An essential part of the social media ecosystem is user-generated comments. However, not all comments are useful to all people as both authors of comments and readers have different intentions and perspectives. Consequently, the development of automated approaches for the ranking of comments and the optimization of viewers' interaction experiences are becoming increasingly important. This work proposes an adaptive faceted ranking framework which enriches comments along multiple semantic facets (e.g., subjectivity, informativeness, and topics), thus enabling users to explore different facets and select combinations of facets in order to extract and rank comments that match their interests. A prototype implementation of the framework has been developed which allows us to evaluate different ranking strategies of the proposed framework. We find that adaptive faceted ranking shows significant improvements over prevalent ranking methods which are utilized by many platforms such as YouTube or The Economist. We observe substantial improvements in user experience when enriching each element of a comment along multiple explicit semantic facets rather than in a single topic or subjective facets.

This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e., privacy, and the network costs will also be removed.

Online data journalism, including visualizations and other manifestations of data stories, has seen a recent surge of interest. User comments add a dynamic, social layer to interpretation, enabling users to learn from others' observations and social interact around news issues. We present the results of a qualitative study of commenting around visualizations published on a mainstream news outlet, The Economist's Graphic Detail blog. We find that surprisingly, only 42% of the comments discuss the visualization and/or article content. Over 60% of comments discuss matters of context, including how the issue is framed and the relation to outside data. Further, over one third of total comments provide direct critical feedback on the content of presented visualizations and text articles as well as on contextual aspects of the presentation. Our findings suggest using critical social feedback from comments in the design process, and motivate the development of more sophisticated commenting interfaces that distinguish comments by reference.