How users attend to online comments: 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.

University Library Record

Liuzniak, A. (2022). How users attend to online comments: an eye-tracking approach [Master’s Thesis]. https://doi.org/10.18419/OPUS-11979
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BibTeX
@mastersthesis{https://doi.org/10.18419/opus-11979, abstract = {The modern world is highly influenced by user-contributed content such as user comments, which is one of the most popular forms of communication on social media. They help build a connection between content creators and content consumers, as well as a 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. The existing accounts are limited to address solely an explicit comment reading approach, which is only able to track an active interaction, e.g. analyze attention by counting a number of clicks. This technique can not fully estimate the drawn attention, as 73% of people do not actively interact with comments. Eye-tracking plays a vital role in solving the task of analysing implicit attention, as it allows to estimate such crucial characteristics, as comment features, which drew the most attention, the amount of attention given or the order of seeing specific comments. The current research uses eye-tracking based attention analysis for investigating the phenomena of users' reading behaviour on the real YouTube interface. The present master thesis concentrates on analysing users' attention mechanisms and reading behaviour and finding a correlation between the comment features such as length, language, sparseness, comment position, number of likes, presence of replies, presence of video creator's like and authorised user label, which cannot be exhibited from a pure textual point of view. This work shows that number of likes and presence of answers contribute the most to the attention drawn by comments. The analysis revealed that the category of a video deeply influences the emotion and length of the popular comments: if people are looking for useful information, which applies to educational videos, they tend to pay attention to neutral and long comments, whereas while looking for entertainment, people most probably will notice short and positive comments. The two important findings from the gender analysis are that women tend to read longer comments, but skip a higher percentage of comments than men. It is hoped that this research will contribute to a deeper understanding of the features that draw the most attention, which can be exploited in content generation strategies and developing new ranking algorithms.}, author = {Liuzniak, Alona}, copyright = {info:eu-repo/semantics/openAccess}, doi = {10.18419/OPUS-11979}, institution = {Institute for Parallel and Distributed Systems}, language = {en}, school = {Department of Analytical Computing, Institute for Parallel and Distributed Systems, Universität Stuttgart, }, title = {How users attend to online comments: an eye-tracking approach}, type = {Master's Thesis}, url = {http://elib.uni-stuttgart.de/handle/11682/11996}, year = 2022 }
Link
http://elib.uni-stuttgart.de/handle/11682/11996

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Dr. Chandan Kumar

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Completed Master Thesis

University Library Record

BibTeX

Link

Supervisors

Dr. Chandan Kumar

Prof. Dr. Steffen Staab

Audience

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Organization

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

Completed Master Thesis

University Library Record

BibTeX

Link

Supervisors

Dr. Chandan Kumar

Prof. Dr. Steffen Staab

Here you can reach us

Audience

Formalities

Services

Organization