Creation and evaluation of timelines for longitudinal user posts
This work addresses a specific need in social media analysis for researchers and practitioners by providing incremental improvements in timeline segmentation and evaluation.
The authors tackled the problem of segmenting longitudinal user posts into meaningful timelines to improve manual annotation quality and cost, proposing methods for identifying interesting behavioral changes and a novel evaluation framework applied to two social media datasets.
There is increasing interest to work with user generated content in social media, especially textual posts over time. Currently there is no consistent way of segmenting user posts into timelines in a meaningful way that improves the quality and cost of manual annotation. Here we propose a set of methods for segmenting longitudinal user posts into timelines likely to contain interesting moments of change in a user's behaviour, based on their online posting activity. We also propose a novel framework for evaluating timelines and show its applicability in the context of two different social media datasets. Finally, we present a discussion of the linguistic content of highly ranked timelines.