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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Data Science Portfolio - ABSCBN Tax Evasion</title>
<link rel="apple-touch-icon" sizes="72x72" href="/apple-touch-icon.png">
<link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
<link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png">
<link rel="manifest" href="/site.webmanifest">
<link rel="mask-icon" href="/safari-pinned-tab.svg" color="#5bbad5">
<meta name="msapplication-TileColor" content="#603cba">
<meta name="theme-color" content="#ffffff">
<link rel="stylesheet" href="sass/styles.css">
<script src="https://kit.fontawesome.com/cad6e051d3.js" crossorigin="anonymous"></script>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
</head>
<body>
<header>
<div class="container">
<img src="images/header_logo.png" alt="ABS CBN Logo ">
<nav>
<div class="navDesktop">
<ul>
<li><a onclick="navButtonClick()" href="#">Home</a></li>
<li><a onclick="navButtonClick()" href="#background">Background</a></li>
<li><a onclick="navButtonClick()" href="#datacollection">Data</a></li>
<li><a onclick="navButtonClick()" href="#results">Results</a></li>
<li><a onclick="navButtonClick()" href="#methods">Methods</a></li>
<li><a onclick="navButtonClick()" href="#conclusion">Conclusion</a></li>
<li><a onclick="navButtonClick()" href="#team">Team</a></li>
</ul>
</div>
<div class="ulHolder">
<ul>
<li><a onclick="navButtonClick()" href="#">Home</a></li>
<li><a onclick="navButtonClick()" href="#background">Background</a></li>
<li><a onclick="navButtonClick()" href="#datacollection">Data</a></li>
<li><a onclick="navButtonClick()" href="#methods">Methods</a></li>
<li><a onclick="navButtonClick()" href="#results">Results</a></li>
<li><a onclick="navButtonClick()" href="#conclusion">Conclusion</a></li>
<li><a onclick="navButtonClick()" href="#team">Team</a></li>
</ul>
</div>
<div class="spacer"></div>
<button class="navButton" type="button" onclick="navButtonClick()">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" strokeWidth={1.5} stroke="currentColor">
<path strokeLinecap="round" strokeLinejoin="round" d="M3.75 6.75h16.5M3.75 12h16.5m-16.5 5.25h16.5" />
</svg>
</button>
</nav>
</div>
</header>
<section id="hero" class="container">
<p>JAZ</p>
<h1>Good information may <span class="red-text-flicker">cost a fortune</span>, but bad information can <span class="red-text-flicker">cost a country</span></h1>
<div class="split-1-1">
<div>
<div class="circle bg-red"></div>
<div class="circle bg-green"></div>
<div class="circle bg-blue"></div>
</div>
<div>
<p>Our project is an analysis of the viral issue: ABS-CBN Tax Evasion Mis/Disinformation. Our team, JAZ, aims to use our knowledge about Data Science to gain knowledge and new insight to this issue and share it to the world.</p>
</div>
</div>
<img src="images/hero_banner.png" alt="">
</section>
<section id="overview" class="container">
<p>Overview</p>
<div class="split-3-2">
<div>
<h2>While social media can increase freedom of expression and speech, we can also see how it can do the opposite — <span class="red-text-flicker">the spread of misinformation surrounding ABS-CBN’s tax evasion case</span> on Twitter has arguably contributed to the public support for its non-renewal.</h2>
<p>This is the reason why we decided to focus on this case: to get to the root behind how misinformation in social media can ultimately contribute to the stifling of the freedom of the press.</p>
</div>
<div>
<div class="left-border">
<h3>Problem</h3>
<p>Misinformation regarding ABS-CBN’s, particularly about its tax evasion case, is prevalent on social media sites like Twitter.</p>
</div>
<div class="left-border">
<h3>Solution</h3>
<p>Our solution is to use data science to gain insights on the spread of misinformation on Twitter and subsequently, uncover actionable steps that can battle this misinformation phenomenon.</p>
</div>
</div>
</div>
<!-- <img src="images/overview_banner.png" alt=""> -->
</section>
<section id="background" class="container">
<p>Background</p>
<h2>On May 5, 2020, ABS-CBN, one of the most prominent Philippine media networks, was <span class="red-text-flicker">shut down</span> despite Congress filing 11 bills to renew the franchise since 2014.</h2>
<p class="description">The renewal of the franchise was opposed by former president Rodrigo Duterte for many reasons. One of his main allegations against the media network was that they had been “cheating [the] government by the billions [of pesos] in taxes”, despite BIR clearing ABS-CBN of any tax delinquencies. While the NTC said that ABS-CBN would be allowed to continue operating after May 4, they issued a cease and desist letter against ABS-CBN after being pressured by the Solicitor General. This would prevent ABS-CBN from airing on TV and radio stations.</p>
<ul class="references">
<li><a target="_blank" href="https://business.inquirer.net/301458/abs-cbn-has-no-tax-delinquency-regularly-paying-taxes-bir-exec">[1]</a></li>
<li><a target="_blank" href="https://www.vice.com/en/article/9359k8/abs-cbn-shutdown-duterte-philippines-explained">[2]</a></li>
<li><a target="_blank" href="https://verafiles.org/articles/vera-files-fact-check-duterte-makes-false-claim-abs-cbns-tax">[3]</a></li>
</ul>
<div>
<ul class="taxcases">
<li class="taxcase-card">
<a class="link" href="https://business.inquirer.net/301458/abs-cbn-has-no-tax-delinquency-regularly-paying-taxes-bir-exec" target="_blank">
<div class="img-holder"><img src="images/taxcase1.jpg" alt="Tax evasion case 1"></div>
<div class="bottom">
ABS-CBN has no tax delinquency; ‘regularly’ paying taxes – BIR exec
</div>
</a>
</li>
<li class="taxcase-card">
<a class="link" href="https://www.vice.com/en/article/9359k8/abs-cbn-shutdown-duterte-philippines-explained" target="_blank">
<div class="img-holder"><img src="images/taxcase2.jpg" alt="Tax evasion case 2"></div>
<div class="bottom">
Everything You Want to Know About the ABS-CBN Shutdown, Answered
</div>
</a>
</li>
<li class="taxcase-card">
<a class = "link" href="https://verafiles.org/articles/vera-files-fact-check-duterte-makes-false-claim-abs-cbns-tax" target="_blank">
<div class="img-holder"><img src="images/taxcase3.jpg" alt="Tax evasion case 3"></div>
<div class="bottom">
VERA FILES FACT CHECK: Duterte makes false claim on ABS-CBN’s tax ‘violation’
</div>
</a>
</li>
</ul>
</div>
</section>
<section id="ledtoask" class="container">
<p>This led us to ask,</p>
<div class="split-3-2">
<div>
<h2>Based on the 3 rhetorical appeals (logic, emotion, credibility), <span class="red-text-flicker">how do tweets containing misinformation possibly influence public opinion</span> to oppose the franchise renewal of ABS-CBN? </h2>
</div>
<div>
<div class="left-border">
<h3>Null Hypothesis</h3>
<p>All of the tweets are equally likely to gain interaction whether it appeals to emotion, credibility, and logic.</p>
</div>
<div class="left-border">
<h3>Alternative Hypothesis</h3>
<p>Tweets that use a specific type of rhetoric appeal gain more interaction.</p>
</div>
<div class="left-border">
<h3>Action Plan</h3>
<p>Analyze the content of tweets that posted mis/disinformation about ABS - CBN’s tax payment.</p>
</div>
</div>
</div>
</section>
<section id="datacollection" class="container">
<p>Data Collection</p>
<div class="split-3-2">
<div>
<h2>We mined the internet for <span class="red-text-flicker">fake news data</span> on ABS-CBN Tax Evasion using the following</h2>
</div>
<div>
<p>Analyze the appeals of the tweets containing disinformation about the ABS-CBN tax ecasion case.</p>
<a target="_blank" href="https://docs.google.com/spreadsheets/d/1ewINe6hIYb2kZ8zoC1A8L-Lp7TidlJJo1abCQLxYb4M/edit#gid=527125231" class="viewExternal">
View our dataset here
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="linkIcon">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 19.5l15-15m0 0H8.25m11.25 0v11.25" />
</svg>
</a>
</div>
</div>
<div class="card1">
<div class="title">
<span>Key</span>
<span>Words</span>
</div>
<div class="keywords">
<ul>
<li><a target="_blank" href="https://twitter.com/search?q=Tax%20Evasion&src=typed_query">Tax Evasion</a></li>
<li><a target="_blank" href="https://twitter.com/search?q=ABS-CBN&src=typed_query&f=top">ABS-CBN</a></li>
<li><a target="_blank" href="https://twitter.com/search?q=%23YestoABSCBNshutdown&src=typed_query&f=top">#YestoABSCBNshutdown</a></li>
<li><a target="_blank" href="https://twitter.com/search?q=%23NotoABSCBNshutdown&src=typed_query&f=top">#NotoABSCBNshutdown</a></li>
<li><a target="_blank" href="https://twitter.com/search?q=Buwis&src=typed_query&f=top">Buwis</a></li>
<li><a target="_blank" href="https://twitter.com/search?q=%23NoToABSCBNFranchiseRenewal&src=typed_query&f=top">#NoToABSCBNFranchiseRenewal</a></li>
</ul>
</div>
</div>
<div class="card2">
<div class="title">
<span>Tools</span>
</div>
<ul>
<li>
<a href="https://colab.google/"><img src="images/colab.png" alt="Google Colab"/><span>Google Colab</span></a>
</li>
<li>
<a href="https://www.python.org/"><img src="images/Python.png" alt="Python"/><span>Python</span></a>
</li>
<li>
<a href="https://pandas.pydata.org/"><img src="images/pandas.png" alt="Pandas"/><span>Pandas</span></a>
</li>
<li>
<a href="https://numpy.org/"><img src="images/numpy.png" alt="Numpy"/><span>Numpy</span></a>
</li>
<li>
<a href="https://scipy.org/"><img src="images/scipy.png" alt="Scipy"/><span>Scipy</span></a>
</li>
<li>
<a href="https://matplotlib.org/"><img src="images/matplotlib.png" alt="Matplotlib"/><span>Matplotlib</span></a>
</li>
<li>
<a href="https://www.nltk.org/"><img src="images/nltk.png" alt="NLTK"/><span>NLTK</span></a>
</li>
<li>
<a href="https://pypi.org/project/googletrans/"><img src="images/google translate.png" alt="Google Translate"/><span>Google Translate</span></a>
</li>
<li>
<a href="https://plotly.com/python/"><img src="images/plotly.png" alt="Plotly"/><span>Plotly</span></a>
</li>
<!-- <ul>
<li><img src="/images/colab.png" alt="Google Colab"/></li>
</ul>
<ul>
</ul>
<ul>
<li><img src="" alt=""></li>
</ul> -->
</ul>
</div>
</section>
<section id="methods" class="container">
<p>Methods</p>
<div class="split-3-2">
<div>
<h2>Let's talk about our <span class="red-text-flicker">data science methodology</span>.</h2>
</div>
<div>
<p>We performed inferential statistics to learn more about our data.</p>
<a target="_blank" href="https://colab.research.google.com/drive/1cpsxc5IbCm_V7Qn5TFSrFJVDmbLNdYlh" class="viewExternal">
View our data exploration here
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="linkIcon">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 19.5l15-15m0 0H8.25m11.25 0v11.25" />
</svg>
</a>
</div>
</div>
<ul class="method-list">
<li class="methodItem">
<div class="tile">
<button type="button" onclick="methodClick(1)" id="pill1" class="pill"><span>+</span></button>
<div class="info">
<h2 class="title">Preprocessing</h2>
<p class="desc">Data is never clean when we first collect it. There are always missing values or incorrect formats. An important first step to our data exploration is handling missing data, ensuring formatting consistency
as well as applying some inferential statistics by encoding categorical data and standardizing.</p>
</div>
</div>
<div id="hideable1" class="hideable">
<div id="hideableContent1" class="hideableContent">
<h4>Handling Missing values</h4>
<p>When we check for columns with missing values, the columns 'Account bio', 'Tweet Translated', 'Screenshot', 'Remarks', 'Reviewer' and 'Review' all had missing values, but they are deemed unnecessary for our data exploration so they are left as is.</p>
<p>The column 'Views' is also disregarded since all the tweets were posted before Dec. 22, 2022, when the Views feature was added to Twitter.</p>
<h4>Ensuring Formatting Consistency</h4>
<p>To ensure formatting consistency, we have formatted all of the data as per instructions when we collected our data using our Python algorithm.</p>
<h4>Categorical Data Encoding</h4>
<p>The columns 'Account type', 'Tweet Type', 'Content type', 'Rating', and 'Appeal' have qualitative data which can be encoded using numerical values.</p>
<p>We used numbers starting from 1 to maximum amount of categories for a parameter to encode the categories.</p>
<h4>Standardization</h4>
<p>We used the mean and z-scores to standardize some of the categories like 'Following', 'Followers', 'Likes', 'Replies', 'Retweets', and 'Quote Tweets' to get an overview on what the average interaction of a tweet containing mis/disinformation regarding ABS-CBN Tax Evasion.</p>
<h4>Dealing With Outliers</h4>
<p>Outliers show that there are data gathered that exceed average expectations. This could cause skewed distributions in standardization of the data and may cause abnormalities in the modeling stage.</p>
<p>However, the outliers also show some meaning behind the interactions that tweets containing mis/disinformation. Tweets that contain high interaction are few and far in between and users that have high followings are rare.</p>
<p>Hence, the outliers are left as is.</p>
</div>
</div>
</li>
<li class="methodItem">
<div class="tile">
<button type="button" onclick="methodClick(2)" id="pill2" class="pill"><span>+</span></button>
<div class="info">
<h2 class="title">Natural Language Processing</h2>
<p class="desc">Processing the contents of the tweets proved to be difficult in Filipino. We had needed to translate the tweets into English and manually check
for the correct translation before we could process them for stemming and lemmatization.</p>
</div>
</div>
<div id="hideable2" class="hideable">
<div id="hideableContent2" class="hideableContent">
<h4>Cleaned the Filipino Tweets</h4>
<p>Because the majority of the tweets were in Filipino, they had to be manually cleaned to ensure that the translation would be more accurate. Here are some key parts of the manual cleaning:</p>
<ul>
<li>Changing Filipino slang to improve accuracy <span class="grey">(changing shorthand Filipino to the formal word)</span></li>
<li>Lemmatizing specific, non-english terms <span class="grey">(words referring to ABS-CBN to “abscbn”)</span></li>
<li>Lemmatizing groups of special category of words <span class="grey">(variants of haha, hahaha, HAHAHAH as “laughter”)</span></li>
</ul>
<h4>Translated the Tweets</h4>
<p>GoogleTrans was used to translate the cleaned raw tweets from Filipino to English. The translation was further cleaned by:</p>
<ul>
<li>Lowercasting the translated tweets</li>
<li>Removing punctuation marks</li>
</ul>
<h4>Manually Corrected the Translation</h4>
<p>The Google Sheets was updated to include the translated tweets. The translations were then checked manually to improve the context accuracy, fix erroneous spacing and catch mistakes. After updating, the new data was pulled to be used in the analysis.</p>
</div>
</div>
</li>
<li class="methodItem">
<div class="tile">
<button type="button" onclick="methodClick(3)" id="pill3" class="pill"><span>+</span></button>
<div class="info">
<h2 class="title">Visualization</h2>
<p class="desc">Simple words would not demonstrate the our foundings. After all our hardwork, we've collated our newfound knowledge using graphs
and plots to make it easily digestible.</p>
</div>
</div>
<div id="hideable3" class="hideable">
<div id="hideableContent3" class="hideableContent">
<h4>Average interaction (replies & retweets, vs likes) of the three appeals</h4>
<p>This first bar graph displays an average of 24 likes on logically appealing tweets. Which is evidently higher than tweets that appeal to emotion and credibility. Overall, logical tweets have higher average interaction than the other appeals.</p>
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<h4>Frequency of Tweets about ABS CBN Tax Evasion</h4>
<p>This graph shows that the peak of the tweets discussing the “ABS-CBN” tax evasion case peaked in January 2022 and slowly declined. However, the discussion resurfaced once again around December 2022.</p>
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<h4>Follower Distribution of Accounts</h4>
Follower count can change over time due to various reasons which might be an unreliable measure of credibility in the long run. However, we still believe that follower count is regarded as one of the measures of a user’s credibility on Twitter.
<p>The bar graph shows the amount of followers of our Twitter user sample at the time we collected the data. On the far left, more than 60% of the sample have less than 500 followers. There are lesser Twitter users who propagated misinformation about ABS-CBN and had a high follower count.</p>
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<h4>NLP Word Frequency Visualization</h4>
<p>From the histogram, there are some interesting insights that we can gather. While the four most common words aren’t surprising, the word “billion” is predominantly used by disinformation tweets that appeal to logic and credibility.</p>
<p>Additionally, only tweets that appealed to logic used “GMA”, “hectare” and ITR, which are key points often brought up to explain why the ABS-CBN tax evasion case is valid.</p>
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<h4>Account Date Joined vs Tweet Date Posted</h4>
<p>This heatmap shows the correlation between when a tweet was posted and when the user joined Twitter. This gives further insight to the behavior of the “trolls” who made disinformation posts about the ABS-CBN case.</p>
<p>Visually, we can observe that around half of the users who made disinformation tweets were relatively new users whose accounts were made from 2020 onwards.</p>
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</div>
</div>
</li>
<li class="methodItem">
<div class="tile">
<button type="button" onclick="methodClick(4)" id="pill4" class="pill"><span>+</span></button>
<div class="info">
<h2 class="title">Testing</h2>
<p class="desc">After getting a clearer picture of our data, we used Chi Square Test of Independence to determine whether there is a relationship between the appeals and average interaction of a tweet.</p>
</div>
</div>
<div id="hideable4" class="hideable">
<div id="hideableContent4" class="hideableContent">
<p>But first things first, there are two assumptions for this test.</p>
<ol>
<li>All expected counts are at least 5</li>
<li>Individual observations are independent and the population should be at least 10 times as large as the sample, (10n < N)</li>
</ol>
<p>We can assume #2 to always be true, and you can see in the table below that our dataset achieves the #1 since the lowest expected count is 5.4.</p>
<table>
<thead>
<th>Appeal</th>
<th>Interaction</th>
<th>Observed</th>
<th>Expected</th>
</thead>
<tr>
<td>Logic</td>
<td>Higher than Ave.</td>
<td>3</td>
<td>12.6</td>
</tr>
<tr>
<td>Logic</td>
<td>Lower than Ave.</td>
<td>60</td>
<td>50.4</td>
</tr>
<tr>
<td>Emotion</td>
<td>Higher than Ave.</td>
<td>21</td>
<td>16</td>
</tr>
<tr>
<td>Emotion</td>
<td>Lower than Ave.</td>
<td>59</td>
<td>64</td>
</tr>
<tr>
<td>Credibility</td>
<td>Higher than Ave.</td>
<td>10</td>
<td>5.4</td>
</tr>
<tr>
<td>Credibility</td>
<td>Lower than Ave.</td>
<td>17</td>
<td>21.6</td>
</tr>
</table>
<p class="tableLabel">Observed and Expected Values</p>
<p>Using <span class="code">stats</span> from <span class="code">scipy</span>, we got the following results from the test:</p>
<table>
<tr>
<th>Critical Value</th>
<td>5.99</td>
<th>Significance Level</th>
<td>0.05</td>
</tr>
<tr>
<th>Chi-Square Value</th>
<td>15.9941</td>
<th>Degrees of Freedom</th>
<td>2</td>
</tr>
<tr>
<th>P-value</th>
<td>0.0003</td>
<th></th>
<td></td>
</tr>
</table>
<p class="tableLabel">Chi Square Results</p>
</div>
</div>
</li>
</ul>
</section>
<section id="results" class="container">
<p>Results</p>
<div class="split-3-2">
<div>
<h2>Here's <span class="red-text-flicker">what we found out</span> about the data we analyzed.</h2>
</div>
<div>
<p>We combined our insights from our inferential statistics, Natural Language Processing and statistical modeling.</p>
</div>
</div>
<ul class="result-cards">
<li>
<div class="card">
<h3>Results 1</h3>
<p>A p-value of 0.0003 means we reject our null hypothesis in favor of the alternative hypothesis which states that the amount of interaction and the appeal of a tweet are associated, specifically tweets appealing to logic had lower than its average of 24 likes.</p>
</div>
</li>
<li>
<div class="card">
<h3>Results 2</h3>
<p>If we take a look at emotional appealing tweets, at the height of the controversy, it had the highest count compared to the other appeals but only had a low average interaction. The low frequency of credibility appealing tweets combined with the fact that most of the accounts were relatively new at the time, more than half of those accounts were anonymous, and had the least amount of followers, we can say credibility did not matter as much as the other two.</p>
</div>
</li>
<li>
<div class="card">
<h3>Results 3</h3>
<p>Even if tweets appealing to logic were associated with low interaction, it was the only appeal that had the key arguments relating to “GMA”, “hectare”, and “ITR”. The users who created those tweets generally had more followers and the amount of logic tweets were more consistent over time.</p>
</div>
</li>
</ul>
</section>
<!-- <section id="conclusion" class="container">
<h2><span class="red-text-flicker">Conclusion</span></h2>
<p>In conclusion, <span class="red-text-flicker">tweets appealing to logic can be said to be a major factor in propagating the misinformation regarding ABS-CBN’s tax evasion</span> by having a consistent narrative of comparing it to GMA as well as providing additional misinformation regarding the land it occupied.</p>
</section> -->
<section id="background" class="container">
<p>In conclusion, </p>
<h2>Tweets appealing to <span class="red-text-flicker">logic can be said to be a major factor in propagating the misinformation</span> regarding ABS-CBN’s tax evasion by having a consistent narrative of comparing it to GMA as well as providing additional misinformation regarding the land it occupied.</h2>
</section>
<section id="ending" class="container">
<h3>Implications</h3>
<p>Now, we have an idea on how misinformation tweets are structured and how it could appeal to the common Twitter user. We must be vigilant when we are presented with multiple pieces of information that could make an argument seem factual or logical, convincing us to believe fallacious statements. A good dose of skepticism and factual verification must always be our default when it comes to new information.</p>
<p>Another interesting insight is the lack of appeal to credibility of misinformation tweets. We must also verify our sources of information and whether they have the credibility to back-up their claims. ABS-CBN needed to build their credibility for the people to trust them yet misinformation can tarnish their reputation without a single ounce of credibility. Before even reading or listening to an argument, verify that the source is credible.</p>
<h3>Future Recommendations</h3>
<p>We have realizations that could be further improved in future endeavors:</p>
<ol>
<li>We suggest the development of Natural Language Processing steps designed for Filipino texts. The meaning of the tweets could be lost if we analyzed our data in English instead of Filipino, additional insight or definitive conclusions could be missed.</li>
<li>We would encourage the use of Filipino in presenting future data science projects to include the common Filipinos in the scope of our target audience. Raising awareness about misinformation and sharing our findings are most beneficial if shared with public using our national language.</li>
<li>In continuing this topic, we suggest the creation of a structured classification for appeals to logic, emotion, and credibility for better explanation on how the tweets were categorized. </li>
</ol>
</section>
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