How users attend to online comments: an eye-tracking approach

This thesis focuses on how users attend to online comments based on an eye-tracking approach.

Completed Master Thesis

User comments are one of the most popular form of communication on social me-dia. They help to build connection between content creator and content consumers, as well as the connection between users of a social platform, which makes them highly relevant for community interaction. However, researchers have not treated users’ comment reading strategies in much detail. We aim to investigate the phe-nomena of users’ reading behaviour on the simplified YouTube interface by means of eye-tracking based attention analysis. The additional application of the research is to build a predictive model that can determine the attention probability of com-ments. This master thesis concentrates on analyzing users’ attention mechanisms and reading behaviour and finding a correlation between the comment features (i.e. length, language, sparseness, profile picture, comment position, etc.) and number of likes, which cannot be exhibited from a pure textual point of view.

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