchetypen hervorzuheben, die sich aus Interaktionsmustern ergeben. Wir nutzen unser Feature-Modell, um ein Logistischer Regression Verfahren zu entwickeln, das zukünftige Kontroversen in Reddit-Communities in den Themenbereichen Politik, Weltnachrichten, Sport und persönliche Beziehungen vorhersagen kann. Schlussendlich bietet unser Modell auch die Grundlage für eine Vergleichbarkeit verschiedener Communities im Gesundheitsbereich, auch wenn dort die Themen und die Nutzeraktivität, trotz des gemeinsamen Gesamtfokus, erheblich variieren. In jedem der genannten Themenbereiche gibt unser Framework Erkenntnisgewinne, wie das Verhalten der Nutzer die spezifisch Definition von Kontroversen der Community prägt

Debating the European Union transnationally:Wikipedians’ construction of the EU on a Wikipedia talk page (2001-2015)

This thesis deals with the construction of the European Union (EU) as negotiated among contributors to the English Wikipedia between 2001 and 2015. It focuses on the Talk Page (TP) which accompanies the Wikipedia article on the EU and provides a space for Wikipedia contributors to discuss controversial issues regarding the article. The EU has received considerable attention in Critical Discourse Studies (CDS), addressing e.g. questions regarding language policy and discourses surrounding topics connected to the EU (e.g. Muntigl, Weiss, & Wodak, 2000; Unger, Krzyżanowski, & Wodak, 2014; Wodak, 2007a). However, private individuals’ attempts to make sense of the EU when facing the task of defining it have hardly been touched upon. In this context, Wikipedia constitutes an ideal repository of data as it has recorded debates on the institution since 2001. Taking a corpus-assisted approach (cf. Baker, 2006), I examine how contributors from various backgrounds have grappled with their understanding of the EU. Additionally, this study explores aspects of Wikipedia since this collaboratively created encyclopaedia has received little research attention. Taking the EU on Wikipedia as a starting point, this thesis presents a foray into how Wikipedia can be approached from a CDS perspective. That is, on the one hand, it identifies central aspects of this website’s structure and addresses policies that guide Wikipedia operations and thus shape Wikipedia data. On the other hand, it examines the site’s societal impact/relevance and evaluates to what extent it can function as a transnational public sphere.Findings suggest that a substantial part of discussions amongst Wikipedians addresses the classification of the EU along the continuum between confederation and unified country, depending on different views concerning member states’ sovereignty. Wikipedia’s policies and the nature of the debates further suggest that the TP can, to some extent, serve as a transnational public sphere