The aim of study is to examine the applicability of image segmentation- identification of objects/regions by partitioning images- to examine online social photography. We argue that the need for a meaning-independent reading of online social photography within social markers, such as hashtags, arises due to two characteristics of social photography: 1) internal incongruence resulting from user-driven construction, and 2) variability of content in terms of visual attributes, such as colour combinations, brightness, and details in backgrounds. We suggest visual affluence- plenitude of visual stimuli, such as objects and surfaces containing a variety of colour regions, present in visual imagery- as a basis for classifying visual content and image segmentation as a technique to measure affluence. We demonstrate that images that contain objects with complex texture and background patterns are more affluent while images that include blurry backgrounds are less affluent than others. Moreover, images that contain letters and dark, single-colour backgrounds are less affluent than images that include subtle shades. Mann-Whitney U test results for nine pairs of hashtags showed that seven out of nine pairs had significant differences in visual affluence. The proposed measure can be used to encourage a 'visually-oriented' turn in online social photography research that can benefit from hybrid methods that can extrapolate micro-level findings to macro-level effects.
|Number of pages||24|
|Journal||Social Media and Society|
|Publication status||Accepted/In press - 1 Aug 2019|
- image segmentation
- visual affluence
Rathnayake, C., & Ntalla, I. (Accepted/In press). 'Visual affluence' in social photography: applicability of image segmentation as a visually-oriented approach to study Instagram hashtags. Social Media and Society, 1-24.