<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>kixiQu</author><text>I went and got my laptop to type up a reply to this:&lt;p&gt;Instagram&amp;#x27;s tagging system was and is atrocious &lt;i&gt;in combination with their discovery mechanisms&lt;/i&gt; and the incentives they create.&lt;p&gt;A real example, this has been true for years: I want to look at pictures of Jennifer Lawrence&amp;#x27;s makeup because she, like me, has hooded eyes and that makes useful reference. I go to instagram imagining that I will find fan accounts posting pictures. I search for #jenniferlawrence. 2.8 million posts. Nice.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.instagram.com&amp;#x2F;explore&amp;#x2F;tags&amp;#x2F;jenniferlawrence&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.instagram.com&amp;#x2F;explore&amp;#x2F;tags&amp;#x2F;jenniferlawrence&amp;#x2F;&lt;/a&gt;&lt;p&gt;Only three of the top nine pictures present there today (same on mobile for me) are &lt;i&gt;of Jennifer Lawrence.&lt;/i&gt;&lt;p&gt;This is what happens when all the engagement is counted in one big engagement bucket, all eyeballs are equal eyeballs, and all likes are equal likes. There are two gorgeous pictures of Anne Hathaway here, and I&amp;#x27;m sure they&amp;#x27;ve gotten great engagement, but they are absolute shit &lt;i&gt;at being pictures of Jennifer Lawrence&lt;/i&gt;. So now I have to scroll through a ton of absolutely irrelevant nonsense – with attendant ads, let&amp;#x27;s not forget the ads – if I actually want to use this tag for its sole raison d&amp;#x27;etre.&lt;p&gt;For contrast, consider what happens when a picture is cross-posted on Reddit. If I upvote it in one subreddit, I am giving an engagement signal &lt;i&gt;specific to that subreddit&lt;/i&gt; – and I as one user may choose to downvote that same picture in another if I think it doesn&amp;#x27;t fit there!&lt;p&gt;This isn&amp;#x27;t limited to low-effort reposting of professional photographers&amp;#x27; work. It happens in less egregious ways for many, many tags in many areas I&amp;#x27;ve seen on IG for quite a few years now (though I can&amp;#x27;t speak for the whole history of the app). Artists tagging media they&amp;#x27;re not using, etc. etc.</text><parent_chain><item><author>errantmind</author><text>Instagram&amp;#x27;s tagging system was actually really effective at categorizing content and discovery because each hashtag was treated as a node in a (giant) graph, where each node has multiple properties, including post count (number of posts using a tag), &amp;#x27;velocity&amp;#x27; (number of posts using a particular tag per unit time), etc. I could write up a big post about it as I made a study of it in when I created a web app for finding the most relevant tags a few years ago.&lt;p&gt;All that to say there was a lot to their system and it worked because users became aware that they were rewarded for using the most relevant tags. Using irrelevant tags was punished. This guided users towards using a mix of relevant popular and niche tags to maximize their reach, which, in turn, further improved the tagging system.&lt;p&gt;Instagram&amp;#x27;s tagging system isn&amp;#x27;t as important anymore as their algorithm has deemphasized it, in favor of other methods for classification and discovery, but there were a couple of golden years where it worked very well. Most users still look back on those years as the &amp;#x27;good times&amp;#x27; even if they don&amp;#x27;t know exactly why. I&amp;#x27;d go so far as to say they ruined the app after they deemphasized tags (and added way too many ads)</text></item></parent_chain></comment><story><title>I am endlessly fascinated with content tagging systems</title><url>https://twitter.com/hillelogram/status/1534301374166474752</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>OJFord</author><text>I&amp;#x27;d also be interested in reading about that - in particular are things like &amp;#x27;post count&amp;#x27; computed and stored (i.e. not normalised)? How do you cope with keeping such analytics current as the source changes?&lt;p&gt;(When oh when will postgres get automatic and incremental materialised views?)</text><parent_chain><item><author>errantmind</author><text>Instagram&amp;#x27;s tagging system was actually really effective at categorizing content and discovery because each hashtag was treated as a node in a (giant) graph, where each node has multiple properties, including post count (number of posts using a tag), &amp;#x27;velocity&amp;#x27; (number of posts using a particular tag per unit time), etc. I could write up a big post about it as I made a study of it in when I created a web app for finding the most relevant tags a few years ago.&lt;p&gt;All that to say there was a lot to their system and it worked because users became aware that they were rewarded for using the most relevant tags. Using irrelevant tags was punished. This guided users towards using a mix of relevant popular and niche tags to maximize their reach, which, in turn, further improved the tagging system.&lt;p&gt;Instagram&amp;#x27;s tagging system isn&amp;#x27;t as important anymore as their algorithm has deemphasized it, in favor of other methods for classification and discovery, but there were a couple of golden years where it worked very well. Most users still look back on those years as the &amp;#x27;good times&amp;#x27; even if they don&amp;#x27;t know exactly why. I&amp;#x27;d go so far as to say they ruined the app after they deemphasized tags (and added way too many ads)</text></item></parent_chain></comment><story><title>I am endlessly fascinated with content tagging systems</title><url>https://twitter.com/hillelogram/status/1534301374166474752</url></story>