Fake news is increasingly an issue on social media platforms. In this work, rather than detect misinformation, we propose the use of nudges to help steer internet users into fact checking the news they read online. We discuss two types of nudging strategies, by presentation and by information. We present the tool BalancedView, a proof-of-concept that shows news stories relevant to a tweet. The method presents the user with a selection of articles from a range of reputable news sources providing alternative opinions from the whole political spectrum, with these alternative articles identified as matching the original one by a combination of natural language processing and search. The results of an initial user study of BalancedView suggest that nudging by information may change the behavior of users towards that of informed news readers.

Fallacies like the personal attack-also known as the ad hominem attack-are introduced in debates as an easy win, even though they provide no rhetorical contribution. Although their importance in argumentation mining is acknowledged, automated mining and analysis is still lacking. We show TF-IDF approaches are insufficient to detect the ad hominem attack. Therefore we present a machine learning approach for information extraction, which has a recall of 80% for a social media data source. We also demonstrate our approach with an application that uses online learning.

The Diversity Checker is a tool that aims to make it easier for journalists to author their texts with diversity in mind. To provide helpful hints for them in this respect, it is necessary to define how to quantify diversity so that this can be programmed into the tool. At this early stage in the development of the tool, we present a twofold contribution. First, we offer an analysis on what we mean by "improving diversity". Second, we present the first version of the Diversity Checker, along with some analysis of its current performance.