DMP Week 7 (Results and discussion)

Tonight:

Draft a short pararagraph analysising some of the data you have collected and crafting it arround your research objectives. (Probably day 3-4 in which you believed you were in a filter bubble and how It is easy to fall into one after listening to playlists)

Code your data from Kai and Badras interviews and from specific parts of your audio logs

• Present the reseults in a paragraph 
• Then analyse it in another paragraph 
• Or combine it into one paragraph 

Data visualisation for qualatative 

Direct quotes from

• Interviews 
• Your diary 
• Your audio logs 

Put them in quotes and attribute them to 'Participant 1' or 'audio log 1'

Word clouds 

• A special visualisation to show key trends 
• Highlights the most common words 
• use a vaidation
• Not a stand alone tool (use quotes more often)

Data analysis 

• Finds patterns in your data that relates to your research topic 
• Examines your research topic through your data 
• Reader needs to understand how you have reached your conclusions 
• Seeks to identify patterns and relationships

Qualitative data analysis 

• Thematic analysis is the most commonly used in qualitative analysis
• Simple and flexible approach
• Interpreting the data in terms of themes
• A theme captures something important about the data in relation to the research question

Thematic analysis 

• Transcribe data 
• Read it through the lens of your framework and research objectives (look for themes)
• categorise data through highlights 
• interpretations and presentations (what does it tell usabout the research)

At the end highlight that your sample was small and could have generalised your data 

1. Mention a reference that you have that relates to your research (Filter bubbles in spotify)

2. Mention how your study backs up this claim and shows that filter bubbles can be found on accounts using spotify 

3. Include direct quotes from your audiologs and from interviews

This draft covers the research objective 

-              Explore the effects of Spotify’s Recommender Algorithms on users’ music tastes.

This research has found that Spotify is potentially failing to use user' historical data in a way that helps users explore new music, which is limiting the music tastes of most users. The research also found that users seem to place themselves in what Eli Pariser (2011) calls a filter bubble. An interview conducted with a participant, who uses Spotify daily, proclaimed that his recommendations are "never really anything new, unless I go out and search for it." (Participant 1) Plus, when asked if he felt like the RA Spotify uses was narrowing his music tastes and keeping him in a filter bubble. He replied "Yeah, Id say so. It shows alot of the same stuff, things Ive already listened to." (Participant 1) This data seems to correlate with Eli Periser's claims (2011) and prove that Spotify's RA's directly effect users music tastes. 

Furthermore, over the course of the research study. The researcher had found that, through the research experiment, Spotify was pushing content that felt similar, suggesting that a narrowing effect was taking place. What further helps support these findings is the dairy entrie from the fourth day of the experiment. In which the researcher exaplains his frustartion with the RA and mentions how the applications seemed to be moving the account into a filter bubble. Given that there were no random recommendations or inclusions of diverse content, which Quazi, M, Areeb and others (2023) say is neccesarry for any application using a recommender system.  

"Today I finished the 'Uni Bangers' playlist and proceeded to let Spotify recommend me music for the rest of the day. As once you finish a playlist on Spotify the application continues to recommend you music using its RA. With this, I noticed that the music I was being recommended felt very similar; multiple songs were being replayed which was making my experience using the app very frustrating. I believe that I was in a filter bubble today, as I was being recommended no new content. I had to check the app multiple times just to see if I was still on the playlist." (Diary entry from day 4 of the research study)

Pariser, E. (2011) The filter bubble: What the internet is hiding from you. 1st edn. New York, America: The penguin press, p. 9-13.

Areeb, Q. M., Nadeem, M., Sohail, S. S., Imam, R., Doctor, F., Himeur, Y., Hussain, A. and Amira, A. (2023) 'Filter bubbles in recommender systems: Fact or fallacy—A systematic review', Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(6), pp. e1512.

What I want to do?

Videography 

YouTube 

Music   

Trappin

A lost young adult trying to find his path in the world and discover what he wants to do through the power of content creation.