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ISIS_Timeline

For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter

I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.

I've used Postgres + Django + D3 (The stack I used in my previous internship).

While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy. I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.

This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.

To get started:

  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.

Additional Note: I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.

About

For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
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codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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ISIS_Timeline
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.
I've used Postgres + Django + D3 (The stack I used in my previous internship).
While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy.
I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.
This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.
To get started:
  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.
Additional Note:
I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.
About
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
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ISIS_Timeline
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.
I've used Postgres + Django + D3 (The stack I used in my previous internship).
While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy.
I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.
This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.
To get started:
  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.
Additional Note:
I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.
About
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
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, 'i');
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// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
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var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
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terms.forEach(function(term) {
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
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try {
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ISIS_Timeline
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.
I've used Postgres + Django + D3 (The stack I used in my previous internship).
While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy.
I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.
This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.
To get started:
  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.
Additional Note:
I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.
About
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
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var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
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observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
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var __m = "youtube.com";
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ISIS_Timeline
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.
I've used Postgres + Django + D3 (The stack I used in my previous internship).
While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy.
I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.
This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.
To get started:
  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.
Additional Note:
I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.
About
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
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ISIS_Timeline
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.
I've used Postgres + Django + D3 (The stack I used in my previous internship).
While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy.
I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.
This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.
To get started:
  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.
Additional Note:
I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.
About
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
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// Remove or un-stick sticky/fixed headers that block content
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function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
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el.style.zIndex = 'auto';
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ISIS_Timeline
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.
I've used Postgres + Django + D3 (The stack I used in my previous internship).
While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy.
I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.
This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.
To get started:
  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.
Additional Note:
I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.
About
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
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, 'i');
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// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
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var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
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ISIS_Timeline
For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
I wanted to put my skills from previous internships to use, so I decided that I'd try them out on this dataset.
I've used Postgres + Django + D3 (The stack I used in my previous internship).
While it may be overkill, I just wanted to practice using the tech. The added UI, and built in search and filtering though is very handy.
I hope that data scientists who are not as programming savvy will be able to use these visualizations and UI to help enhance their data analytics.
This is my first personal project on Github! So apologies if I commit anything that is very messy or not meta.
To get started:
  1. Make sure you have the requirements
  2. You'll need to load in the users and tweets into the postgres database. Check out make_users.py and make_tweets.py.
  3. cd into timeline (where manage.py is stored) These next few instructions are for those non-django users
  4. python manage.py runserver
  5. http://127.0.0.1:8000/entry/swag/#/ To view the endpoints http://127.0.0.1:8000/admin to see the Admin UI :D
  6. NOTE: Only GET is supported for now. I currently have no need to make the other endpoints, so I didn't bother.
Additional Note:
I haven't done any testing, and I know that's not great software engineering. But right now I'm really interested in doing some other things with this dataset so I'll put that on the backburners for now.
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For the Kaggle Post: https://www.kaggle.com/kzaman/how-isis-uses-twitter
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