Community based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities.
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.