CommunityData:Twitch

We have an opportunity to study Twitch. For now, lets collect ideas and references that might be useful on this page.

Ideas

Community governance

How do streamers balance competing interests?

A list of possible interests:

• community expressiveness (free speech, interacting with the community)
• entertainment
• control over the community
• attracting / pleasing advertisers
• attracting / pleasing donors
• compliance with platform requirements
• attracting and maintaining a community

Streamer success

Can we predict which channels will take off?

Theories of streamer success:

• Density dependence (prediction: those among the first to stream a game or activity in the early stages of the game's popularity will succeed)
• Resource partitioning (prediction: those who differentiate from others streaming the same game or activity can also succeed)
• Density dependence and resource partitioning looking at viewer overlaps.
• Cross-site communities (those who active in another community will succeed (search gaming sites, forums, reddit, youtube, for evidence)
• Preferential attachment (prediction: those who get popular quicker get more popular)
• Stage / development model (What is important may vary along developmental trajectories)
• Mutualism: Streamers who enter mutualistic relationships with one another (have their communities spam their faces, advertise for one another, show the other's stream on their channel) will be more successful.

Interaction on Twitch

How does Twitch produce

Social computing papers about Twitch

• Play: Cultural Production on Twitch.tv
• Video Game Live-Streams with Enhanced Communication Channels: A Case Study
• on twitch: fostering participatory communities of play within live mixed media
• Pro and Anti-Social Behavior on Twitch Through Moderation and Example-Setting