post an image of an element of your Content Analysis that needs input from your peers.
I want to know if I should go more in-depth for my data analysis. I lowkey think it's fine, but I kind of think I should do a little more.

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@jeffharley5
post an image of an element of your Content Analysis that needs input from your peers.
I want to know if I should go more in-depth for my data analysis. I lowkey think it's fine, but I kind of think I should do a little more.

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post an image of an element of your Content Analysis that needs input from your peers.
I was wondering if I should do individual pages for each of their backgrounds or if one page is fine?
post an image of an element of your Content Analysis that needs input from your peers.
Should I add more graphs to my paper? Personally, I think this is fine, but what do y'all think?
What did you learn about your writing process that will help you after college?
I learned that my writing process is strongest when I take time to plan, draft, and revise instead of trying to do everything at once. Organizing my ideas before writing helps me stay focused, and going back through multiple drafts improves clarity, flow, and overall quality. I also realized that stepping away from my work and revisiting it later helps me catch mistakes and see areas that need improvement. After college, this will help me produce more clear and professional writing, especially in situations where communication needs to be precise and effective.
what did you learn about drafting across multiple weeks that will inform your writing process after collegeÂ
Over multiple weeks of drafting, I learned that strong writing develops over time rather than in one sitting. Giving myself space between drafts helped me see my work more clearly, improve organization, and refine my ideas. I also realized that revising is not just about fixing grammar but about strengthening arguments, clarity, and flow. This experience will influence my writing after college by encouraging me to plan ahead, allow time for multiple drafts, and be more intentional about revising instead of trying to complete everything at once.

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OWP - Week 12
What are your ideas/suggestions for graphing your data so it's reported in multiple, meaningful ways?
I plan to represent my data using a mix of bar graphs, line graphs, and possibly pie charts to highlight different patterns. For example, bar graphs can compare performance across content creators, while line graphs can show trends over time (like engagement growth). Pie charts could help visualize proportions, such as types of content posted. Using multiple formats allows me to present the same dataset from different perspectives, making patterns easier to understand and strengthening my overall analysis.
This is a solid plan, and I like that you’re thinking about using multiple graph types to show different angles of your data. Your explanation of bar graphs for comparing creators and line graphs for showing trends over time is especially clear.
One way to make this even stronger is by being a little more specific about what each graph will show. For example, you could say bar graphs will compare likes or views between tutorial vs. non-tutorial videos, while line graphs could track engagement over a set period of posts. Also, pie charts are useful, but only if you’re showing clear proportions—otherwise they can be harder to read than bar graphs.
Overall, your idea of using multiple visuals is effective because it helps your audience understand patterns more clearly and makes your analysis more convincing.
What is data within your space? How does it impact your analysis?
Data within YouTube is the number of views, likes, and comments that individual people use to interact with videos that they either like/dislike. The option to dislike a video is also a feature that viewers can use but these days it’s unlikely you’ll see a dislike count. This is important for my analysis because paying attention to what types of videos are pulling higher views, can tell me what type of content specific audiences are looking to interact with the most. This allows me to understand which videos are more likely to perform well from a creator within the genre of content I’m analyzing in my project.
This is a strong explanation of what counts as data in your space, and you clearly connect it to your analysis. I like how you point out views, likes, and comments as key indicators since they show different levels of audience interaction.
One way to strengthen this is by explaining what each metric tells you. For example, views show how many people clicked on the video, likes suggest positive reactions, and comments can show deeper engagement or opinions. That adds more depth to your analysis.
You could also mention that while dislikes aren’t as visible anymore, they still play a role in how content is perceived, even if that data is harder to access.
Overall, you do a good job showing how this data helps you figure out what type of content performs best and what audiences are most interested in.
Original Content Post #3
what elements of data matter in your space? why?
The elements of data that matter in my space the most are the likes, comments, and shares. The number of followers doesn't pertain to my project because I'm not trying to see who gets the biggest audience. But the likes, comments, and shares will help the reader understand my question better. I'm trying to see which specific type of content within three different food TikTok accounts gets the most attention. The two types of content I determined to compare were food tutorials and non-food tutorials. That being said, however much engagement the posts receive, it would be the most beneficial to my project, compared to the length of the video, the people in it, how many followers one has, etc.
Your focus on likes, comments, and shares makes a lot of sense because those actually show how people are interacting with the content, not just how many people could see it. I also like how you explained that follower count doesn’t really matter for your project since you’re comparing types of posts, not popularity.
One thing you could add is why each type of engagement matters. For example, likes might show quick approval, comments can show deeper engagement or interest, and shares might mean the content is valuable enough for someone to pass along. That could help strengthen your point even more.
Also, even though you said things like video length or who’s in it don’t matter as much, they could still influence engagement. It might be worth briefly acknowledging that as a possible limitation in your project.
Overall, your reasoning is clear—you’re focusing on the data that best answers your question about which type of food content (tutorial vs. non-tutorial) gets the most attention.
What elements of data matter in your space? Why?
In my project, the most important elements of data are the ones that help me understand how audiences interact with different types of content. Because my research question focuses on which content category (promotional, personal, entertainment, or challenge) gets the most engagement, the data that matters most is the data that directly measures audience response and content type. The key elements are post type, likes, comments, and total engagement.
What are your ideas/suggestions for graphing your data so it's reported in multiple, meaningful ways?
To report my data in a meaningful way, I plan to use several different types of graphs, each highlighting a different aspect of my analysis. Because my project compares engagement across content categories (promotional, personal, entertainment, challenge), using multiple visualizations helps show patterns that a single graph might hide.

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How are you using comments from me to work through Draft #2?
I am using the comments to revise Draft #2 by directly addressing the gaps they identified in my first draft. Your feedback helped me understand what parts of my project needed more clarity, structure, and explanation
Original Content 2 Week 10
How will your research question help you collect data?
Writing a research question helps to take this huge chunk of content from an account, and focus on a more specific part of it. I am focusing on how visually appealing/unappealing video aesthetics affect my 3 chosen cooking pages instagram accounts. With this, when I do carry on to my research I will be focusing more on what they are doing in the video, their camera angles, the way they interact with cooking, how much the recipe really matters, etc. Research questions are great for helping you know what data it is you have to collect.
I agree—having a clear research question really helps narrow down what to focus on. For my project, my research question points me to specific content types (Challenge, Entertainment, Promotional, Personal/Emotional) and measurable engagement metrics (likes and comments). This way, I know exactly what data to collect from each post and can keep my analysis focused on how content style affects audience engagement.
Original Content Writing
How are you determining meaningful data? What method will you use to collect it?
I am determining if data is meaningful or not to my research based on the contents of the video and if it is relevant to my research question and hypothesis. My research question outlines a few specific things about the content I should be analyzing. The content should focus on the cat, the age of the cat being kitten aged (0-2 years old), and the types of content which go into a few categories, Rehabilitation, Care, or Daily Life of the cat and their human. As long as the video contains these attributes and falls into one or more of the types of content it can be considered meaningful data to my research.
That makes sense! I’m approaching meaningful data in a similar way. For my research, a post is considered meaningful if it clearly fits into one of my content categories (Challenge, Entertainment, Promotional, or Personal/Emotional) and shows measurable audience engagement through likes and comments.
How will your research question add to the ongoing conversation about content strategy?
My research question contributes to the ongoing conversation about content strategy by providing data-driven insight into what types of posts generate the most engagement on Instagram. By comparing challenge, entertainment, promotional, and personal/emotional posts across major creators, this study helps show which strategies are most effective for capturing audience attention. This adds to the broader discussion of how social media creators can design content that aligns with audience preferences and maximizes interaction
How are you determining meaningful data? What method will you use to collect it?Â
Meaningful data is determined by its relevance to my research question. I am collecting information that directly shows how audiences engage with different types of content, so likes and comments are the primary metrics because they reflect both basic and deeper interaction. To collect this data, I will manually record engagement numbers from 5 recent Instagram posts from each creator (Kai Cenat, Jake Paul, and MrBeast). I will also code each post into one clear content category (Challenge, Entertainment, Promotional, or Personal/Emotional) to make comparisons consistent and meaningful

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How will your research question help you collect data?
My research question helps me collect data by guiding what I focus on in each post. Since I’m studying how different content types impact engagement, I specifically collect data on content type, likes, and comments. This ensures that every piece of data I record directly helps me compare engagement across categories and answer my research question
Data analysis—what did you learn about data analysis from the module 4 data analysis practice project? How will that help you in module 4?
From the Module 4 data analysis practice project, I learned that data analysis is not just about collecting numbers but about identifying patterns and explaining what those patterns mean. It’s important to organize data clearly (like in tables or categories) and then compare results to see what stands out.
I also learned that interpretation is key—you have to explain why certain content performs better, not just state that it does. This includes thinking about audience behavior, content design, and how the platform works.
This will help me in Module 4 because I’ll be better prepared to turn my data into meaningful conclusions. Instead of just listing engagement numbers, I’ll be able to explain what they say about content strategy and what creators can do to improve their content.