Big Data, Fake News und Fremdbestimmung

Fake News in Social Networks

We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in acco...

Characterizing Political Fake News in Twitter by its Meta-Data

This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets contai...

The Economics of "Fake News"

Automatic Detection of Fake News

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online...

Referenzprojekte für den Informatikunterricht - Unterrichtsprojekte zwischen Selbst- und Fremdbestimmung

Der Kern des IT-Weiterbildungssystems sind die Referenzprojekte der APO-IT (Arbeitsprozessorientierten IT-Weiterbildung). Trotz ihrer Ausrichtung auf arbeitsbegleitende Prozesse von Berufstätigen lassen sich aus ihr zahlreiche Anregungen für den Schulunterricht und die Entwicklung zeitgemäßer Curricula ableiten. Referenzprojekte sind das Bindeglied zwischen dem selbstbestimmten und ergebnisorie...