"Im Kampf gegen Fake News" - der Einfluss von Risikobereitschaft, erkenntnistheoretischem Risiko und Glaubwürdigkeit der Quelle auf die Wirkung von Richtigstellungen

Julian Sparrer
2018 unpublished
The topic of this Master's thesis is how to battle fake news through corrections. Therefore it tries to categorize and define the excessively used term fake news in a first step. Furthermore, fake news are categorized epistemologically to illustrate the dilemma we face when receiving news or fake news: summarized, if there are accessible scales for us to rate news a priori, and if so, when it is justified to believe news and when it is not. Particularly important graduation models and
more » ... to personal risk taking and epistemological risk, which to some extent characterizes the involvement with which we attend to a topic, are outlined in further consequence and eventually established in the research questions. Credibility of the communicator is argued to be another fundamental concept for rating news, which becomes a welcome heuristic nowadays due to information overload and the speed of social media. The impact of credibility of the communicator is therefore examined as well in the experimental design, which features one control group and two treatment groups. The control group received a false report, as did the two treatment groups, but the treatment groups also received a correction of a credible (treatment group 1) respectively an uncredible (treatment group 2) source afterwards. This design was able to determine effects of the correction in general, the credibility of the source and moderation effects trough personal risk taking and epistemological risk. However, there were no significant effects found for either of the hypotheses. The results are limited to a few tendencies and possible prospects for future research in the field of fake news, which should focus on motives and brain activities and furthermore try to model sceneries of the distribution of fake news more accurately.
doi:10.25365/thesis.52622 fatcat:q5q5tsfa2nha3byjzma5uoi5bm