6:30 a.m. – I wake up and scroll through TikTok for a few minutes. By watching and liking videos, I’m providing behavior data (what I watch, how long I stay, what I skip). The app also collects my interests (e.g., motivational content, lifestyle videos) and likely feeds me more similar content. TikTok’s algorithm uses this data to curate my For You Page, which means I don’t have full control over what I see.

6:45 a.m. – While getting ready, I stream music on Spotify. Spotify tracks listening behavior (skips, playlists, replayed songs), which informs recommendations and targeted ads (even though I mostly use playlists). It also collects my device data and time-of-day usage, which helps it know my morning routine.

7:45 a.m. – At work, I check Outlook and Microsoft Teams. Here, I’m providing professional communication data (emails, calendar scheduling, chats). While these platforms are secure within the workplace, they still log metadata like who I message, when, and how often, which shapes how my employer tracks productivity. I am also still using Spotify through out the day which informs the system of recommendations.

12:00–12:30 p.m. – Lunch break. I watch anime on Crunchyroll, which collects subscription/payment data (since I pay for premium) as well as my viewing preferences. This data might be used to recommend other shows, or even marketed to advertisers who want to target anime fans.

12:30–4:00 p.m. – Back to Teams and Outlook. By using these, I share work-related behavior data, such as when I respond, how quickly, and which tasks I prioritize. Even though this is professional communication, the platforms are designed to measure engagement and efficiency.

5:00–7:00 p.m. – After work, I watch YouTube. I mostly watch gaming, yoga, and travel videos. YouTube collects watch history and search queries, shaping its recommendation algorithm. I also see a lot of promoted content and ads — often travel ads, which shows that my viewing behavior connects directly to ad targeting.

8:00 p.m. – I read on my Kindle. Amazon collects purchase and reading data — it knows which books I buy, how quickly I read them, and even where I stop in a book. This data helps Amazon suggest new titles and also contributes to its broader consumer tracking.

8:30 p.m. – I scroll on Pinterest to look for inspiration for nails, outfits, or DIY apartment projects. This generates search queries, viewing history, and saved pin data, which Pinterest uses to profile my personal style and household interests. That profile influences the promoted content and brand partnerships I see, often leaning toward beauty, fashion, and home goods.

9:00–11:00 p.m. – I play video games on with my friends, specifically a game called Peak, where the goal is to climb mountain tops in different climates. Steam collects gameplay behavior data (time spent, achievements unlocked, in-game purchases, and multiplayer interactions). It also records social data when I chat with friends. This information can influence recommendations for other games, as well as how Steam markets content to me. Additionally, game developers may use the analytics to track player habits and adjust updates or features.

11:30 p.m. – I fall asleep watching Cars on Disney+. Even though this feels low-risk, streaming platforms still log behavioral data (what I play, how often, what time of day). Over time, these patterns help shape what shows and movies are promoted to me.

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Reflection

Looking back, I realize how much behavioral and interest data I generate across platforms without always thinking about it. Even simple activities like streaming music, watching anime, or reading a book involve my data being logged, categorized, and fed into algorithms. One clear theme is that most platforms are not just providing content but also learning about my habits in order to keep me engaged and in some cases, to push advertisements.

Adding Pinterest in the evening highlighted how targeted these platforms can be with lifestyle and consumer habits. The app clearly learns about my style preferences and promotes content in beauty, fashion, and home improvement. Similarly, gaming on Steam showed me how even recreational activities come with behavioral tracking and social interaction data.

I also realize that some of my personal information, like my home address and phone number, is already stored within the media I use (for example, on shopping or subscription platforms). This means that in addition to my browsing and interaction data, companies have access to sensitive identifying details that can connect all of my digital activity back to me.

While I’m not drastically changing my media habits, I am more aware of how every click and interaction contributes to a digital profile of me. This reflection makes me consider tightening privacy settings, limiting autoplay features, and being more mindful of how algorithms influence what I consume.

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References

ByteDance Ltd. (2025). TikTok [Mobile app]. https://www.tiktok.com

Spotify AB. (2025). Spotify [Mobile app]. https://www.spotify.com

Microsoft Corporation. (2025). Outlook [Email service]. https://outlook.live.com

Microsoft Corporation. (2025). Microsoft Teams [Communication platform]. https://www.microsoft.com/microsoft-teams

Pinterest, Inc. (2025). Pinterest [Mobile app]. https://www.pinterest.com

Crunchyroll, LLC. (2025). Crunchyroll [Streaming service]. https://www.crunchyroll.com

Google LLC. (2025). YouTube [Video platform]. https://www.youtube.com

Amazon.com, Inc. (2025). Kindle [E-book service]. https://www.amazon.com/kindle-dbs

Valve Corporation. (2025). Steam [Game distribution service]. https://store.steampowered.com

Pixar Animation Studios & Walt Disney Pictures. (2003). Finding Nemo [Film]. Walt Disney Studios Motion Pictures.