# orderedItems[].object.content
How active are the vis.social users?

From 454 accounts 120 wrote at least one Toot. 21 published more than 10 Toots. 7.5 Toots on average by the active Tooters (Mammuts?).

https://vis.social/media/VDx-QKgxZ-qaH Jpqlvc

# orderedItems[].object.contentMap.en
How active are the vis.social users?

From 454 accounts 120 wrote at least one Toot. 21 published more than 10 Toots. 7.5 Toots on average by the active Tooters (Mammuts?).

https://vis.social/media/VDx-QKgxZ-qaH Jpqlvc

# orderedItems[].object.content
Updated vis.social network. Thanks to the feedback by @RFon.

New data, invisible circles, lighter arrows, splined text sizes. https://vis.social/media/nwcD4i0MHwf1-ameuN0

# orderedItems[].object.contentMap.en
Updated vis.social network. Thanks to the feedback by @RFon.

New data, invisible circles, lighter arrows, splined text sizes. https://vis.social/media/nwcD4i0MHwf1-ameuN0

# orderedItems[].object.content
@JohnMuyskens If you use the #introductions hashtag, it's easier for people to find the Toot.

# orderedItems[].object.contentMap.en
@JohnMuyskens If you use the #introductions hashtag, it's easier for people to find the Toot.

# orderedItems[].object.content
The Modularity algorithm tries to detect sub communities but there aren't really any, so it's not that useful. https://github.com/gephi/gephi/wiki/Mo dularity

# orderedItems[].object.contentMap.en
The Modularity algorithm tries to detect sub communities but there aren't really any, so it's not that useful. https://github.com/gephi/gephi/wiki/Mo dularity

# orderedItems[].object.content
@paulfree14 @scott It tries to detect sub communities, but isn't that useful because of how strongly everyone is connected. https://github.com/gephi/gephi/wiki/Mo dularity

# orderedItems[].object.contentMap.en
@paulfree14 @scott It tries to detect sub communities, but isn't that useful because of how strongly everyone is connected. https://github.com/gephi/gephi/wiki/Mo dularity

# orderedItems[].object.content
@pururun @scott I analyze social media for a living, but many of them are just for fun.

# orderedItems[].object.contentMap.en
@pururun @scott I analyze social media for a living, but many of them are just for fun.

# orderedItems[].object.content
Less information, more art. https://vis.social/media/DB9MEBEIOUogK bt3lxA

# orderedItems[].object.contentMap.en
Less information, more art. https://vis.social/media/DB9MEBEIOUogK bt3lxA

# orderedItems[].object.attachment[].name
Black background with neon green and neon pink points and lines.

# orderedItems[].object.content
@gka In that case, I will go with the 🔨.

# orderedItems[].object.contentMap.en
@gka In that case, I will go with the 🔨.

# orderedItems[].object.content
Filtered to all accounts with at least one follower and one following. (Sorry, @scott, you are not included because of how I collected the data.)

124 accounts with 1 241 follow relationships.

Most followed (88-18 incoming follows): @gka, @1wheel, @lisacrost, @moritz, @enjalot, @paulgb, @jburnmurdoch, @kristinHenry, @dominikus, @noahi, @chrisv, @tillnagel, @colorfuldata, @ru, @ale_zotta, @taylorbaldwin, @jprctr, @mahir, @sebg, @monfera, @darenw

https://vis.social/media/Sv_uYAB0Ax8Ca gJjRrQ

# orderedItems[].object.contentMap.en
Filtered to all accounts with at least one follower and one following. (Sorry, @scott, you are not included because of how I collected the data.)

124 accounts with 1 241 follow relationships.

Most followed (88-18 incoming follows): @gka, @1wheel, @lisacrost, @moritz, @enjalot, @paulgb, @jburnmurdoch, @kristinHenry, @dominikus, @noahi, @chrisv, @tillnagel, @colorfuldata, @ru, @ale_zotta, @taylorbaldwin, @jprctr, @mahir, @sebg, @monfera, @darenw

https://vis.social/media/Sv_uYAB0Ax8Ca gJjRrQ

# orderedItems[].object.content
405 accounts are connected through 2 597 follow relationships. On average each account follows 6,4 others.

# orderedItems[].object.contentMap.en
405 accounts are connected through 2 597 follow relationships. On average each account follows 6,4 others.

# orderedItems[].object.content
I made a quick network visualization of all vis.social users.

Each follower of @scott is a node, each follow relationship is an arrow. The more followers an account has, the bigger it is. Color by modularity, position by ForceAtlas2.

If you want to visualize your own network, my quick python script is here: https://gist.github.com/lucahammer/bb2 770114fe734b2c96250ada6df7ca5

https://vis.social/media/J0lxXYUXvvbxi 4vQivY

# orderedItems[].object.contentMap.en
I made a quick network visualization of all vis.social users.

Each follower of @scott is a node, each follow relationship is an arrow. The more followers an account has, the bigger it is. Color by modularity, position by ForceAtlas2.

If you want to visualize your own network, my quick python script is here: https://gist.github.com/lucahammer/bb2 770114fe734b2c96250ada6df7ca5

https://vis.social/media/J0lxXYUXvvbxi 4vQivY

# orderedItems[].object.attachment[].name
Every user is a point with varying size and each follow relationship is an arrow.

# orderedItems[].object.content
@gka @scott @DominikHaas @marianoguerra You can follow accounts on other mastodon instances as well. Which makes the home timeline even more diverse.

# orderedItems[].object.contentMap.en
@gka @scott @DominikHaas @marianoguerra You can follow accounts on other mastodon instances as well. Which makes the home timeline even more diverse.

# orderedItems[].object.content
@marianoguerra @DominikHaas @scott @gka Local Timeline doesn't show replies (but @scott s). If you want to see replies, you need to follow both involved accounts.

# orderedItems[].object.contentMap.en
@marianoguerra @DominikHaas @scott @gka Local Timeline doesn't show replies (but @scott s). If you want to see replies, you need to follow both involved accounts.

# orderedItems[].object.content
@spjpgrd I found sigma.js to be the most efficient one, so I use it for big network graphs. For smaller ones D3 works great as well, especially if one is used to it. https://bl.ocks.org/mbostock/4062045

# orderedItems[].object.contentMap.en
@spjpgrd I found sigma.js to be the most efficient one, so I use it for big network graphs. For smaller ones D3 works great as well, especially if one is used to it. https://bl.ocks.org/mbostock/4062045

# orderedItems[].object.content
@DominikHaas If you click on the setting icon at top of the column you can pin the column und a new one will appear on the right.

# orderedItems[].object.contentMap.en
@DominikHaas If you click on the setting icon at top of the column you can pin the column und a new one will appear on the right.

# orderedItems[].object.content
The local timeline is great to see Toots from all users on this instance, but you won't see replies there. For that you need to follow both accounts.

# orderedItems[].object.contentMap.en
The local timeline is great to see Toots from all users on this instance, but you won't see replies there. For that you need to follow both accounts.

# orderedItems[].object.content
@gka @scott The local timeline is useful as well.

# orderedItems[].object.contentMap.en
@gka @scott The local timeline is useful as well.

# orderedItems[].object.content
@kristinHenry You could subscribe to accounts from this instance with your mastodon.social account.

# orderedItems[].object.contentMap.en
@kristinHenry You could subscribe to accounts from this instance with your mastodon.social account.

# orderedItems[].object.content
#introductions

My focus is social media analysis. Example: Network visualization of all verified Twitter users: https://medium.com/startup-grind/analy zing-205-718-verified-twitter-users-cf0811781ac8 Smaller projects: https://twitter.com/i/moments/86051462 8845133825

I contribute to open source tools to collect Social Media data (eg. twecoll) and work on my own tool to easily analyse Twitter accounts: https://accountanalysis.lucahammer.com/

Most of my clients are from Germany (Neo Magazin Royale, Netzpolitik.org) Austria (die.socialisten, Digital Champion) and Switzerland (gfs.bern).

# orderedItems[].object.contentMap.en
#introductions

My focus is social media analysis. Example: Network visualization of all verified Twitter users: https://medium.com/startup-grind/analy zing-205-718-verified-twitter-users-cf0811781ac8 Smaller projects: https://twitter.com/i/moments/86051462 8845133825

I contribute to open source tools to collect Social Media data (eg. twecoll) and work on my own tool to easily analyse Twitter accounts: https://accountanalysis.lucahammer.com/

Most of my clients are from Germany (Neo Magazin Royale, Netzpolitik.org) Austria (die.socialisten, Digital Champion) and Switzerland (gfs.bern).

# orderedItems[].object.content
Hello from Germany.

# orderedItems[].object.contentMap.en
Hello from Germany.