anchor.diff:  
     <h4>Calculating credibility</h4>
   
  -  <p>How can we use these votes and evidence to get to a summary of how credible a Factlink is? For this, we first introduce some math. We define a credibility distribution to be a set of four numbers: &lt;<em>true</em>, <em>false</em>, <em>unsure</em>&gt;, or &lt;<em>t</em>, <em>f</em>, <em>u</em>&gt;. These numbers represent fractions of how <span class="blog-fact-graph-agree">true</span>, how <span class="blog-fact-graph-disagree">false</span>, or how <span class="blog-fact-graph-unsure">unsure</span> a Factlink is. The size of each number represents how much “work” or “thinking” has gone into reaching this conclusion. For now, let’s just say it’s the number of votes.</p>
  -
  -  <p>We denote the credibility distribution of a <span class="blog-fact-graph-f1">Factlink F1</span> as <em>d</em>(<span class="blog-fact-graph-f1">F1</span>). The numbers <em>t</em>, <em>f</em>, and <em>u</em> correspond to green, red, and blue parts of the Factwheel. The number inside the Factwheel is the sum of those numbers, representing the total amount of votes, or the amount of “thinking” people have put in.</p>
  -
  -  <p>Examples:</p>
  -
  -  <ol>
  -    <li><em>d</em>(<span class="blog-fact-graph-factlink">“Generally agreed upon Factlink”</span>) = <span class="blog-fact-graph-agree">&lt;10000, 8, 13&gt;</span></li>
  -    <li><em>d</em>(<span class="blog-fact-graph-factlink">“Factlink with only few votes”</span>) = <span class="blog-fact-graph-agree blog-fact-graph-2-1-0">&lt;2, 1, 0&gt;</span></li>
  -    <li><em>d</em>(<span class="blog-fact-graph-factlink">“Factlink without any votes”</span>) = <span class="blog-fact-graph-unsure">&lt;0, 0, 0&gt;</span></li>
  -    <li><em>d</em>(<span class="blog-fact-graph-factlink">“Highly controversial Factlink”</span>) = <span class="blog-fact-graph-undetermined blog-fact-graph-7839-8009-3041">&lt;7839, 8009, 3041&gt;</span></li>
  -    <li><em>d</em>(<span class="blog-fact-graph-factlink">“Vague or unknown Factlink”</span>) = <span class="blog-fact-graph-unsure">&lt;182, 119, 1205&gt;</span></li>
  -  </ol>
  -
  -  <p>To be a bit more precise, the voting part of <span class="blog-fact-graph-f1">F1</span>’s credibility distribution <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) is the sum of the credibility distribution of individual votes. An “agree” vote corresponds to <span class="blog-fact-graph-agree">&lt;1, 0, 0&gt;</span>, a “disagree” vote to <span class="blog-fact-graph-disagree">&lt;0, 1, 0&gt;</span> and an “unsure” vote to <span class="blog-fact-graph-unsure">&lt;0, 0, 1&gt;</span>.</p>
  -
  -  <p>To calculate the sum of different votes, we need to define addition on credibility distributions. An intuitive choice would be to just add up the numbers:</p>
  +  <p>How can we use these votes and evidence to get to a summary of how credible a Factlink is? For this, we first introduce some math. We define a <em>credibility tuple</em> to be a tuple of three numbers: &lt;<em>true</em>, <em>false</em>, <em>unsure</em>&gt;, or &lt;<em>t</em>, <em>f</em>, <em>u</em>&gt;. These numbers represent how <span class="blog-fact-graph-agree">true</span>, how <span class="blog-fact-graph-disagree">false</span>, or how <span class="blog-fact-graph-unsure">unsure</span> a Factlink is. The size of each number represents how much “work” or “thinking” has gone into reaching this conclusion. For now, let’s just say it’s the number of votes.</p>
  +
  +  <p>We denote the credibility tuple of a <span class="blog-fact-graph-f1">Factlink F1</span> as <em>c</em>(<span class="blog-fact-graph-f1">F1</span>). The numbers <em>t</em>, <em>f</em>, and <em>u</em> correspond to green, red, and blue parts of the Factwheel. The number inside the Factwheel is the sum of those numbers, representing the total amount of votes, or the amount of “thinking” people have put in. For example:</p>
  +
  +  <ol>
  +    <li><em>c</em>(<span class="blog-fact-graph-factlink">“Generally agreed upon Factlink”</span>) = <span class="blog-fact-graph-agree">&lt;10000, 8, 13&gt;</span></li>
  +    <li><em>c</em>(<span class="blog-fact-graph-factlink">“Factlink with only few votes”</span>) = <span class="blog-fact-graph-agree blog-fact-graph-2-1-0">&lt;2, 1, 0&gt;</span></li>
  +    <li><em>c</em>(<span class="blog-fact-graph-factlink">“Factlink without any votes”</span>) = <span class="blog-fact-graph-unsure">&lt;0, 0, 0&gt;</span></li>
  +    <li><em>c</em>(<span class="blog-fact-graph-factlink">“Highly controversial Factlink”</span>) = <span class="blog-fact-graph-undetermined blog-fact-graph-7839-8009-3041">&lt;7839, 8009, 3041&gt;</span></li>
  +    <li><em>c</em>(<span class="blog-fact-graph-factlink">“Vague or unknown Factlink”</span>) = <span class="blog-fact-graph-unsure">&lt;182, 119, 1205&gt;</span></li>
  +  </ol>
  +
  +  <p>To be a bit more precise, the voting part of <span class="blog-fact-graph-f1">F1</span>’s credibility tuple <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) is the sum of the credibility tuple of individual votes. An “agree” vote corresponds to <span class="blog-fact-graph-agree">&lt;1, 0, 0&gt;</span>, a “disagree” vote to <span class="blog-fact-graph-disagree">&lt;0, 1, 0&gt;</span> and an “unsure” vote to <span class="blog-fact-graph-unsure">&lt;0, 0, 1&gt;</span>.</p>
  +
  +  <p>To calculate the sum of different votes, we need to define addition on credibility tuples. An intuitive choice would be to just add up the numbers:</p>
   
     <p>&lt;<em>t1</em>, <em>f1</em>, <em>u1</em>&gt; + &lt;<em>t2</em>, <em>f2</em>, <em>u2</em>&gt; = &lt;<em>t1</em>+<em>t2</em>, <em>f1</em>+<em>f2</em>, <em>u1</em>+<em>u2</em>&gt;</p>
   
     <p>Now, if you have a <span class="blog-fact-graph-f1">Factlink F1</span> with, say, 2 <span class="blog-fact-graph-agree">agreeing</span> votes, and 1 <span class="blog-fact-graph-disagree">disagreeing</span> vote, you would get:</p>
   
  -  <p><em>d<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree">&lt;1, 0, 0&gt;</span> + <span class="blog-fact-graph-agree">&lt;1, 0, 0&gt;</span> + <span class="blog-fact-graph-disagree">&lt;0, 1, 0&gt;</span> = <span class="blog-fact-graph-agree blog-fact-graph-2-1-0">&lt;2, 1, 0&gt;</span></p>
  +  <p><em>c<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree">&lt;1, 0, 0&gt;</span> + <span class="blog-fact-graph-agree">&lt;1, 0, 0&gt;</span> + <span class="blog-fact-graph-disagree">&lt;0, 1, 0&gt;</span> = <span class="blog-fact-graph-agree blog-fact-graph-2-1-0">&lt;2, 1, 0&gt;</span></p>
   
     <p>Looks reasonable, right? It becomes more interesting when we add evidence. How does useful evidence change the credibility calculation? One way to look at it is to say that useful evidence is the same as a lot of people voting. Bad evidence is the same as only a few people voting.</p>
   
  -  <p>For example, if you have <span class="blog-fact-graph-supporting">supporting evidence</span>, that a lot of people find relevant, and a lot of people think is true, then that could count as a big vote in favour of the Factlink, say <span class="blog-fact-graph-agree">&lt;300, 0, 0&gt;</span>. Such evidence would weigh the same as 300 people voting in favour, so there must be a darn good reason why it weighs so much. In our model there must be 300 people <span class="blog-fact-graph-agree">agreeing</span> with the <span class="blog-fact-graph-supporting">supporting Factlink</span>, and 300 people saying that the relation is <span class="blog-fact-graph-agree">relevant</span>.</p>
  -
  -  <p>So, the evidence part of a Factlink’s credibility distribution is the sum of the credibility distribution of its evidence. <span class="blog-fact-graph-supporting">Supporting evidence</span>, denoted as <span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span> (<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting">supporting</span> <span class="blog-fact-graph-f1">F1</span>), has a credibility distribution <em>d</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>). The same goes for <span class="blog-fact-graph-weakening">weakening evidence</span>, <span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>. Such a distribution is made up of the relevance votes on the evidence, and the credibility distribution of <span class="blog-fact-graph-f2">F2</span>:</p>
  -
  -  <p><em>d</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;<em>min</em>(<em>t1</em>-f1, <em>t2</em>-<em>f2</em>), 0, 0&gt;<br>
  -    <em>d</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;0, <em>min</em>(<em>t1</em>-f1, <em>t2</em>-<em>f2</em>), 0&gt;<br>
  -    where &lt;<em>t1</em>, <em>f1</em>, <em>u1</em>&gt; = <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) (up/downvotes on evidence)<br>
  -    and &lt;<em>t2</em>, <em>f2</em>, <em>u2</em>&gt; = <em>d</em>(<span class="blog-fact-graph-f2">F2</span>)</p>
  +  <p>For example, if you have <span class="blog-fact-graph-supporting">supporting evidence</span>, that a lot of people find relevant, and a lot of people think is true, then that could count as a big vote in favour of the Factlink, say <span class="blog-fact-graph-agree">&lt;300, 0, 0&gt;</span>. Such evidence would weigh the same as 300 people voting in favour, so there must be a darn good reason why it weighs so much. In our model there must be 300 people <span class="blog-fact-graph-agree">agreeing</span> with the <span class="blog-fact-graph-supporting">supporting Factlink</span>, and 300 people saying that the relation is <span class="blog-fact-graph-agree">relevant</span>. If either the credibility or relevance is lower, the evidence won’t weigh as much.</p>
  +
  +  <p>So, the evidence part of a Factlink’s credibility tuple is the sum of the credibility tuple of its evidence. <span class="blog-fact-graph-supporting">Supporting evidence</span>, denoted as <span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span> (<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting">supporting</span> <span class="blog-fact-graph-f1">F1</span>), has a credibility tuple <em>c</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>). The same goes for <span class="blog-fact-graph-weakening">weakening evidence</span>, <span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>. Such a tuple is made up of the relevance votes on the evidence, and the credibility tuple of <span class="blog-fact-graph-f2">F2</span>:</p>
  +
  +  <p><em>c</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;<em>min</em>(<em>t1</em>-f1, <em>t2</em>-<em>f2</em>), 0, 0&gt;<br>
  +    where &lt;<em>t1</em>, <em>f1</em>, <em>u1</em>&gt; = <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) (up/downvotes on evidence)<br>
  +    and &lt;<em>t2</em>, <em>f2</em>, <em>u2</em>&gt; = <em>c</em>(<span class="blog-fact-graph-f2">F2</span>)</p>
  +
  +  <p>Similarly, <em>c</em>(<span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;0, <em>min</em>(<em>t1</em>-f1, <em>t2</em>-<em>f2</em>), 0&gt;</p>
   
     <p>This ensures that we don’t puff up the importance of this complex opinion more than any of its constituent parts and bounds the information that can pass through evidence. It allows highly credible Factlinks to support or weaken other Factlinks, but requires matching relevance of the relationship to transmit their credibility.</p>
   
  -  <p>The final credibility distribution of a <span class="blog-fact-graph-f1">Factlink F1</span> is the sum of the voting part <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>), and of the evidence part <em>d<sub>e</sub></em>(<span class="blog-fact-graph-f1">F1</span>), which we define as the sum of distributions of evidence of <span class="blog-fact-graph-f1">F1</span>. An example:</p>
  -
  -  <p><span class="blog-fact-graph-f1">F1</span> = <span class="blog-fact-graph-factlink blog-fact-graph-f1">“peanut butter is great”</span>, <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-unsure blog-fact-graph-4-2-8">&lt;4, 2, 8&gt;</span><br>
  -    <span class="blog-fact-graph-f2">F2</span> = <span class="blog-fact-graph-factlink blog-fact-graph-f2">“peanut butter has great taste”</span>, <em>d</em>(<span class="blog-fact-graph-f2">F2</span>) = <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f2">F2</span>) = <span class="blog-fact-graph-agree blog-fact-graph-10-3-0">&lt;10, 3, 0&gt;</span><br>
  -    <span class="blog-fact-graph-f3">F3</span> = <span class="blog-fact-graph-factlink blog-fact-graph-f3">“peanut butter has awful texture”</span>, <em>d</em>(<span class="blog-fact-graph-f3">F3</span>) = <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f3">F3</span>) = <span class="blog-fact-graph-agree blog-fact-graph-6-4-2">&lt;6, 4, 2&gt;</span><br>
  -    <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree blog-fact-graph-11-2-0">&lt;11, 2, 0&gt;</span><br>
  -    <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree blog-calulcation-9-3-0">&lt;9, 3, 0&gt;</span></p>
  +  <p>The final credibility tuple of a <span class="blog-fact-graph-f1">Factlink F1</span> is the sum of the voting part <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>), and of the evidence part <em>c<sub>e</sub></em>(<span class="blog-fact-graph-f1">F1</span>), which we define as the sum of tuples of evidence of <span class="blog-fact-graph-f1">F1</span>. An example:</p>
  +
  +  <p><span class="blog-fact-graph-f1">F1</span> = <span class="blog-fact-graph-factlink blog-fact-graph-f1">“peanut butter is great”</span>, <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-unsure blog-fact-graph-4-2-8">&lt;4, 2, 8&gt;</span><br>
  +    <span class="blog-fact-graph-f2">F2</span> = <span class="blog-fact-graph-factlink blog-fact-graph-f2">“peanut butter has great taste”</span>, <em>c</em>(<span class="blog-fact-graph-f2">F2</span>) = <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f2">F2</span>) = <span class="blog-fact-graph-agree blog-fact-graph-10-3-0">&lt;10, 3, 0&gt;</span><br>
  +    <span class="blog-fact-graph-f3">F3</span> = <span class="blog-fact-graph-factlink blog-fact-graph-f3">“peanut butter has awful texture”</span>, <em>c</em>(<span class="blog-fact-graph-f3">F3</span>) = <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f3">F3</span>) = <span class="blog-fact-graph-agree blog-fact-graph-6-4-2">&lt;6, 4, 2&gt;</span><br>
  +    <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree blog-fact-graph-11-2-0">&lt;11, 2, 0&gt;</span><br>
  +    <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree blog-calulcation-9-3-0">&lt;9, 3, 0&gt;</span></p>
   
     <p>Then:<br>
  -    <em>d</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;<em>min</em>(11-2, 10-3), 0, 0&gt; = <span class="blog-fact-graph-agree">&lt;7, 0, 0&gt;</span><br>
  -    <em>d</em>(<span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;0, <em>min</em>(9-3, 6-4), 0&gt; = <span class="blog-fact-graph-disagree">&lt;0, 2, 0&gt;</span><br>
  -    So <em>d<sub>e</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <em>d</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) + <em>d</em>(<span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree blog-fact-graph-7-2-0">&lt;7, 2, 0&gt;</span><br>
  -    So <em>d</em>(<span class="blog-fact-graph-f1">F1</span>) = <em>d<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) + <em>d<sub>e</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-unsure blog-fact-graph-4-2-8">&lt;4, 2, 8&gt;</span> + <span class="blog-fact-graph-agree blog-fact-graph-7-2-0">&lt;7, 2, 0&gt;</span> = <b><span class="blog-fact-graph-agree blog-calulcation-11-4-8">&lt;11, 4, 8&gt;</span></b></p>
  +    <em>c</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;<em>min</em>(11-2, 10-3), 0, 0&gt; = <span class="blog-fact-graph-agree">&lt;7, 0, 0&gt;</span><br>
  +    <em>c</em>(<span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = &lt;0, <em>min</em>(9-3, 6-4), 0&gt; = <span class="blog-fact-graph-disagree">&lt;0, 2, 0&gt;</span><br>
  +    So <em>c<sub>e</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <em>c</em>(<span class="blog-fact-graph-f2">F2</span> <span class="blog-fact-graph-supporting-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) + <em>c</em>(<span class="blog-fact-graph-f3">F3</span> <span class="blog-fact-graph-weakening-arrow">⟶</span> <span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-agree blog-fact-graph-7-2-0">&lt;7, 2, 0&gt;</span><br>
  +    So <em>c</em>(<span class="blog-fact-graph-f1">F1</span>) = <em>c<sub>v</sub></em>(<span class="blog-fact-graph-f1">F1</span>) + <em>c<sub>e</sub></em>(<span class="blog-fact-graph-f1">F1</span>) = <span class="blog-fact-graph-unsure blog-fact-graph-4-2-8">&lt;4, 2, 8&gt;</span> + <span class="blog-fact-graph-agree blog-fact-graph-7-2-0">&lt;7, 2, 0&gt;</span> = <b><span class="blog-fact-graph-agree blog-calulcation-11-4-8">&lt;11, 4, 8&gt;</span></b></p>
   
     <p>When now looking at <span class="blog-fact-graph-f1">F1</span>, you will see that the prevailing opinion is that peanut butter is great, even though the majority of people didn’t vote directly in agreement with that. However, quite a few people agreed with the underlying <span class="blog-fact-graph-supporting">supporting evidence</span>, and enough people agreed that the evidence was <span class="blog-fact-graph-agree">relevant</span>.</p>
   
  -  <p>The kicker is that this final credibility distribution of <span class="blog-fact-graph-f1">F1</span> is used when <span class="blog-fact-graph-f1">F1</span> is used as evidence for other Factlinks, allowing votes to propagate throughout the network of Factlinks and evidence.</p>
  +  <p>The kicker is that this final credibility tuple of <span class="blog-fact-graph-f1">F1</span> is used when <span class="blog-fact-graph-f1">F1</span> is used as evidence for other Factlinks, allowing votes to propagate throughout the network of Factlinks and evidence.</p>
   
   
     <h4>Variations</h4>
  
positive.code: 
    <p>However, truth is not a popularity contest. Prevailing “truths” have often turned out not to be very true at all: the earth “was” once flat. So what is more powerful than opinions, what is a better predictor of truth? Hard evidence.</p>
  
    <p>The key idea of our credibility calculation was evidence. You could add Factlinks as evidence for another Factlink. This evidence could be either <span class="blog-fact-graph-supporting">supporting</span> or <span class="blog-fact-graph-weakening">weakening</span>. Then, if the <span class="blog-fact-graph-supporting">supporting Factlink</span> was likely to be <span class="blog-fact-graph-agree">true</span>, then surely the Factlink it supported was also more likely to be <span class="blog-fact-graph-agree">true</span>. If a <span class="weakening">weakening Factlink</span> was likely to be <span class="blog-fact-graph-agree">true</span>, then surely the Factlink it weakened was more likely to be <span class="blog-fact-graph-disagree">false</span>. We called this network of evidence the “Fact Graph”.</p>
  
    <p>An example. <span class="blog-fact-graph-f1">Factlink 1 (F1)</span> is: <span class="blog-fact-graph-factlink blog-fact-graph-f1">“Working from home is more productive than working in an office.”</span> You could find some evidence on a website that talks about personal experience, and annotate <span class="blog-fact-graph-f2">F2</span>: <span class="blog-fact-graph-factlink blog-fact-graph-f2">“I’m much happier working from home, so I work harder.”</span> You can add <span class="blog-fact-graph-f2">F2</span> as <span class="blog-fact-graph-supporting">supporting evidence</span> to <span class="blog-fact-graph-f1">F1</span>. If it turns out that <span class="blog-fact-graph-f2">F2</span> is <span class="blog-fact-graph-agree">very credible</span> &mdash; many people voted that they agree, or it gets a lot of supporting evidence &mdash; then in turn <span class="blog-fact-graph-f1">F1</span> will also become <span class="blog-fact-graph-agree">more credible</span>. The green part of the Factwheel of <span class="blog-fact-graph-f1">F1</span> will become bigger.</p>
  
  
negative.code: 
    <p>What if someone adds nonsense <span class="blog-fact-graph-supporting">supporting evidence</span>, <span class="blog-fact-graph-f4">F4</span>: <span class="blog-fact-graph-factlink blog-fact-graph-f4">“The sky is blue.”</span> Someone can do this to falsely increase or decrease credibility of <span class="blog-fact-graph-f1">F1</span>, especially if <span class="blog-fact-graph-f4">F4</span> itself is <span class="blog-fact-graph-agree">very credible</span>. We could allow users to flag such invalid arguments, and have them removed by a moderator, or if enough people flag it.</p>
  
    <p>But it might also be that evidence that looks like nonsense, is in fact relevant. It might just be that you don’t understand the subject matter well enough, or that it needs further explaining. Relevance isn’t binary &mdash; either relevant or irrelevant &mdash; but it’s a scale. We are not just interested in the credibility of Factlinks, but also in the credibility of relations between Factlinks.</p>
  
    <p>In the same way you can vote on the credibility of Factlinks, you can vote on the credibility of the relations between Factlinks, evidence. In an attempt to avoid confusion, we call this voting on relevance. You can <span class="blog-fact-graph-agree">agree</span> or <span class="blog-fact-graph-disagree">disagree</span> that evidence is relevant.</p>
  
  