Keywords
Human factors, social media, human faces, information retrieval, recall, user interface, crisis response, extreme environments, human computer interaction, twitter, information management, human information processing, memory encoding, human face recognition
Abstract
As the amount of computer mediated information (e.g., emails, documents, multi-media) we need to process grows, our need to rapidly sort, organize and store electronic information likewise increases. In order to store information effectively, we must find ways to sort through it and organize it in a manner that facilitates efficient retrieval. The instantaneous and emergent nature of communications across networks like Twitter makes them suitable for discussing events (e.g., natural disasters) that are amorphous and prone to rapid changes. It can be difficult for an individual human to filter through and organize the large amounts of information that can pass through these types of social networks when events are unfolding rapidly. A common feature of social networks is the images (e.g., human faces, inanimate objects) that are often used by those who send messages across these networks. Humans have a particularly strong ability to recognize and differentiate between human Faces. This effect may also extend to recalling information associated with each human Face. This study investigated the difference between human Face images, non-human Face images and alphanumeric labels as retrieval cues under different levels of Task Load. Participants were required to recall key pieces of event information as they emerged from a Twitter-style message feed during a simulated natural disaster. A counter-balanced within-subjects design was used for this experiment. Participants were exposed to low, medium and high Task Load while responding to five different types of recall cues: (1) Nickname, (2) Non-Face, (3) Non-Face & Nickname, (4) Face and (5) Face & Nickname. The task required participants to organize information regarding emergencies (e.g., car accidents) from a Twitter-style message feed. The messages reported various events such as fires occurring around a fictional city. Each message was associated with a different recall cue type, depending on the experimental condition. Following the task, participants were asked to recall the information associated with one of the cues they worked with during the task. Results indicate that under medium and high Task Load, both Non-Face and Face retrieval cues increased recall performance over Nickname alone with Non-Faces resulting in the highest mean recall scores. When comparing medium to high Task Load: Face & Nickname and Non-Face significantly outperformed the Face condition. The performance in Non-Face & Nickname was significantly better than Face & Nickname. No significant difference was found between Non-Faces and Non-Faces & Nickname. Subjective Task Load scores indicate that participants experienced lower mental workload when using Non-Face cues than using Nickname or Face cues. Generally, these results indicate that under medium and high Task Load levels, images outperformed alphanumeric nicknames, Non-Face images outperformed Face images, and combining alphanumeric nicknames with images may have offered a significant performance advantage only when the image is that of a Face. Both theoretical and practical design implications are provided from these findings.
Notes
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu
Graduation Date
2014
Semester
Summer
Advisor
Karwowski, Waldemar
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Industrial Engineering and Management Systems
Degree Program
Industrial Engineering
Format
application/pdf
Identifier
CFE0005318
URL
http://purl.fcla.edu/fcla/etd/CFE0005318
Language
English
Release Date
August 2014
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
Subjects
Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic
STARS Citation
Costello, Anthony, "Facilitating Information Retrieval in Social Media User Interfaces" (2014). Electronic Theses and Dissertations. 4558.
https://stars.library.ucf.edu/etd/4558