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post-time.html
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Test Library</title>
<link rel="icon" type="image/png" href="docs/images/favicon.PNG">
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<link href="./docs/css/styles.css" rel="stylesheet"/>
<script src="./docs/js/testDetailsTemplate.js" defer></script>
</head>
<body>
<div class="container-fluid p-5 page-container">
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<div class="display-5">CIB Mango Tree</div>
<div class="fw-light">A Civic Tech DC Project</div>
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<div class="col-9 mb-5">
<div class="card card-body transparent-card">
<div id="template-placeholder"></div>
<test-details>
<span slot="test-title">
Graphing Post Time Test
</span>
<span slot="test-summary">
This test will graph all the posts in your dataset by the
time of day that they were made.
The test will yield a graph that visualizes the frequency
of posting within discrete time windows of a length that
you choose across a 24-hour period, allowing you to look
for abnormal posting patterns.
</span>
<a slot="code-link" href="https://cib-mango-tree.github.io/CIB-Mango-Tree-Website/" target="_blank" rel="noopener noreferrer">
<b>See Code for Test on GitHub</b>
</a>
<a slot="reference-link" href="https://pmc.ncbi.nlm.nih.gov/articles/PMC7608205/" target="_blank" rel="noopener noreferrer">
Wang, Austin Horng-En, Mei-chun Lee, Min-hsuan Wu, and Puma Shen.
“Influencing overseas Chinese by tweets: text‑images as the key
tactic of Chinese propaganda.” Journal of Computational Social
Science 3, no. 20 (November 2020): 469-486.
</a>
<div slot="reqs-boxes">
<div class="reqs-boxes">Username</div>
<div class="reqs-boxes">Unique Post Number</div>
<div class="reqs-boxes">Timestamp</div>
</div>
<div slot="optional-reqs-boxes">
<div class="reqs-boxes">Post Content</div>
</div>
<ol slot="test-process" type="1">
<br>
<li>
<b>Set Time Window Length: </b>Start by specifying
the desired length of your time windows for the
24-hour period (recommended: 30 minutes).
</li>
<li>
<b>Tally Posts: </b>The test will count the posts
made in each time window across your dataset.
For example: with a 30-minute window, it will
count posts from 12:00 AM to 12:30 AM, 12:30 AM
to 1:00 AM, and so on.
</li>
<li>
<b>Graph Results: </b>Finally, the test will display
these frequencies in a csv file and bar chart, with
each bar representing a specific time window across
the 24-hour period.
</li>
</ol>
<div slot="output-summary">
<br>
<span>
<b>Output Format Option 1: .csv File</b>
</span>
<ul>
<li>
The test will produce a .csv file that provides the
numerical tallies of posts for each given time window
in the 24-hour period.
</li>
</ul>
<img slot="output-image" src="./docs/images/graphing-time-test-csv-output.png" alt="Sample CSV output" style="max-width: 100%; height: auto; display: block; border: 2px solid #309c5c; border-radius: 8px; padding: 5px;">
<br>
<span>
<b>Output Format Option 2: Offline Dashboard</b>
</span>
<ul>
<li>
The test will also produce a link to a
dashboard that can run offline in your browser
(this dashboard does not require an internet connection).
The dashboard will render the 24-hour graph of post frequency.
</li>
</ul>
<img slot="output-image" src="./docs/images/graphing-post-time-dashboard.png" alt="Sample Dashboard output" style="max-width: 100%; height: auto; display: block; border: 2px solid #309c5c; border-radius: 8px; padding: 5px;">
</div>
</test-details>
</div>
</div>
</div>
</div>
</div>
</div>
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