Project 2:Quantified Self in the Digital Era: My Instagram Interactions

Introduction

The move to the digital age brought us lots of data, interesting not just for businesses but also for scientists studying society. They’re using it to learn about trends, especially from social media, which is a big part of our day. While reading a book by Matthew J. Salganik called “Bit by Bit: Social Research in the Digital Age,” I started thinking about my own use of Instagram. It was taking up too much of my time and distracting me. I even deleted the app from my phone for a week and decided to exercise more. So, I looked closely at how I used Instagram in March, like how much time I spent liking, commenting on, and posting things. I wanted to see if Instagram was really good for me or if it was time to let it go for good.

Research Questions:

Are there any visible trends in the time spent on Instagram and engagement levels throughout March?
Is there a specific day of the week or time period in March where Instagram activity peaks, and how does this align with mood fluctuations?
How is reducing Instagram activity related to increasing outdoor exercise?

Audiences:

My project not only makes me to be aware of the upsides and downsides of using Instagram but also speaks to a wide range of people. It’s for anyone feeling swamped by too much time online and looking for ways to cut back for a happier life. It’s for those curious about how our online lives impact our mood and happiness. It’s also for people wanting to use their phones and social media more wisely, to make real-life moments better. This story could help researchers and teachers understand more about how technology affects us and society. Plus, it’s helpful for parents and teachers trying to guide kids on how to balance their online and offline worlds. Also, it may be useful to those who are curious about how repurposing big data can impact social research.

Data Explanation:

The dataset used for this analysis was sourced directly from my Instagram account. The initial data extracted from the platform required thorough cleaning to address the common inconsistencies typical of data obtained from online sources. Instagram offers a feature that tracks the amount of time a user spends on the app. Additionally, users can request a comprehensive download of their activities over a specified period. After submitting a request, Instagram generates a link, usually available after a few hours, from which the data can be downloaded. This raw data then requires processing to structure it meaningfully for any subsequent analysis.

Starting from the beginning of March, I also began to log details of my outdoor exercises and daily mood. For the exercise log, I employed a binary variable, where ‘1’ indicates that I exercised on a given day, and ‘0’ signifies a day without exercise. My daily mood was assessed based on my overall productivity and emotional state at the end of the day, using a scale from 1 to 10. A rating of ’10’ represents a day where I felt positive and was highly productive, whereas a rating closer to ‘1’ would indicate a less productive and lower-spirited day.

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This graph displays the varying levels of my interaction on Instagram throughout the month. It tracks four key metrics: the average number of posts commented on, posts liked, stories viewed, and the average time spent on the platform each day. The lines fluctuate, showing the daily engagement levels. Peaks suggest days with higher engagement, while valleys indicate less activity. As It is clear from 21 to 25 March I removed my Instagram application. Observing this graph can help in identifying trends or patterns, such as specific days when I was more active, which could be insightful for understanding my behavior.

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This bar chart showcases the average daily time spent on Instagram, highlighted by green bars for days with lower-than-average usage and red for those exceeding the average. Notably, engagement peaks during weekends, and specifically, Tuesdays show the highest usage, likely due to staying at home without school or work commitments

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This scatterplot explores the relationship between my mood and social media activity, with each dot representing the time spent on Instagram plotted against a corresponding mood rating. Color intensity reflects the mood’s scale, ranging from lower (red) to higher (green) ratings. A trend seems to emerge where higher mood ratings may correlate with less time spent on Instagram, suggesting a potential link between social media use and emotional well-being

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The chart you’ve provided appears to be a scatter plot mapping the relationship between outdoor exercise and time spent on Instagram. What stands out is the cluster of blue points at the lower end of the y-axis, suggesting that on days when outdoor exercise was performed, less time was spent on Instagram. Conversely, the red points, especially those higher on the y-axis, suggest that more time was spent on Instagram on days without outdoor exercise. This visual relationship suggests a potential inverse correlation where increased physical activity might be associated with reduced social media usage

Findings:

The analysis suggests a consistent pattern: higher Instagram use on weekends and notably on Tuesdays, perhaps due to less structured time. Mood analysis indicated that higher well-being corresponds with decreased Instagram use. Additionally, days with outdoor exercise are associated with lower social media activity, hinting at a trade-off between physical activity and online engagement. Reflecting on the relationship between Instagram usage, mood, and well-being has led me to uninstall the app from my phone. While I am unsure how long I will stay off Instagram, my analysis suggests that even a week without it could positively impact my health and mood.

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