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The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80% in comparison to state-of-the-art approaches.
Watch our new MOOC in German about hate and fake in the Internet ("Trolle, Hass und Fake-News: Wie können wir das Internet retten?") on openHPI (link).
Our work on Measuring and Comparing Dimensionality Reduction Algorithms for Robust Visualisation of Dynamic Text Collections will be presented at CHIIR 2021.
I added some photos from my trip to Hildesheim.
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