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Analyzing User Attention in Social Media Browsing

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. Eye tracking devices would help you capture user’s point of interest through gaze control. Moreover you could employ BCI (brain computer interface) to compliment the gaze-based signals. You would explore cognitive state using reactive and passive BCI, which could provide different mind states of cognitive load, switching attention, event noticed, surprised, committed error during the eye-based interaction with the social application. For the purpose, we would provide you state of the art interactive devices e.g. eyetracker and brain computer interface with SDK

References:

1.    Eye tracking SMI http://www.smivision.com/en/gaze-and-eye-tracking-systems/products/red250mobile.html

2.    Emotive BCI https://emotiv.com/product-specs/Emotiv%20EPOC%20Specifications%202014.pdf 

3.    A. Nazir, S. Raza, and C.-N. Chuah. Unveiling
Facebook: a measurement study of social network
based applications. In ACM IMC, 2008.

4.    H. Chun, H. Kwak, Y.-H. Eom, Y.-Y. Ahn, S. Moon, and H. Jeong. Online social networks: Sheer volume vs social interaction: a case study of Cyworld. In ACM IMC, 2008.

5.    B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in Facebook. In ACM SIGCOMM WOSN, 2009.

6.    C. Wilson, B. Boe, A. Sala, K. P. N. Puttaswamy, and B. Y. Zhao. User interactions in social networks and their implications. In ACM EuroSys, 2009.

Studienart: 
master
Ausschreibungsdatum: 
2017