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Clickstream data, which come with a massive volume generated by humanactivities on websites, have become a prominent feature for identifyingreaders' characteristics by newsrooms after the digitization of news outlets.Although the nature of clickstream data has a similar logic within websites, ithas inherent limitations in recognizing human behaviours when looking from abroad perspective, which brings the need to limit the problem in niche areas.This study investigates the anonymized readers' click activities on theorganizations' websites to identify news consumption patterns followingreferrals from Twitter,who incidentally reach but propensity is mainly routednews content. Methodologies for ensemble cluster analysis with mixed-typeembedding strategies are applied and compared to find similar reader groups andinterests independent of time. Various internal validation perspectives areused to determine the optimality of the quality of clusters, where the CalinskiHarabasz Index (CHI) is found to give a generalizable result. Our findingsdemonstrate that clustering a mixed-type dataset approaches the optimalinternal validation scores, which we define to discriminate the clusters andalgorithms considering applied strategies when embedded by Uniform ManifoldApproximation and Projection (UMAP) and using a consensus function as a key toaccess the most applicable hyperparameter configurations in the given ensemblerather than using consensus function results directly. Evaluation of theresulting clusters highlights specific clusters repeatedly present in theseparated monthly samples by Adjusted Mutual Information scores greater than0.5, which provide insights to the news organizations and overcome thedegradation of the modeling behaviours due to the change in the interest overtime.

Unsupervised Behaviour Analysis of News Consumption in Turkish Media