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An Example of Our Services Leading to Simple Insights

Nine college-age individuals viewed the University of Tampa's TikTok page for five minutes. They were told to explore and find something interesting. Below is visual scanning data and emotional responses to the first four rows of videos.

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Visual Scanning Metrics

Here, we have drawn areas of interest (AOIs) around each row. This allows us to get a number of measures including how common fixations (blue dots from the first example) where in each row. As the table on the right of the image shows, more attention was given to the first row. Average fixation count for row 1 was 22.8, followed by row 3 with an average of 13 fixations. Notice that on several AOI metrics row 3 received noticeably more attention than row 2; suggesting that there is either something about the spacing that guides viewers to skip row 2, or that row 2 did not have as many eye catching videos as row 3.

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Emotional Heat Map

Here, we combined viewers visual scanning behaviors with measures of their facial expression. While this tool can provide more detailed information, this example is a simple dichotomy of whether positive or negative emotions where being reflected on viewers' faces while they looked at certain content. In this case, positive emotions were represented by blue highlights and negative by red highlights. Note that there is a clear dispaly of positive emotion when scanning the first row and a subtle reflection of negative emotion when scanning the bottom right corner set of videos. This display likely reflects a bit of joy when first opening the page and seeing the line of videos, and then the joy fades to something neutral/slightly negative after the novelty is lost. However, the light negative highlights are less about the content and more about the viewers getting settled into the task.

Access Users'
Implicit Perceptions

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