# Considered Factors of Online News Based on Respondents' Eye Activity Using Eye-Tracker Analysis
## 4 Results
### Content Interest Measurement  
Experiments were carried out in rooms with adjusted ergonomic conditions and meters at 293 lux. Experiments began by providing respondents a news website with a 50% ratio of text and image elements. News content was arranged in the same width, so the only difference was the Y coordinate for placement of text and news images on the screen. News was presented with content quoted from several official news websites in Indonesia. The news included several latest stories, namely the events of flight accidents, cases of conflict in Indonesia's UU ITE (Constitution of Information and Electronic Transaction), and post-earthquake conditions in Palu and Lombok. Selection of news aimed to give an impulse of anxiety towards the respondent and to also trigger the memory of the respondent of anxiety experienced in the past month in order to be able to fill out the BAI questionnaire and engage in the interview. Forty respondents were included in the experiment, which had no age or professional limitations. The 40 respondents consisted of 26 (65%) undergraduate students, 10 (25%) graduate students, and 4 (10%) employees. The tools used for data retrieval were Tobii Eye-Tracker, Tobii Gaze Overlay, Open Broadcaster Software (OBS) for recording screen, and a console program to record the X and Y coordinates. When respondents explored and observed news content, the eye gaze and eye movements were recorded into CSV files, and in this experiment, the AOI used was part of text and images on the news page. CSV data are shown in Table 5. Eye-tracking data captured with Tobii were visualized as a heat map, and the scanned path is shown in Figure 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Gender**} & Male & 25 & 62.5\% & Line Today & 12 & 27.9\% \\  & Female & 15 & 37.5\% & Detik & 7 & 16.3\% \\ \hline \multirow{4}{*}{**Age**} & \textless{}20 & 1 & 2.5\% & \multirow{4}{*}{**Sources**} & Kompas & 6 & 14.0\% \\  & 20–25 & 31 & 77.5\% & Instagram & 4 & 9.3\% \\  & 25–30 & 2 & 5.0\% & Other & 11 & 32.6\% \\ \cline{3-6}  & \textgreater{}40 & 6 & 15.0\% & \multirow{4}{*}{**Sews**} & Politics & 24 & 22.6\% \\ \cline{3-6}  & \textgreater{}30 & 35 & 87.5\% & \multirow{4}{*}{**Categories**} & Sci-Tech & 16 & 15.1\% \\ \cline{3-6}  & Outside & 5 & 12.5\% & Sports & 13 & 12.3\% \\ \hline \multirow{4}{*}{**Profession**} & Student & 31 & 77.5\% & Entertainment & 13 & 12.3\% \\  & Private & 2 & 5.0\% & Lifestyle & 12 & 11.3\% \\ \cline{3-6}  & Employees & & & Other & 28 & 26.4\% \\ \cline{3-6}  & Government & \multirow{4}{*}{**Media Quality**} & Text & 18 & 45.0\% \\ \cline{3-6}  & Employees & & & Images & 11 & 27.5\% \\ \cline{3-6}  & Businessman & 3 & 7.5\% & Video & 11 & 27.5\% \\ \cline{3-6}  & Other & 3 & 7.5\% & \multirow{4}{*}{**Media Type**} & Printed & 2 & 5.0\% \\ \cline{3-6}  & & & Digital & 38 & 95.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: demographics. Then, the raw data obtained were processed to measure content interest. Data classification was based on the following metrics:  
* amount of fixation on AOI images for each news article;
* amount of fixation on AOI text in each news article;
* comparison ratio of fixation of images and text in each news article;  
Figure 1: (**a**) Example of gaze plot; (**b**) Example of heat map. \begin{table}
\begin{tabular}{c c c} \hline \hline
**X** & **Y** & **Time** \\ \hline
585.2921 & 723.4481 & 13.53.36.3 \\
584.7035 & 723.6976 & 13.53.36.15 \\
584.8232 & 723.7401 & 13.53.36.25 \\
586.6572 & 723.4815 & 13.53.36.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Example of CSV eye-tracking data. * duration of fixation on AOI images in each news article;
* duration of fixation on AOI text in each news article;
* percentage of time spent on AOI images and news (total fixed duration (FD) on AOI / total FD on the webpage; data shown in Table 5.  
Then, the content interest data were processed to find correlation with data from the anxiety check process using the Beck Anxiety Inventory questionnaire.