Online Library

We have collected over 30M messages from the publicly available /pol/ message boards on 4chan and 8chan, and compiled them into a model of toxic language use. The trained word embeddings (±0.4GB) are released for free and may be useful for further study on toxic discourse or to boost hate speech detection systems: textgain.com/8chan.

We present two studies of online ephemerality and anonymity based on the popular discussion board /b/ at 4chan.org: a website with over 7 million users that plays an influential role in Internet culture. Although researchers and practitioners often assume that user identity and data permanence are central tools in the design of online communities, we explore how /b/ succeeds despite being almost entirely anonymous and extremely ephemeral. We begin by describing /b/ and performing a content analysis that suggests the community is dominated by playful exchanges of images and links. Our first study uses a large dataset of more than five million posts to quantify ephemerality in /b/. We find that most threads spend just five seconds on the first page and less than five minutes on the site before expiring. Our second study is an analysis of identity signals on 4chan, finding that over 90% of posts are made by fully anonymous users, with other identity signals adopted and discarded at will. We describe alternative mechanisms that /b/ participants use to establish status and frame their interactions.

Die vorliegende Arbeit beschreibt den Zusammenhang zwischen den digitalen Erinnerungskulturen von ‚9/11‘ und der Mediengeschichte des World Wide Web im Zeitraum von 2001 bis 2016. Die darin angelegte Problemstellung behandelt das rekursive Verhältnis von Medialität und Historizität und lässt sich als Kombination von zwei Fragestellungen fassen: Welchen Einfluss hat der digitale Medienwandel – insbesondere in Form des World Wide Web – auf die Verarbeitung des Medienereignisses ‚9/11‘ in verschiedenen Erinnerungskulturen? Und welche Rolle spielen im Gegenzug das Medienereignis und die Erinnerungskultur von ‚9/11‘ für die Beobachtbarkeit des Web als historischer Medienkonstellation und für die Bedingungen der Internet- und Webhistoriographie? Bislang gibt es zu beiden Fragen lediglich punktuelle kulturwissenschaftliche Untersuchungen, die zwar einzelne Anwendungen oder bestimmte Zeitpunkte thematisieren, jedoch die strukturelle Koevolution von Medialität und Historizität nur unzureichend berücksichtigen. Die vorliegende Arbeit ist die erste medienhistoriographische Studie, die nicht nur einzelne webbasierte Erscheinungsformen des Erinnerungsortes ‚9/11‘ betrachtet, sondern ihre heterogenen Entwicklungslinien und transmedialen Verflechtungen genealogisch wie systematisch untersucht. Im Umkehrschluss besteht das Novum der Arbeit in der Ausarbeitung und Anwendung einer reflexiven Perspektive, die den Status von ‚9/11‘ als Element von webbasierten Gedächtnisformen und als Erkenntnisobjekt der Webgeschichte diskutiert.

Contestation over European integration has been widely studied in the rhetoric of parties, leaders, and movements on the far right in a variety of media. Focusing on Twitter use by far right actors in Western Europe, we apply corpus-aided discourse analysis to explore how imaginative geographies are used to politicize Europe among their digital publics. We find that the idea of a crisis of cultural identity pervades imaginaries of Europe amongst far right digital publics. While Europe is presented as facing a crisis of cultural identity, we find that the far right articulates an aspirational imaginary of Europe, the ‘Europe des Nations’ that rejects liberal-democratic pluralism in the EU and the ‘establishment’. We find that the contestation of Europe in far right digital publics relies on a crisis of cultural identity, representing a translation of Nouvelle Droite imaginaries of Europe into the social media space.

Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.