<p> Information visualization offers a unique method to assist users in understanding large quantities of data, such as that which is found in social media. The recent surge in the use of social media platforms, the abundance of data generated, and the implications about what this data means has made it increasingly necessary to provide feedback to these users about what they and others are presenting online. Thus, it is critical for these individuals to access this information and gain some level of visual understanding regarding their own identities or that of a particular group. This dissertation is organized in the format of a three-paper dissertation. Chapter 1 is the introduction for the subsequent three chapters and provides background on information visualization and identity presentation in social media, while exploring theoretical approaches to visual perception and design. Chapter 2 demonstrates a variety of past and current multidimensional information visualization techniques that are relevant to social media data, as related to online identity presentation. The overview includes data portraits, motion-based visualization, music visualization, and textual structures. Chapter 3 introduces <i>CarrinaCongress</i>, an information visualization dashboard that affords users with the ability to compare two members of Congress in order to better understanding the elected officials’ tweets and external information. Chapter 4 presents <i> HadithViz</i>, a motion-based information visualization dashboard that borrows from video game interfaces and focuses on event-based tweets, as defined by hashtags related to sexism in the video gaming industry. Finally, Chapter 5 is the conclusion to this dissertation and will summarize the three individual studies, discuss limitations and implications, and provide recommendations that future work consist of simple, accessible visualizations that are based on existing visual languages and can be interpreted by a wide-ranging audience. </p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10174166 |
| Date | 01 November 2016 |
| Creators | Mahmud, Athir |
| Publisher | Illinois Institute of Technology |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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