  Clickstream data, which come with a massive volume generated by human
activities on websites, have become a prominent feature for identifying
readers' characteristics by newsrooms after the digitization of news outlets.
Although the nature of clickstream data has a similar logic within websites, it
has inherent limitations in recognizing human behaviours when looking from a
broad perspective, which brings the need to limit the problem in niche areas.
This study investigates the anonymized readers' click activities on the
organizations' websites to identify news consumption patterns following
referrals from Twitter,who incidentally reach but propensity is mainly routed
news content. Methodologies for ensemble cluster analysis with mixed-type
embedding strategies are applied and compared to find similar reader groups and
interests independent of time. Various internal validation perspectives are
used to determine the optimality of the quality of clusters, where the Calinski
Harabasz Index (CHI) is found to give a generalizable result. Our findings
demonstrate that clustering a mixed-type dataset approaches the optimal
internal validation scores, which we define to discriminate the clusters and
algorithms considering applied strategies when embedded by Uniform Manifold
Approximation and Projection (UMAP) and using a consensus function as a key to
access the most applicable hyperparameter configurations in the given ensemble
rather than using consensus function results directly. Evaluation of the
resulting clusters highlights specific clusters repeatedly present in the
separated monthly samples by Adjusted Mutual Information scores greater than
0.5, which provide insights to the news organizations and overcome the
degradation of the modeling behaviours due to the change in the interest over
time.
