User attentional analyses on web elements help in synthesis and rendering of webpages. However, majority of the existing analyses are limited in incorporating the intrinsic visual features of text and images. This study aimed to analyze the influence of elements’ visual features (font-size, font-family, color, etc., for text; and brightness, color, intensity, etc., for images) besides their position on users’ free-viewing visual attention. The investigation includes: (i) user’s position-based attention allocation on text and image web elements, (ii) identification of informative visual features with respect to the attention, (iii) performance of informative visual features in predicting the ordinal visual attention (fixation-indices). Towards the study, an eye-tracking experiment was conducted with 42 participants on 36 real-world webpages. The analyses revealed: (i) Though users predominantly allocate the initial attention to MiddleCenter}, MiddleLeft, TopCenter, TopLeft regions, the elements in Right and Bottom regions are not completely ignored; (ii) Space -related (column-gap, line-height, padding) and font Size -related (font-size, font-weight) intrinsic text features, and Mid-level Color Histogram intrinsic image features are informative, while position and size are informative for both the types; (iii) the informative visual features predict the ordinal visual attention on an element with 90% average accuracy and 70% micro-F1 score. Our approach finds applications in element-granular web-designing and user attention prediction.

中文翻译：

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调查和建模 Web 元素的视觉特征对自由查看注意力的影响

对网页元素的用户注意力分析有助于网页的合成和渲染。然而，大多数现有的分析在结合文本和图像的内在视觉特征方面受到限制。本研究旨在分析元素的视觉特征（文本的字体大小、字体系列、颜色等；图像的亮度、颜色、强度等）以及它们的位置对用户自由度的影响。观看视觉注意力。调查包括：（i）用户在文本和图像网络元素上的基于位置的注意力分配，（ii）关于注意力的信息视觉特征的识别，（iii）信息视觉特征在预测顺序视觉注意（固定指数）。在这项研究中，42 名参与者在 36 个真实世界的网页上进行了眼动追踪实验。分析显示：（i）虽然用户主要将初始注意力分配到 MiddleCenter}、MiddleLeft、TopCenter、TopLeft 区域，但 Right 和 Bottom 区域的元素并没有被完全忽略；(二)空间- 相关（列间距、行高、填充）和字体尺寸-相关（字体大小，字体粗细）内在文本特征，以及中级颜色直方图内在图像特征是信息性的，而位置和大小对于这两种类型都是信息性的；(iii) 信息丰富的视觉特征以 90% 的平均准确率和 70% 的 micro-F1 分数预测元素上的顺序视觉注意力。我们的方法在元素粒度网页设计和用户注意力预测中找到了应用。