anchor.diff:  
     <p><em>d<sub>v</sub></em>(<span class="blog-calculation-f1">F1</span>) = <span class="blog-calculation-agree">&lt;1, 0, 0&gt;</span> + <span class="blog-calculation-agree">&lt;1, 0, 0&gt;</span> + <span class="blog-calculation-disagree">&lt;0, 1, 0&gt;</span> = <span class="blog-calculation-agree blog-calculation-2-1-0">&lt;2, 1, 0&gt;</span></p>
   
  -  <p>Looks reasonable, right? It becomes more interesting when we add evidence. How does good evidence change the credibility calculation? One way to look at it is to say that good evidence is the same as a lot of people voting. Bad evidence is the same as only a few people voting.</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-calculation-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-calculation-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-calculation-agree">agreeing</span> with the <span class="blog-calculation-supporting">supporting Factlink</span>, and 300 people saying that the relation is <span class="blog-calculation-agree">relevant</span>.</p>
   
  
positive.code: 
    <h4>Epilogue</h4>
  
    <p>In the end, we didn’t gain enough traction with this product, and decided to focus on annotation. But we think the idea was quite intriguing. We hope that someday online communities can visibly work together to assess the credibility of statements using evidence. To call out misinformation. We hope to have inspired you.</p>
  
  <% end %>
  
negative.code: 
    <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-calculation-agree">true</span>, how <span class="blog-calculation-disagree">false</span>, or how <span class="blog-calculation-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-calculation-f1">Factlink F1</span> as <em>d</em>(<span class="blog-calculation-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>
  
  