Instagram Web Scrape
Instagram is a social media platform that has, “…become the home for visual storytelling” (Instagram 2016). Captioned images form the basis of Instagram with users having the capacity to upload and share images both privately and publicly. Users also have the opportunity to scroll through streams of photos which are filled with other peoples’ stories. This stream may be their home feed, which features image posted recently by people they have opted to follow, or it may be a stream that they have refined through conducting a specific search.
Instagram, much like Twitter, has become renowned for its use of the hashtag (#). They are a useful form of metadata, allowing people to search for related images. In a way they allow for communal commentaries on various topics. They are also a unique way for people to inject extra personality or meaning into their post. For example, people may use hashtags in a witty way such as renter Ross Bernhardt (2016) who celebrated a photo of his new and functional toilet with the following hashtags—#renterslife, #gameoftoilets and #porcelainthrone (a cheeky reference to Game of Thrones). Another feature of Instagram is that it tailors trending images related to a person’s interests based on their user history which includes images they have ‘liked’.
Bernhardt, R. 2016, I never thought I’d be happy to see a toilet, but finally my apartment has a new toilet, Instagram, viewed 5 September 2016, <https://www.instagram.com/p/BI8LP-UjefO/>.
Instagram 2016, About Us, viewed 6 September 2016, <https://www.instagram.com/about/us/>.
To conduct a web scrape of Instagram I filtered images related to the issue of housing affordability by entering single hashtags into the search bar. After much experimenting, I solidified which hashtags presented me with the most useful and relevant imagery. For example, I found that searching for #renters returned a lot of spam and unrelated results. Searching for #renterslife however, focused much more on content shared by people in rental properties.
The purpose of my web scrape was not to gather quantitative data, rather to collect qualitative and poetic data.
I wanted to find out the experiences, opinions and attitudes of people from the positions I have a particular interest in amidst the issue of housing affordability i.e. first home buyers, renters and people in public housing. I also wanted to see what people were sharing visually about the housing crisis in general. Although I have had a focus on Australia throughout my other posts, I found it fascinating and very insightful to conduct this web scrape on a global scale. Doing so allows for comparisons to be drawn and engaging discussions into topics. For example, seeing the photos of the overwhelmingly high density public housing buildings in Asia urges me to speculate what the future of Australia housing might look like.
For the colour analysis, I provided swatches of two dominant colours from each image. I wasn’t surprised that the #firsthome yielded warm, bright and cheerful colours however I wasn’t expecting the #housingcrisis palette to be as bright and intense as it was. I have assumed that this is because people want to share striking and powerful images that grab peoples’ attention and speak loudly on the issue. Overall the #publichousing palette was very dark and dull, communicative of disrepair and unfavourable living conditions.
There were many fascinating things common throughout each category. Doors, lawns, renovation work, pets and smiles frequented the images shared by first home buyers. Toilets and broken fixtures were often used to represent #renterslife. High-rises, graffiti and derelict landscapes summed up #publichousing. Finally, protests, street art and images of the homeless were common when searching #housingcrisis. Understandably, #firsthome was generally associated with celebratory sentiments whilst #renterslife was overrun with complaints from frustrated tenants. Both #publichousing and #housingcrisis were negatively associated with themes of displacement and inequality.
I was heavily intrigued by the image shared by Duncan (2016) which was found using #housingcrisis.
Taken locally in Sydney’s North Shore, this is a satirical image that highlights the disparity between the wealthy and poor. A man sits on a balcony sipping on wine and the caption reads, “Beatrice! get those pesky kids off our negatively geared lawn!”
I appreciated the sarcasm and light-hearted humour as I feel that it is part of the quintessential ‘Aussie’ attitude.
In terms of possible design responses, I thoroughly enjoyed reading the lists of hashtags that accompanied each image in addition to the one used to find it. Some carry serious sentiments whilst others are very playful. Seeing how users draw links from their image to other topics or comments is really fascinating. It is also interesting to note the hashtags that become trends and others that are seldom used. Some of my favourite hastags that I came across included #soakingwetclothesaretheworst, #broken, #dogshit, #useless, #gameoftoilets, #inconsiderateneighbors, #landlordproblems, #onepaycheckaway and #timeforapuppy which was followed by #orbabies. It would be very interesting to thus create a visual response or visualisation of the relationships between hashtags, how often they are said, who uses them and how they may be interpreted contextually. It feels like trending hashtags carry with them a sense of community i.e. the collective motives of its users. My initial thoughts as to how this could be visualised, are as a #directory (hashtag directory) or as a data visualisation poster.
Somewhat of a Twitter Web Scrape
Prior to conducting my Instagram web scrape, I scraped Twitter (2016) for data using the advanced search function. I wanted to find out what individuals not associated with the media or positions of authority such as the Government, were saying about the issue of housing affordability. In order to find broad and non-biased results I searched for ‘housing’ near Sydney. That is, I didn’t associate any negative or positive terms with it which would skew the results. After spending a while perusing the results I decided to scrape Instagram instead because I wasn’t feeling terrible inspired or excited by the content I was reading. I have however, included some of the more fascinating tweets that I came across in order to not let my initial efforts go to complete waste.
Twitter 2016, housing -http near:”Sydney, New South Wales” within:15mi, advanced search, viewed 3 September 2016, <https://twitter.com/search?f=tweets&vertical=default&q=housing%20-http%20near%3A%22Sydney%2C%20New%20South%20Wales%22%20within%3A15mi&src=typd>.