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Mapping people and tags on Mastodon

In Mastodon Relationship Graphs, I showed how to use Steampipe to map the neighborhoods of the Mastodon network. when i use the word map Here, I’m channeling Denis Wood’s The Power of Maps:

Each map shows this … but not it’sand each map shows what it shows Here … but not the other way. This is not only unavoidable, but is due precisely to this self-interested selectivity, to this choice of word, sign, or aspect of the world. make a point—that the map is enabled to work.

The aspect chosen by these neighborhood maps is the increase—the Mastodon version of a retweet. One of the maps focuses on a selected instance that appears on the starting timeline. Shows people who belong to that instance and who drive beeps for people in the same or different instances.

The other map zooms out to show the momentum relationships between all the instances that appear on the starting timeline. This view would not be readable if it included people, so it omits them to focus on server-to-server relationships.

These maps represent (or as Denis Wood emphasizes, “re-present”) a set of beeps. They skip the original beeps that no one responds to, and they skip the responses too, to focus on building relationships. What about the answers? That would be a different map, one that might also be interesting to draw.

However, in the meantime, I came up with another map to display the tags that appear in the results of a Mastodon tag search, along with the accounts that use those tags. He proved his worth today when I was seeking perspectives on Section 230 of the Communications Decency Act. As you probably noticed, Section 230 is being reconsidered by the US Supreme Court. My understanding of the subject was out of date, I wanted to update it, and especially wanted to test whether Mastodon could provide a useful alternative to a conventional web search. .

One possibility offered by Mastodon: search for beeps that use the #Section230 label. Here are two ways to map the results of that search.

section 230 IDG

On the left is a conventional Mastodon view: a list of toots that match the query. In this case, the article you finally wanted to read appears way down on that list. The beep that announced it was from The Markup, “a nonprofit newsroom that investigates how powerful institutions are using technology to change our society.” The article, Section 230 Is a Load-Bearing Wall Falling Down? Is It Falling? transcribes part of a conversation with two lawyers I know to be reliable guides to Internet-related issues.

On the right is my Steampipe based Mastodon tag browser. Working with the same data, The Markup article appeared in a way that immediately caught my attention. The first thing that caught my attention was the conjunction of two labels: #section230 and #scot. Since the Supreme Court’s interest in Section 230 is what is driving the current news cycle, I wanted to hear from qualified legal scholars to discuss the court’s interest in Section 230. So the conjunction of labels was a milestone. significant.

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The map shows two nodes that connect to both #section230 and #scot. How did I choose between them? My previous familiarity with The Markup led me to click on that node and visit the Mastodon instance of Markup where I read the article.

If I had been following The Markup then, as I am now, I probably would have seen the article in the news feed to which I assigned The Markup account. But that wouldn’t have changed the experience of looking for the #section230 label. The relationship graph works by restating the results of that search. It omits the text of the toots that contain the tag, and the images in those toots, to highlight two aspects of the results list: people (or accounts) and tags. Contextualize those tags by plotting their relative frequency in the results list. And attach, to each tag node, a link to a new graph centered on that tag.

This “interested selectivity” allows the map to do its job: find accounts that use given labels. Like a tag node, an account node provides a link, in this case, to the account’s Mastodon home page. It also reports the description of the account via a property that appears on hover over the node. So if you weren’t familiar with The Markup, you could reveal its description without leaving the chart. Here is the query that adds that property to the node.

  query = ''

| note                |
|  Watching Big Tech. |

That query is embedded in another query that is joined via two Steampipe plugins: one that wraps the Mastodon API and one that queries RSS feeds. This is because, as stated in Mastodon, Steampipe and RSS, the RSS feeds provided by Mastodon for tags enrich the results available in the main API.

Enabling SQL to query various APIs in a common way is one of Steampipe’s core superpowers. Allowing such queries to form the nodes and edges of relationship graphs is another. Used together, these two superpowers enable maps that pick out what’s useful, omit what’s not, and thus “render” the information for a given purpose.

These series:

  1. Autonomy, pack size, friction, fanout and speed
  2. Mastodon, Steampipe and RSS
  3. Navigating the fediverse
  4. A Bloomberg terminal for Mastodon
  5. Create your own Mastodon UX
  6. Lists and people on Mastodon
  7. How many people on my Mastodon feed also tweeted today?
  8. Qualified Mastodon URLs per instance
  9. Mastodon Ratio Charts
  10. Working with Mastodon lists
  11. Images considered harmful (sometimes)
  12. Mapping the broader fediverse
  13. Protocols, APIs and conventions
  14. News in fediversa
  15. Mapping people and tags in Mastodon

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