Reputation System for Communication Structuring (might be interesting for SNET as well as other parties)
- Explore the communication graphs between team/group/channel/community members to identify trending topics vs. sentiment (and cognitive distortions?) about these topics
- Explore reputations of the community members treating these topics
- Assess the reliability of the topics vs. sentiment from perspective of reputations of the agents generating them
- Come up with framework of community member reputation assessment along with communication topic trending and sentiment assessment
- Run the experiments according to the above on
— simulation model
— Steemit/Twitter/Reddit/Telegram/etc.
- Have the above implemented in either
— a) https://github.com/aigents/aigents-java/blob/master/src/main/java/net/webstructor/peer/Reputationer.java (can have full support from @akolonin , need to learn Java, no simulation present)
— b) https://github.com/singnet/reputation (all in Python, simulation prototype is in-place but might need to get changed)
— c) combine both (RS engine from a, simulation from b)
Resources:
https://aigents.medium.com/ - whatever is found on Reputation and Sentiment
Reputation System for Communication Structuring (might be interesting for SNET as well as other parties)
— simulation model
— Steemit/Twitter/Reddit/Telegram/etc.
— a) https://github.com/aigents/aigents-java/blob/master/src/main/java/net/webstructor/peer/Reputationer.java (can have full support from @akolonin , need to learn Java, no simulation present)
— b) https://github.com/singnet/reputation (all in Python, simulation prototype is in-place but might need to get changed)
— c) combine both (RS engine from a, simulation from b)
Resources:
https://aigents.medium.com/ - whatever is found on Reputation and Sentiment