Framing the research method.
Framed under an auto-ethnographic* research approach, an array of methods were utilised, allowing me to explore rising technology culture and human digital experiences effectively through my own downloadable data and reflections. Placing myself as a case study enables me to reach a wider audience in a more relatable and personal manner, creating a more significant impact.
*AUTOETHNOGRAPHY:
A method of research where the writer draws from personal, anecdotal experiences through self-reflection to explore social and cultural meanings.
A method of research where the writer draws from personal, anecdotal experiences through self-reflection to explore social and cultural meanings.
Determining the data I am working with.
The source of all my data is from Facebook—A staggering 19 GB worth of information accumulated over a decade was downloaded. This information can be easily downloaded under the format of .JSON* files, allowing for easy data mining and sorting as it can be sorted under certain categories (timestamp, person, content, etc.). With this information in hand, I conducted a suite of experiments using a variety of processes. Shown below is the structure of the downloaded data.
*.JSON (JavaScript Object Notation)
A text-based file type that is able to contain human-readable data that can also be effectively stored and transmitted by machines.
A text-based file type that is able to contain human-readable data that can also be effectively stored and transmitted by machines.
Both primary and secondary processes were involved.
To analyse and dissect this information, data collating, mining and visualisation were the primary processes utilised to explore previously concealed trends, patterns, or any unusual/new findings.
These processes were conducted through the use of code, specifically Python* as it is the most accessible language for data analysis. Libraries such as Textblob was also utilised which assisted in conducting sentiment analysis*.
In the context of this project, data mining includes processes such as:
With these primary processes used for the actual experimentation acting as stimuli, secondary methods were utilised as a response to these findings.
Such methods include user journey mapping, critical reflection through journaling, annotating, diagramming and user-testing to explore different methods of reflection and contemplation.
These processes were conducted through the use of code, specifically Python* as it is the most accessible language for data analysis. Libraries such as Textblob was also utilised which assisted in conducting sentiment analysis*.
In the context of this project, data mining includes processes such as:
- Determining the usage/frequency of certain words used
- Conducting sentiment analysis on words—whether certain messages are positive, neutral or negative in terms of emotions
- Visual analysis & mapping of screenshots/photos to uncover patterns
- Message activity frequency based on time of day
With these primary processes used for the actual experimentation acting as stimuli, secondary methods were utilised as a response to these findings.
Such methods include user journey mapping, critical reflection through journaling, annotating, diagramming and user-testing to explore different methods of reflection and contemplation.
*PYTHON:
A non-specific programming language that is typically used for website building, software development and data analysis.
*SENTIMENT ANALYSIS:
A method of analysis (often done with code) to identify the mood and emotive value behind a piece of writing/text.
A non-specific programming language that is typically used for website building, software development and data analysis.
*SENTIMENT ANALYSIS:
A method of analysis (often done with code) to identify the mood and emotive value behind a piece of writing/text.
Presenting the data.
As the main concept in all of these experiments involve change over time, each experiment is presented in chronological order from past to present over the 10 years of data.
Each experiment is presented across the web page with a code poem overlayed on top to create a multilayered user-experience where the viewer can read both the stimuli and response simultaneously. Through this method of recontextualising by presenting 10 years of data in one sitting, a new perspective of time is formed.
Each experiment is presented across the web page with a code poem overlayed on top to create a multilayered user-experience where the viewer can read both the stimuli and response simultaneously. Through this method of recontextualising by presenting 10 years of data in one sitting, a new perspective of time is formed.
UP.LOADED - Carmen Yeh 2021