Aesthetical Appeal and Dissemination of Architectural Heritage Photographs in Instagram
Abstract
:1. Introduction
- Section 2 deals with the literature background concerning the technical elements of the research, mainly focused on visual resources of building photos posted on Instagram. It includes the image social meaning generation in IBSN, the aesthetical appeal of pictures (referred to color and composition) and the dimension of human being in architecture.
- Section 3 covers the materials and methods, including the explanation of the non-experimental research design, to analyze the social perception of architectural heritage on Instagram, as well as how the aesthetic appeal of pictures influence their dissemination within the social network. It also describes the research variables and how they were categorized, comprising the Instagram user profile, the degree of dissemination, the typology of architectural heritage and the aesthetical appeal features of the picture.
- Section 4 addresses the results based on statistical analysis which mainly focus on two types of analyses. First, contingency tests offer the description of the message depending on the Instagram user profile. Second, non-parametric tests assess whether the aesthetical appeal features are related to the degree of dissemination of architectural heritage pictures.
- Section 5 provides a discussion on the findings in relation to the literature, and some practical actions are provided so government agents can promote the conservation of architectural heritage through image-based social networks. These proposals incorporate both the replication of the research method to diagnose the degree of visibility of a specific case and the application of the aesthetical appeal features.
2. Literature Background
2.1. Image Social Meaning Generation
2.2. Aesthetical Appeal: Color and Composition
2.3. Human Figure and Architecture
3. Materials and Methods
Variables
4. Results
4.1. Building Typology, Element Detail and User Account: Contingency Analysis
4.2. Normality and Variance Test
5. Discussion and Conclusions
- Public institutions dedicated to the conservation of architectural heritage should use image-based social networks such as Instagram in an educational way, since it is currently one of the most important social information carriers for youth. The change of platform involves adapting to how users experience it, rather than filling it with content. In this case, the use of own hasthtags and others from related communities should be encouraged to increase visibility towards networks with an interest in heritage that enhances social interaction.
- When promoting architectural heritage in image-based social networks, some aesthetic factors must be taken into account to produce images that can help reach a wider audience. The use of one vanishing point perspective is one of the most effective features due to the screen size where image-based social network runs, which entails positioning the point of interest within the device. However, other characteristics such as color and human dimension should not be a limitation in terms of its ability to spread. Instead, a criterion based on the feeling of closeness or cultural identity that they can convey should be considered, keeping in mind the objective of increasing population awareness towards architectural heritage.
- This research design is available to be replicated by governments and public institutions to diagnose to what extent its architectural heritage is visible in these media. Consequently, they should manage strategic plans to promote those elements with less visibility, without controlling the content generated by the population, but with the aim of complementing the information.
- This research can also serve as an example to trainers and teachers to educate students on how information can be biased in digital environments and how it can lead to the loss of an element as important as heritage.
Author Contributions
Funding
Conflicts of Interest
References
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Variable | Categories | Likes Test Results | Engagement Test Results | ||||
---|---|---|---|---|---|---|---|
Statistic | df | Sig. | Statistic | df | Sig. | ||
Human dimension | Presence | 0.347 | 29 | 0.000 | 0.210 | 29 | 0.002 |
Absence | 0.199 | 111 | 0.000 | 0.079 | 111 | 0.087 | |
Color | Greyscale | 0.194 | 13 | 0.195 | 0.186 | 22 | 0.046 |
Warm | 0.164 | 63 | 0.000 | 0.081 | 93 | 0.169 | |
Cold | 0.238 | 16 | 0.016 | 0.096 | 25 | 0.200 | |
Linear perspective | 1VPP | 0.206 | 38 | 0.000 | 0.086 | 57 | 0.200 |
2VPP | 0.227 | 41 | 0.000 | 0.159 | 63 | 0.000 | |
3VPP | 0.418 | 13 | 0.000 | 0.126 | 20 | 0.200 | |
Aesthetic quality | Low | 0.259 | 60 | 0.000 | 0.111 | 60 | 0.065 |
Medium | 0.292 | 60 | 0.000 | 0.125 | 60 | 0.020 | |
High | 0.261 | 20 | 0.001 | 0.183 | 20 | 0.078 |
Variable | Categories | n | Mean Rank | Mann–Whitney U | Kruskal–Wallis H | Sig. |
---|---|---|---|---|---|---|
Human dimension | Presence | 29 | 55.64 | 1178.500 | - | 0.027 |
Absence | 111 | 74.38 | ||||
Color | Greyscale | 22 | 45.59 | - | 10.325 | 0.006 |
Warm | 93 | 73.81 | ||||
Cold | 25 | 80.12 | ||||
Linear perspective | 1VPP | 57 | 86.25 | - | 15.000 | 0.001 |
2VPP | 63 | 61.44 | ||||
3VPP | 20 | 54.15 | ||||
Aesthetic quality | Low | 60 | 67.60 | - | 0.741 | 0.690 |
Medium | 60 | 73.86 | ||||
High | 20 | 69.13 |
Variable | (I) | (J) | Mean Difference (I–J) | Error Deviation | Sig. |
---|---|---|---|---|---|
Color | Grayscale | Warm | −53.607 | 28.452 | 0.147 |
Cold | −89.011 | 35.082 | 0.033 | ||
Linear perspective | 1VPP | 2VPP | 51.685 | 22.015 | 0.049 |
2VPP | 29.310 | 31.299 | 0.618 | ||
Aesthetical quality | Low | Medium | −20.267 | 22.354 | 0.637 |
High | −17.700 | 31.613 | 0.842 |
Variable | Categories | n | Mean Rank | Mann–Whitney U | Kruskal–Wallis H | Sig. |
---|---|---|---|---|---|---|
Human dimension | Presence | 29 | 54.81 | 1154.500 | - | 0.019 |
Absence | 111 | 74.60 | ||||
Color | Grayscale | 22 | 87.59 | - | 4.941 | 0.085 |
Warm | 93 | 66.24 | ||||
Cold | 25 | 71.30 | ||||
Linear perspective | 1VPP | 57 | 80.46 | - | 6.565 | 0.038 |
2VPP | 63 | 65.87 | ||||
3VPP | 20 | 56.73 | ||||
Aesthetic quality | Low | 60 | 63.54 | - | 3.157 | 0.206 |
Medium | 60 | 76.39 | ||||
High | 20 | 73.70 |
Variable | (I) | (J) | Mean Difference (I–J) | Error Deviation | Sig. |
---|---|---|---|---|---|
Linear perspective | 1VPP | 2VPP | 0.0264 | 0.0163 | 0.243 |
3VPP | 0.0522 | 0.0233 | 0.048 |
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López-Chao, V.; Lopez-Pena, V. Aesthetical Appeal and Dissemination of Architectural Heritage Photographs in Instagram. Buildings 2020, 10, 225. https://doi.org/10.3390/buildings10120225
López-Chao V, Lopez-Pena V. Aesthetical Appeal and Dissemination of Architectural Heritage Photographs in Instagram. Buildings. 2020; 10(12):225. https://doi.org/10.3390/buildings10120225
Chicago/Turabian StyleLópez-Chao, Vicente, and Vicente Lopez-Pena. 2020. "Aesthetical Appeal and Dissemination of Architectural Heritage Photographs in Instagram" Buildings 10, no. 12: 225. https://doi.org/10.3390/buildings10120225