I'm from the Lisbon hub, and each year we define a topic to work on.
Signal's platform offers a machine learning-powered topic classification system that lets users define a topic they're interesting in and then train the algorithm on what should and shouldn't be in that topic.
If we had a precise philosophical account of what it is for a statement to be about a particular object or subject matter, then we could define a topic-neutral statement as one that is not about anything or, perhaps, one that is about everything indifferently.
I.F.: Our working group defined a few topics that are very important for young scientists, such as mechanisms of support and access to mentoring, the issue of productivity, international mobility, and gender equality.
Based on our previous work on automatic extraction of interpretable topics from online discourse (Zhang et al. 2012), we define a hot topic as HT = {focal: facet 1, facet 2, …, facet n }, where HT represents a hot topic.
Following works in hot topic detection in online communities (Zhang & Li 2011), a "hot topic" is defined as a topic that is discussed frequently during a period of time.
In this area of research, a sensitive topic is defined as a topic that "seems to be threatening in some way to those being studied" [ 20].
A decision was defined as a topic which was (a) discussed, with the result that (b) either changes were made or there was agreement that no changes should be made.
Once we had established the key themes, including topics defined a priori from the research aims and issues raised by the interviewees, we thematically indexed and "charted" the transcripts and notes thus allowing comments related to each theme to be grouped and sorted.
In addition, for each topic they defined a set of objectives, and translated the relevant information and evidence from literature into educational material based on the principles of problem-based learning.
