From fd8b5843fc405ea97347eb3b7c3ed6fc153b04c9 Mon Sep 17 00:00:00 2001 From: imuniyat <108698650+imuniyat@users.noreply.github.com> Date: Mon, 11 Mar 2024 19:41:20 +0000 Subject: [PATCH] adding readability --- .../HTML/readability_of_privacy_policies.html | 668 +++++++++++++++++ content/post/Data viz/HTML/stock_market.html | 672 ++++++++++++++++++ content/post/Data viz/index.html | 2 - .../post/Data viz/{old_index.md => index.md} | 44 +- 4 files changed, 1383 insertions(+), 3 deletions(-) create mode 100644 content/post/Data viz/HTML/readability_of_privacy_policies.html create mode 100644 content/post/Data viz/HTML/stock_market.html delete mode 100644 content/post/Data viz/index.html rename content/post/Data viz/{old_index.md => index.md} (53%) diff --git a/content/post/Data viz/HTML/readability_of_privacy_policies.html b/content/post/Data viz/HTML/readability_of_privacy_policies.html new file mode 100644 index 0000000..6c2eae0 --- /dev/null +++ b/content/post/Data viz/HTML/readability_of_privacy_policies.html @@ -0,0 +1,668 @@ + + + + + + + Readability of social media privacy policies – Callysto + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Readability of social media privacy policies

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+ How difficult is it to read the terms of services? +

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Grades 5 - 9

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Ever find yourself swiftly clicking “I understand and agree?” to lengthy terms and conditions on a new service you've just signed up for? Many people do, given the often intricate language involved.

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Just how tough is it to read these terms? Our data visualization explores the readability of privacy policies on popular social media sites.

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To answer our question:
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  • We used the Princeton-Leuven Longitudinal Privacy Policy Dataset, an archive of website policies that spans 20+ years and hosts over 1 million policies. We picked the policies from popular social media sites: TikTok, Twitter, Facebook, Instagram, YouTube, and Pinterest.
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  • We analyzed the readability of the privacy policies by looking at their grade level score, reading score, and reading time.
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Visualizing the data

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The visualization below shows different measures of readability of privacy policies for TikTok, Twitter, Facebook, Instagram, YouTube, and Pinterest. You can toggle between the reading time, readability score, and grade level to compare how each of these companies perform using these different measures to test readability difficulty level.

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Readability score: Measures how easy it is to read a document. We applied the Dale-Chall readability formula, which uses a list of 3,000 words that are easily understood by an average 4th-grade student in America. Any word outside of that list is considered difficult to comprehend. A score of 9.0-9.9 indicates you have to be a college student to understand the text. Text with a score of 4.9 or lower means it is understandable by an average 4th-grade student, or younger.

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Grade score: Indicates the number of years of education required to understand the text. We applied the Flesch-Kincaid grade level formula, where a score of 5 means that a fifth grader will generally understand the text.

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Reading time: Measures the time it takes an average person to read the text. It assumes a reading pace of 14.69 milliseconds (one-thousandth of a second) per character.

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We used the Textstat Python Library to calculate all scores. You can find more information about the library here.

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The bar graphs show the privacy policies of social media platforms are overall difficult to read. The grade level score for TikTok, Twitter, Facebook, Instagram, and YouTube are all above 18, which means their privacy policies are as difficult to read as an academic paper. Pinterest’s readability score is 7.16 (understood by an average 9th or 10th-grade student) and TikTok’s is 8.64 (understood by an average 11th or 12th-grade student).

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TikTok's policy is the quickest to read. With the lengthiest reading time, Twitter matches TikTok in reading score and grade level.

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Reflect on what you see

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Look and interact with the data visualization above. When you hover the mouse over the bar graph, you’ll notice more information appears. You can toggle between grade score, reading score, and reading time to compare the three measures.

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Think about the following questions.

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  • What is the reading score of the different privacy policies?
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  • What grade level is needed to understand the privacy policies?
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  • Is there a privacy policy that is easier to read than the others, or are they all similar in difficulty?
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  • What do you wonder about the data?
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Use the fill-in-the-blank prompts to summarize your thoughts.

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  • “I used to think _______”
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  • “Now I think _______”
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  • “I wish I knew more about _______”
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  • “These data visualizations remind me of _______”
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  • "I really like _______”
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Learn how we visualized the data

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Go to our walk-through (in Jupyter notebook format) to see how the data science process was applied to create these graphs, from formulating a question to gathering the data and analyzing the data with code.

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+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/content/post/Data viz/HTML/stock_market.html b/content/post/Data viz/HTML/stock_market.html new file mode 100644 index 0000000..751d1b8 --- /dev/null +++ b/content/post/Data viz/HTML/stock_market.html @@ -0,0 +1,672 @@ + + + + + + + Understanding the stock market – Callysto + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Understanding the stock market

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+ How does understanding the stock market help students make better financial decisions? +

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Grades 9 - 12

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Grasping financial literacy through stocks from a young age establishes a foundation for long-term financial literacy, encouraging students to make informed decisions, develop saving habits, and leverage the compounding benefits of early investments.

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In this data visualization, our goal is to introduce the concept of stocks, highlighting the advantages of cultivating a positive mentality and mindset towards investing.

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To answer our question we used data from:
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Yfinance is a Python library which helps to download market data using Yahoo!’s finance API. With this library, we can freely find historical data about stocks in the stock market.

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Visualizing the data

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Initially, we explored the opening and closing price history of the S&P 500 Index — a collection of 500 of the biggest companies in the United States, chosen to show how well the stock market is doing overall. It helps investors understand if the stock market is going up or down.

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The trend line doesn’t show a significant increase until approximately 1990. Following this period, we observe a gradual uptick in stock prices, punctuated by periods of substantial growth (such as in the 2010s) and stagnation (as seen in the 2000s). However, the overall trend indicates an upward trajectory, despite periods of sudden growth and decline. This analysis served as a good benchmark to demonstrate how investing in a safe group of stocks like an ETF can lead to long-term growth and financial sustainability.

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Afterwards, we implemented the ability for users to research the historical price of different stocks on the Nasdaq, leading to informative insights such as which stocks historically perform better during particular periods and analyzing patterns of when prices generally will rise or fall.

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In this particular example, we can see Apple’s historical stock prices from 2000 to 2023. Apple’s peak stock price was around 2021 to 2022, and throughout Apple’s history, its stock price has generally shown a slow increase, starting from approximately 2010 onwards.

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Reflect on what you see

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Look and interact with the data visualization above. When you hover the mouse over the bar graph, you’ll notice more information appears.

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Think about the following questions.

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  • What do you notice about the visualizations?
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  • What do you wonder about the data?
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Use the fill-in-the-blank prompts to summarize your thoughts.

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  • “I used to think _______”
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  • “Now I think _______”
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  • “I wish I knew more about _______”
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  • “These data visualizations remind me of _______”
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  • "I really like _______”
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Learn how we visualized the data

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Go to our walk-through and Machine Learning notebook walk-through (in Jupyter notebook format) to see how the data science process was applied to create these graphs, from formulating a question to gathering the data and analyzing the data with code.

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+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/content/post/Data viz/index.html b/content/post/Data viz/index.html deleted file mode 100644 index c4c6b6e..0000000 --- a/content/post/Data viz/index.html +++ /dev/null @@ -1,2 +0,0 @@ -

hello world

-- test html \ No newline at end of file diff --git a/content/post/Data viz/old_index.md b/content/post/Data viz/index.md similarity index 53% rename from content/post/Data viz/old_index.md rename to content/post/Data viz/index.md index 8af0f28..f8cc466 100644 --- a/content/post/Data viz/old_index.md +++ b/content/post/Data viz/index.md @@ -51,4 +51,46 @@ These pre-made, introductory data science lessons are a way for students to deve

Learn how we visualized the data

Go to our walk-through (in Jupyter notebook format) to see how the data science process was applied to create these graphs, from formulating a question to gathering the data and analyzing the data with code.

-{{< /spoiler >}} \ No newline at end of file +{{< /spoiler >}} + + +{{< spoiler text="Eunderstanding stock market" >}} +

Grades 9 - 12

+

Grasping financial literacy through stocks from a young age establishes a foundation for long-term financial literacy, encouraging students to make informed decisions, develop saving habits, and leverage the compounding benefits of early investments.

+

In this data visualization, our goal is to introduce the concept of stocks, highlighting the advantages of cultivating a positive mentality and mindset towards investing.

+

To answer our question we used data from:
+

+ +

Yfinance is a Python library which helps to download market data using Yahoo!’s finance API. With this library, we can freely find historical data about stocks in the stock market.

+

Visualizing the data

+

Initially, we explored the opening and closing price history of the S&P 500 Index — a collection of 500 of the biggest companies in the United States, chosen to show how well the stock market is doing overall. It helps investors understand if the stock market is going up or down.

+

 

+

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The trend line doesn’t show a significant increase until approximately 1990. Following this period, we observe a gradual uptick in stock prices, punctuated by periods of substantial growth (such as in the 2010s) and stagnation (as seen in the 2000s). However, the overall trend indicates an upward trajectory, despite periods of sudden growth and decline. This analysis served as a good benchmark to demonstrate how investing in a safe group of stocks like an ETF can lead to long-term growth and financial sustainability.

+ +

Afterwards, we implemented the ability for users to research the historical price of different stocks on the Nasdaq, leading to informative insights such as which stocks historically perform better during particular periods and analyzing patterns of when prices generally will rise or fall.

+

+

In this particular example, we can see Apple’s historical stock prices from 2000 to 2023. Apple’s peak stock price was around 2021 to 2022, and throughout Apple’s history, its stock price has generally shown a slow increase, starting from approximately 2010 onwards.

+ +

Reflect on what you see

+

Look and interact with the data visualization above. When you hover the mouse over the bar graph, you’ll notice more information appears.

+

Think about the following questions.

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Use the fill-in-the-blank prompts to summarize your thoughts.

+ +

Learn how we visualized the data

+

Go to our walk-through and Machine Learning notebook walk-through (in Jupyter notebook format) to see how the data science process was applied to create these graphs, from formulating a question to gathering the data and analyzing the data with code.

+ +{{< /spoiler >}}