Social Interaction Design (SxD) at https://gravity7.com/, ex CX/UX/Digital Transformation Deloitte Digital, Stanford IR, Born Edinburgh Lived Berlin. SF. Guitar, Cycling, Photography, Film, Philosophy.
Interested in the intersection of AI and social interaction design - where AI and social behaviors meet on social platforms and around common web social features.
Some ChatGPT thoughts on whether bi-directional prompting could be a thing, namely, for UX
https://medium.com/@gravity7/bi-directional-prompting-a82c02bf92c6
Community Interaction and Conflict on the Web
https://arxiv.org/abs/1803.03697
"Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. ... We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts."
Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users. Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities. Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts---such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used toreate early-warning systems for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities.
Gave it a read - I think the social amplification algos could be specific to user persona (we tried this at Klout ages ago). And to the type of social network you're hoping to cultivate.
A conversational network would use different metrics for amplification than one for personal influence, for example. Conversations want to be topical, fresh, current. People want attention, praise, and other influencers to compare themselves to.
And so on.
Frank Pasquale
Howard Rheingold