Social media have become a central part of the users online experience. Users connect with each other, share and find content, and disseminate information through social media sites (e.g., Facebook, Twitter, Flickr, YouTube). The content of social networks highly reflects user’s emotional states, and understanding how users browse through the social content is important for several reasons, e.g., it would allow better interface design of existing systems, advertisement placement policies, viral marketing etc. Despite the potential benefits, little is known about social network workloads. Most of the studies so far has considered the historical data (messages, third party applications, click through data). These studies couldn't provide conclusive results since the historical click data doesn't signify the users attention and emotional states during social media browsing. Hence our goal is to study the users intention and analyze the browsing behavior of users in more interactive settings. In this thesis, you would characterize the attention of users using their eye movement and mental workload.
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