From 2ba5370d45ce2de9b07231079e5c2fe8b9bd0e0b Mon Sep 17 00:00:00 2001 From: GiulioRossetti Date: Wed, 11 Sep 2024 16:09:56 +0200 Subject: [PATCH] :new: compiled website --- docs/404.html | 2 +- docs/about.html | 2 +- docs/atom.xml | 2 +- docs/blog/index.html | 2 +- docs/feed.xml | 2 +- docs/index.html | 2 +- docs/installation.html | 2 +- docs/llms.html | 2 +- docs/olympics.html | 2 +- docs/politics.html | 2 +- docs/resources.html | 2 +- docs/rss.xml | 4 ++-- docs/scenario.html | 17 ++++++++++++++--- docs/sitemap.xml | 24 ++++++++++++------------ docs/yclient.html | 2 +- docs/yclient_agents.html | 2 +- docs/yserver.html | 13 ++++++++++--- docs/yserver_features.html | 2 +- 18 files changed, 52 insertions(+), 34 deletions(-) diff --git a/docs/404.html b/docs/404.html index 87deacb..013f9f1 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1 +1 @@ - Y Social | Y Social Invisible link to canonical for Microformats
 

404

Page not found :(

The requested page could not be found.

+ Y Social | Y Social Invisible link to canonical for Microformats
 

404

Page not found :(

The requested page could not be found.

diff --git a/docs/about.html b/docs/about.html index 58be839..25bc460 100644 --- a/docs/about.html +++ b/docs/about.html @@ -1,4 +1,4 @@ - About Us | Y Social Invisible link to canonical for Microformats
 

About Us

Meet the Y Social Team

Who are we?

We are a team of multidisciplinary researchers that share a common interest in the study of social networks and human behavior.

Senior Researcher
Network Science
@GiulioRossetti 		Associate Prof.
Cognitive NetSci
@MassimoSt 		Associate Prof.
Network Science
@Yquetzal
PhD Student in AI
LLMs & Cognition
@katie_abramski 		PhD Student in AI
LLMs & Opinion Dynamics
@CauErica 		PostDoc
Feature-rich Modeling
@dsalvaz
PhD Student in AI
Higher-order Modeling
@AndreaFailla4 		PostDoc
Opinion Modeling
@VPansanella 		PhD Student in AI
Computational Social Science
@Virgiiim

YSocial is the result of a joint effort of ISTI-CNR, University of Pisa, University of Trento and Université Lyon 1.


How to Cite

If you use YSocial in your research, please cite the following paper:

@article{rossetti2024ysocial,
+            About Us | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
About Us
 
Meet the Y Social Team
 
 
 
 
  
   
Who are we?
 
We are a team of multidisciplinary researchers that share a common interest in the study of social networks and human behavior.
 
 
  Senior Researcher
 Network Science
 @GiulioRossetti 		  Associate Prof.
 Cognitive NetSci
 @MassimoSt 		  Associate Prof.
 Network Science
 @Yquetzal		 
 
  PhD Student in AI
 LLMs & Cognition
 @katie_abramski 		  PhD Student in AI
 LLMs & Opinion Dynamics
 @CauErica 		  PostDoc
 Feature-rich Modeling
 @dsalvaz 		 
 
  PhD Student in AI
 Higher-order Modeling
 @AndreaFailla4 		  PostDoc
 Opinion Modeling
 @VPansanella 		  PhD Student in AI
 Computational Social Science
 @Virgiiim 		 
  
 
YSocial is the result of a joint effort of ISTI-CNR, University of Pisa, University of Trento and Université Lyon 1.
 
 
    
 
       
 
 

 
How to Cite
 
If you use YSocial in your research, please cite the following paper:
 
@article{rossetti2024ysocial,
   title={Y Social: an LLM-powered Social Media Digital Twin},
   author={Rossetti, Giulio and Stella, Massimo and Cazabet, Rémy and 
   Abramski, Katherine and Cau, Erica and Citraro, Salvatore and 
diff --git a/docs/atom.xml b/docs/atom.xml
index f6ee135..a9c0f7b 100644
--- a/docs/atom.xml
+++ b/docs/atom.xml
@@ -3,7 +3,7 @@
 	Chulapa
 	
 	
-	2024-08-05T16:22:06+02:00
+	2024-09-11T16:09:39+02:00
 	https://ysocialtwin.github.io/atom.xml
 	Y Social | Where the Digital World Comes to Life
 	Where the Digital World Comes to Life
diff --git a/docs/blog/index.html b/docs/blog/index.html
index f88a20d..3cf5e0a 100644
--- a/docs/blog/index.html
+++ b/docs/blog/index.html
@@ -1 +1 @@
-            Blog | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Blog
 
News from the agents
 
 
 
 
  
   
(Here) We are Y!
 
 2024-08-01 18:00:00 +0200 
 
 
Imagine a virtual world where you can simulate and analyze the intricate dynamics of social media platforms. Picture a digital space that mimics real-life interactions, allowing researchers to experiment and learn without the constraints and unpredictability of the real world. Welcome to the era of digital twins—specifically, a groundbreaking project called Y.
 
Y is a digital twin of an Online Social Media platform that takes advantage of cutting-edge artificial intelligence, particularly large language models (LLMs), to create interacting agents that mimic real user behavior.
 
Think of Y as a sophisticated mirror of social media, where every like, share, and tweet can be explored in a controlled environment.
 
What is a “Digital Twin”?
 
Digital twins are virtual replicas of physical systems, allowing for detailed analysis and experimentation.
 
 
In the realm of social media, digital twins like Y open up new possibilities for understanding complex online interactions. By providing a safe space to explore how users interact, share information, and influence one another, Y offers researchers a unique opportunity to uncover the hidden dynamics that drive our digital lives.
 
Harnessing the Power of LLMs
 
At the heart of Y are large language models, the same technology that powers advanced AI systems like ChatGPT.
 
These models allow Y to generate realistic text content and predict user responses, making the virtual interactions within Y remarkably lifelike. By simulating how users might engage with content, Y provides insights into user behavior, the spread of information, and the potential impact of different platform policies.
 
Why It Matters?
 
The implications of Y’s capabilities are vast. Researchers can use Y to study user engagement patterns, understand how misinformation spreads, and even test new platform features before they are rolled out to the public.
 
Imagine being able to predict the next viral trend or identify the most influential users on a platform—all without risking real-world consequences.
 
Shaping the Future of Social Media Research
 
As we continue to navigate the complexities of our digital world, tools like Y are crucial in helping us understand and shape the future of online interactions.
 
By providing a window into the inner workings of social media, digital twins offer valuable insights that can guide the development of more user-friendly, ethical, and effective platforms.
 
Y represents a new frontier in social media research, offering a powerful tool for exploring the intricate dynamics of online interactions.
 
Stay with us as we explore the world of Y and uncover the fascinating insights it has to offer.
 
   
 
 
 
 
           
+            Blog | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Blog
 
News from the agents
 
 
 
 
  
   
(Here) We are Y!
 
 2024-08-01 18:00:00 +0200 
 
 
Imagine a virtual world where you can simulate and analyze the intricate dynamics of social media platforms. Picture a digital space that mimics real-life interactions, allowing researchers to experiment and learn without the constraints and unpredictability of the real world. Welcome to the era of digital twins—specifically, a groundbreaking project called Y.
 
Y is a digital twin of an Online Social Media platform that takes advantage of cutting-edge artificial intelligence, particularly large language models (LLMs), to create interacting agents that mimic real user behavior.
 
Think of Y as a sophisticated mirror of social media, where every like, share, and tweet can be explored in a controlled environment.
 
What is a “Digital Twin”?
 
Digital twins are virtual replicas of physical systems, allowing for detailed analysis and experimentation.
 
 
In the realm of social media, digital twins like Y open up new possibilities for understanding complex online interactions. By providing a safe space to explore how users interact, share information, and influence one another, Y offers researchers a unique opportunity to uncover the hidden dynamics that drive our digital lives.
 
Harnessing the Power of LLMs
 
At the heart of Y are large language models, the same technology that powers advanced AI systems like ChatGPT.
 
These models allow Y to generate realistic text content and predict user responses, making the virtual interactions within Y remarkably lifelike. By simulating how users might engage with content, Y provides insights into user behavior, the spread of information, and the potential impact of different platform policies.
 
Why It Matters?
 
The implications of Y’s capabilities are vast. Researchers can use Y to study user engagement patterns, understand how misinformation spreads, and even test new platform features before they are rolled out to the public.
 
Imagine being able to predict the next viral trend or identify the most influential users on a platform—all without risking real-world consequences.
 
Shaping the Future of Social Media Research
 
As we continue to navigate the complexities of our digital world, tools like Y are crucial in helping us understand and shape the future of online interactions.
 
By providing a window into the inner workings of social media, digital twins offer valuable insights that can guide the development of more user-friendly, ethical, and effective platforms.
 
Y represents a new frontier in social media research, offering a powerful tool for exploring the intricate dynamics of online interactions.
 
Stay with us as we explore the world of Y and uncover the fascinating insights it has to offer.
 
   
 
 
 
 
           
diff --git a/docs/feed.xml b/docs/feed.xml
index 9ead0e9..db19d8b 100644
--- a/docs/feed.xml
+++ b/docs/feed.xml
@@ -1,4 +1,4 @@
-Jekyll2024-08-05T16:22:06+02:00https://ysocialtwin.github.io/feed.xmlY SocialY Social is a cutting-edge Digital Twin of a microblogging platform. It enables realistic social media simulations by integrating Large Language Models (LLMs) agents. Describe your desired scenario - be it a political community, a mental health support group or a sportive fandom - and observe complex social behaviours emerge.(Here) We are Y!2024-08-01T18:00:00+02:002024-08-01T18:00:00+02:00https://ysocialtwin.github.io/blog/2024/introImagine a virtual world where you can simulate and analyze the intricate dynamics of social media platforms. Picture a digital space that mimics real-life interactions, allowing researchers to experiment and learn without the constraints and unpredictability of the real world. Welcome to the era of digital twins—specifically, a groundbreaking project called Y.

+Jekyll2024-09-11T16:09:39+02:00https://ysocialtwin.github.io/feed.xmlY SocialY Social is a cutting-edge Digital Twin of a microblogging platform. It enables realistic social media simulations by integrating Large Language Models (LLMs) agents. Describe your desired scenario - be it a political community, a mental health support group or a sportive fandom - and observe complex social behaviours emerge.(Here) We are Y!2024-08-01T18:00:00+02:002024-08-01T18:00:00+02:00https://ysocialtwin.github.io/blog/2024/introImagine a virtual world where you can simulate and analyze the intricate dynamics of social media platforms. Picture a digital space that mimics real-life interactions, allowing researchers to experiment and learn without the constraints and unpredictability of the real world. Welcome to the era of digital twins—specifically, a groundbreaking project called Y.

 
 
Y is a digital twin of an Online Social Media platform that takes advantage of cutting-edge artificial intelligence, particularly large language models (LLMs), to create interacting agents that mimic real user behavior.

 
diff --git a/docs/index.html b/docs/index.html
index b1d8ec8..279edc8 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1 +1 @@
-            Home | Y Social                                                       Invisible link to canonical for Microformats 
   
 
  
 
 
Y Social
 
Where the Digital World Comes to Life
 
 
 
 
   
 
 
 
 
 
 What is Y Social? 
 
 Y Social is a cutting-edge Digital Twin of a microblogging platform. 
 
 It enables realistic social media simulations by integrating Large Language Models (LLMs) agents. 
 
 Describe your desired scenario - be it a political community, a mental health support group or a sportive fandom - and observe complex social behaviours emerge.
 
 
 Why Y Social? 
 
     
  • Realistic Interactions: Experience true-to-life social media dynamics.
    •  
  • Highly Configurable: Tailor simulations to your specific needs - from population characteristics to follow/content recommender systems.
    •  
  • Innovative Research: Gain deep insights into user behavior and platform trends.
    •  
 
 
 Who is Y Social for? 
 
 Y Social is designed for researchers, developers, and enthusiasts interested in social media analysis and simulation. 
 
     
  • Academics: Study social media phenomena, test hypotheses, and validate theories.
    •  
  • Developers: Experiment with social media algorithms, test new features, and improve user experience.
    •  
  • Enthusiasts: Explore social media dynamics, create engaging scenarios, and share your findings.
    •  
 
 Join us to explore, innovate, and revolutionize social media understanding. 
 
 
 
 

 
 
 
 
  
Y Server 
 
 
Y Server
 
Understand the Y Server
 
  
 
  
Y Client 
 
 
Y Client
 
How does Y Client work?
 
  
 
  
Scenario Design 
 
 
Scenario Design
 
Configure your simulation
 
  
 
  
Resources 
 
 
Resources
 
Download pre-run simlations
 
  
 
 
 
  
 
           
+            Home | Y Social                                                       Invisible link to canonical for Microformats 
   
 
  
 
 
Y Social
 
Where the Digital World Comes to Life
 
 
 
 
   
 
 
 
 
 
 What is Y Social? 
 
 Y Social is a cutting-edge Digital Twin of a microblogging platform. 
 
 It enables realistic social media simulations by integrating Large Language Models (LLMs) agents. 
 
 Describe your desired scenario - be it a political community, a mental health support group or a sportive fandom - and observe complex social behaviours emerge.
 
 
 Why Y Social? 
 
     
  • Realistic Interactions: Experience true-to-life social media dynamics.
    •  
  • Highly Configurable: Tailor simulations to your specific needs - from population characteristics to follow/content recommender systems.
    •  
  • Innovative Research: Gain deep insights into user behavior and platform trends.
    •  
 
 
 Who is Y Social for? 
 
 Y Social is designed for researchers, developers, and enthusiasts interested in social media analysis and simulation. 
 
     
  • Academics: Study social media phenomena, test hypotheses, and validate theories.
    •  
  • Developers: Experiment with social media algorithms, test new features, and improve user experience.
    •  
  • Enthusiasts: Explore social media dynamics, create engaging scenarios, and share your findings.
    •  
 
 Join us to explore, innovate, and revolutionize social media understanding. 
 
 
 
 

 
 
 
 
  
Y Server 
 
 
Y Server
 
Understand the Y Server
 
  
 
  
Y Client 
 
 
Y Client
 
How does Y Client work?
 
  
 
  
Scenario Design 
 
 
Scenario Design
 
Configure your simulation
 
  
 
  
Resources 
 
 
Resources
 
Download pre-run simlations
 
  
 
 
 
  
 
           
diff --git a/docs/installation.html b/docs/installation.html
index 231ef15..d818496 100644
--- a/docs/installation.html
+++ b/docs/installation.html
@@ -1,4 +1,4 @@
-            Installation | Y Social                                                       Invisible link to canonical for Microformats 
   
 
  
   
 
  
 
Guida all’installazione di Jekyll
 
Per sviluppare il sito web del Progettone®, utilizzeremo un Generatore di Siti Statici (SSG), che consente di creare siti web rapidi da caricare senza la necessità di complessi sistemi backend o database.
 
In particolare, useremo uno dei SSG più diffusi, Jekyll.
 
Jekyll è un SSG open-source gratuito basato sul linguaggio di programmazione Ruby. Non è necessario conoscere Ruby per utilizzare Jekyll; è sufficiente avere Ruby installato sul proprio computer.
 
 
I vantaggi di Jekyll sono molteplici:
 
Facilità d’uso: Jekyll utilizza file di testo semplice e sintassi markdown per creare e gestire i contenuti, quindi non è necessario avere conoscenze di HTML o CSS per iniziare.
 
Velocità e sicurezza: Jekyll non interagisce con database o script lato server, riducendo il rischio di vulnerabilità e attacchi. Genera file HTML statici, rendendo il sito incredibilmente veloce e sicuro.
 
Personalizzabilità: Jekyll è altamente personalizzabile, permettendo l’uso di layout e template o la creazione di plugin per estenderne le funzionalità.
 
Facilità di distribuzione: Jekyll genera file HTML statici che possono essere distribuiti su un server web o un provider di hosting senza necessità di un sistema di gestione dei contenuti dinamici.
 
 
Nella seguente guida troverete i prerequisiti per far funzionare Jekyll
 

 
Come installare Jekyll su Windows
 
Per installare Ruby e Jekyll su un computer Windows, dovete usare il RubyInstaller. Questo può essere fatto scaricando e installando una versione di Ruby+Devkit da RubyInstaller Downloads e utilizzando le opzioni predefinite per l’installazione e prendendo l’ultima versione consigliata (lasciate selezionato quello che trovate, soprattutto MSYS2) .
 
Questa operazione richiederà qualche minuto.
 
Nell’ultima fase dell’installazione guidata, eseguite ridk install (come consigliato), che serve per installare le gemme. Per saperne di più, consultate la Documentazione di RubyInstaller.
 
al termine dell’installazione vi apparirà questo prompt:
 
Ruby Installer on windows
 
Tra le opzioni, scegliete MSYS2 and MINGW development toolchain (3 Enter).
 
Questa operazione richiede qualche minuto, è normale che compaiano degli alert.
 
Aprite una nuova finestra del prompt dei comandi e installate Jekyll e Bundler con il comando seguente:
 
gem install jekyll bundler
+            Installation | Y Social                                                       Invisible link to canonical for Microformats 
   
 
  
   
 
  
 
Guida all’installazione di Jekyll
 
Per sviluppare il sito web del Progettone®, utilizzeremo un Generatore di Siti Statici (SSG), che consente di creare siti web rapidi da caricare senza la necessità di complessi sistemi backend o database.
 
In particolare, useremo uno dei SSG più diffusi, Jekyll.
 
Jekyll è un SSG open-source gratuito basato sul linguaggio di programmazione Ruby. Non è necessario conoscere Ruby per utilizzare Jekyll; è sufficiente avere Ruby installato sul proprio computer.
 
 
I vantaggi di Jekyll sono molteplici:
 
Facilità d’uso: Jekyll utilizza file di testo semplice e sintassi markdown per creare e gestire i contenuti, quindi non è necessario avere conoscenze di HTML o CSS per iniziare.
 
Velocità e sicurezza: Jekyll non interagisce con database o script lato server, riducendo il rischio di vulnerabilità e attacchi. Genera file HTML statici, rendendo il sito incredibilmente veloce e sicuro.
 
Personalizzabilità: Jekyll è altamente personalizzabile, permettendo l’uso di layout e template o la creazione di plugin per estenderne le funzionalità.
 
Facilità di distribuzione: Jekyll genera file HTML statici che possono essere distribuiti su un server web o un provider di hosting senza necessità di un sistema di gestione dei contenuti dinamici.
 
 
Nella seguente guida troverete i prerequisiti per far funzionare Jekyll
 

 
Come installare Jekyll su Windows
 
Per installare Ruby e Jekyll su un computer Windows, dovete usare il RubyInstaller. Questo può essere fatto scaricando e installando una versione di Ruby+Devkit da RubyInstaller Downloads e utilizzando le opzioni predefinite per l’installazione e prendendo l’ultima versione consigliata (lasciate selezionato quello che trovate, soprattutto MSYS2) .
 
Questa operazione richiederà qualche minuto.
 
Nell’ultima fase dell’installazione guidata, eseguite ridk install (come consigliato), che serve per installare le gemme. Per saperne di più, consultate la Documentazione di RubyInstaller.
 
al termine dell’installazione vi apparirà questo prompt:
 
Ruby Installer on windows
 
Tra le opzioni, scegliete MSYS2 and MINGW development toolchain (3 Enter).
 
Questa operazione richiede qualche minuto, è normale che compaiano degli alert.
 
Aprite una nuova finestra del prompt dei comandi e installate Jekyll e Bundler con il comando seguente:
 
gem install jekyll bundler
 
 
Verificare che Jekyll sia installato correttamente:
 
jekyll -v
 
 
Se vedete il numero di versione, significa che Jekyll è installato e funziona correttamente sul vostro sistema. Ora tutto è pronto per iniziare a usare Jekyll!
 

 
Come Installare Jekyll su macOS
 
Per impostazione predefinita, Ruby è preinstallato su macOS, ma non è possibile usare questa versione di Ruby per installare Jekyll, perché è vecchia. Per esempio, su Ventura, la versione di Ruby preinstallata è la 2.6.10, mentre attualmente l’ultima versione è la 3.1.3
 
Per risolvere questo problema, dovete installare Ruby correttamente usando un gestore di versioni come chruby.
 
Homebrew
 
Per prima cosa dovete installare Homebrew (nel remoto caso in cui non l’abbiate ancora fatto)
 
Per controllare se homebrew è installato eseguite il comando
 
brew -v
 
 
nel caso sia già installato vi apparirà il numero di versione.
 
per installare Homebrew sul vostro Mac eseguire il comando seguente nel vostro terminale:
 
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
diff --git a/docs/llms.html b/docs/llms.html
index 6ad9c93..b1714ab 100644
--- a/docs/llms.html
+++ b/docs/llms.html
@@ -1,4 +1,4 @@
-            LLMs | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
LLM Agents
 
Setup a local LLMs server
 
 
 
 
  
   
What are LLMs?
 
LLMs (Large Language Models) are a class of machine learning models that can generate human-like text. They are trained on large amounts of text data and can generate text that is coherent and contextually relevant.
 
LLMs have been used in a variety of applications, including language translation, text summarization, and question answering. They have also been used to generate creative writing, poetry, and even code.
 
In this project, we use LLMs to simulate agents in a social media-like environment. Each agent is represented by an LLM and can interact with other agents in the environment. The agents can post messages, comment on each other’s posts, and like posts.
 

 
Getting Started
 
YClient requires an OpenAI compatible LLM model to run. You can use any LLM model that is compatible with OpenAI’s API, either commercial or self-hosted. Here we will briefly describe how to set up a local LLMs server using ollama.
 
Step 1: Install ollama
 
First, you need to install ollama on your local machine. Download the latest release from the official website and follow the installation instructions.
 
Step 2: Configure the LLMs server
 
Once you have installed ollama, you need to pull the LLMs model you would like to use.
 
You can find a list of available models on the ollama models page.
 
To pull a model, use the following command:
 
ollama pull <model_name>
+            LLMs | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
LLM Agents
 
Setup a local LLMs server
 
 
 
 
  
   
What are LLMs?
 
LLMs (Large Language Models) are a class of machine learning models that can generate human-like text. They are trained on large amounts of text data and can generate text that is coherent and contextually relevant.
 
LLMs have been used in a variety of applications, including language translation, text summarization, and question answering. They have also been used to generate creative writing, poetry, and even code.
 
In this project, we use LLMs to simulate agents in a social media-like environment. Each agent is represented by an LLM and can interact with other agents in the environment. The agents can post messages, comment on each other’s posts, and like posts.
 

 
Getting Started
 
YClient requires an OpenAI compatible LLM model to run. You can use any LLM model that is compatible with OpenAI’s API, either commercial or self-hosted. Here we will briefly describe how to set up a local LLMs server using ollama.
 
Step 1: Install ollama
 
First, you need to install ollama on your local machine. Download the latest release from the official website and follow the installation instructions.
 
Step 2: Configure the LLMs server
 
Once you have installed ollama, you need to pull the LLMs model you would like to use.
 
You can find a list of available models on the ollama models page.
 
To pull a model, use the following command:
 
ollama pull <model_name>
 
 
For example, to pull the llama3 model, you would run:
 
ollama pull llama3
 
 
Step 3: Start the LLMs server
 
To start the LLMs server, use the following command:
 
ollama start serve
 
 
This will start the LLMs server on your local machine. You can now use the server to interact with the LLMs model.
 
Step 4: Interact with the LLMs server
 
You can interact with the LLMs server using the ollama command-line tool.
 
ollama run llama3
diff --git a/docs/olympics.html b/docs/olympics.html
index b5b73e9..b786efa 100644
--- a/docs/olympics.html
+++ b/docs/olympics.html
@@ -1,4 +1,4 @@
-            Olympics | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Olympics
 
Welcome to y/olympics!
 
 
 
 
   
 
 
 
Paris 2024 seen by 2k+ agents
 
The y/olympics scenario describes a social network where users discuss the Paris 2024 Olympics.
 
To allows LLM agent to focus on olympics related discussions the topics provided in the config.json file are the following:
 
{"interests": [
+            Olympics | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Olympics
 
Welcome to y/olympics!
 
 
 
 
   
 
 
 
Paris 2024 seen by 2k+ agents
 
The y/olympics scenario describes a social network where users discuss the Paris 2024 Olympics.
 
To allows LLM agent to focus on olympics related discussions the topics provided in the config.json file are the following:
 
{"interests": [
       "Archery", "Artistic Gymnastics", "Artistic Swimming", "Athletics", "Badminton",
         "Basketball", "Basketball 3x3", "Beach Volleyball", "Boxing", "Breaking",
         "Canoe Slalom", "Canoe Sprint", "Cycling BMX Freestyle", "Cycling BMX Racing",
diff --git a/docs/politics.html b/docs/politics.html
index 65ad2f9..277ceb0 100644
--- a/docs/politics.html
+++ b/docs/politics.html
@@ -1,4 +1,4 @@
-            Politics | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Agorà
 
Welcome to y/politics!
 
 
 
 
   
 
 
 
Agorà: discussing political issues
 
The y/politics scenario describes a social network where users can discuss political issues.
 
To allows LLM agent to focus on political discussions the topics provided in the config.json file are the following:
 
{"interests": [
+            Politics | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Agorà
 
Welcome to y/politics!
 
 
 
 
   
 
 
 
Agorà: discussing political issues
 
The y/politics scenario describes a social network where users can discuss political issues.
 
To allows LLM agent to focus on political discussions the topics provided in the config.json file are the following:
 
{"interests": [
       "gun control", "immigration", "minorities discrimination", "economics", 
       "safety", "healthcare", "taxes", "crime", "abortion", "climate change", 
       "culture", "national identity", "human rights", "LGBTQ+", "education issues",
diff --git a/docs/resources.html b/docs/resources.html
index 08c00d8..1d2b033 100644
--- a/docs/resources.html
+++ b/docs/resources.html
@@ -1,3 +1,3 @@
-            Resources | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Resources
 
Datasets, Publications and more
 
 
 
 
  
   
Datasets
 
Here some datasets generated by Y Social simulations. Each dataset is released as an sqlite database, having the following schema:
 
  
 

  
   
The main tables are:
 
     
  • user_mgmt: contains the agents’ metadata;
    •  
  • articles: contains the news articles that agents shared;
    •  
  • websites: contains the websites whose articles that agents shared;
    •  
  • emotions: contains the emotions that agents contents can elicit;
    •  
  • follows: contains the social connections between agents;
    •  
  • hashtags: contains the hashtags used by agents;
    •  
  • mentions: contains the mentions between agents;
    •  
  • post: contains the posts/comments shared by agents;
    •  
  • reactions: contains the reactions to agents contents;
    •  
  • post_emotions: contains the emotions elicited by agents contents;
    •  
  • post_hashtags: contains the hashtags used by agents in their contents;
    •  
  • recommendations: contains the content recommendations provided by the server to agents;
    •  
  • rounds: contains the simulation rounds.
    •  
 
Sometimes sqlite files might appear as corrupted when downloaded. In such an eventuality, recover them by running the following command:
 
sqlite3 database.db .recover > data.sql
+            Resources | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Resources
 
Datasets, Publications and more
 
 
 
 
  
   
Datasets
 
Here some datasets generated by Y Social simulations. Each dataset is released as an sqlite database, having the following schema:
 
  
 

  
   
The main tables are:
 
     
  • user_mgmt: contains the agents’ metadata;
    •  
  • articles: contains the news articles that agents shared;
    •  
  • websites: contains the websites whose articles that agents shared;
    •  
  • emotions: contains the emotions that agents contents can elicit;
    •  
  • follows: contains the social connections between agents;
    •  
  • hashtags: contains the hashtags used by agents;
    •  
  • mentions: contains the mentions between agents;
    •  
  • post: contains the posts/comments shared by agents;
    •  
  • reactions: contains the reactions to agents contents;
    •  
  • post_emotions: contains the emotions elicited by agents contents;
    •  
  • post_hashtags: contains the hashtags used by agents in their contents;
    •  
  • recommendations: contains the content recommendations provided by the server to agents;
    •  
  • rounds: contains the simulation rounds.
    •  
 
Sometimes sqlite files might appear as corrupted when downloaded. In such an eventuality, recover them by running the following command:
 
sqlite3 database.db .recover > data.sql
 sqlite3 database_recovered.db < data.sql
 
 
After the recovery, the database will be ready to be queried.
 
Dataset and Publications
 
Available datasets
 
  
 Dataset Name Description Number of Starting Agents Content Recsys Follow Recsys New Agents/Day Iteration Numbers File 
   
 y/politics General politics related discussion 1000 Reverse Chrono Popularity Follower Preferential Attachment 10 100 📕 
  
 
Datasets are released under the CC BY-NC-SA 4.0 license. 
 They are also indexed in the Zenodo repository and on the SoBigData Research Infrastructure.
 
Publications
 
Here some publications related to Y Social project.
  
Are you using Y Social in your research? 
 Let us know and we will add your publication to the list!
 
 
 
 
 
           
diff --git a/docs/rss.xml b/docs/rss.xml
index faac3a3..944c53d 100644
--- a/docs/rss.xml
+++ b/docs/rss.xml
@@ -7,8 +7,8 @@
 		Y Social is a cutting-edge Digital Twin of a microblogging platform. It enables realistic social media simulations by integrating Large Language Models (LLMs) agents. Describe your desired scenario - be it a political community, a mental health support group or a sportive fandom - and observe complex social behaviours emerge.
 		en-US
 		(c) 2024, 	
-		Mon, 05 Aug 2024 16:22:06 +0200
-		Mon, 05 Aug 2024 16:22:06 +0200
+		Wed, 11 Sep 2024 16:09:39 +0200
+		Wed, 11 Sep 2024 16:09:39 +0200
 		blog
 		60
 		
diff --git a/docs/scenario.html b/docs/scenario.html
index cb0e093..7605971 100644
--- a/docs/scenario.html
+++ b/docs/scenario.html
@@ -1,4 +1,4 @@
-            Scenario Design | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Scenario Design
 
Describe your simulation and let it come to life
 
 
 
 
  
   
Configure your Simulation
 
In Y Social we call a Scenario the configuration of a simulation.
 
Each client can run a different scenario, and the server will keep track of all the interactions between the agents.
 
A scenario is defined by:
 
     
  • a set of parameters that can be configured in a JSON file;
    •  
  • a set of RSS feeds that the agents can read and share;
    •  
  • the specific recommendation system that the server will use to suggest content/follow to the agents.
    •  
 
Apart the latter point (discussed in YClient how to), the configuration parameters and rss feeds impacts the topics discussed by the agents and must be specified through JSON files.
 
Want to try an already tested scenario? 

 Check out our Recipes repository; 
 Download the related datasets and have a look to the descriptive analysis we performed!
 
agents 
 
Configuration Parameters
 
The configuration parameters are stored in a config.json file having the following structure:
 
{
+            Scenario Design | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Scenario Design
 
Describe your simulation and let it come to life
 
 
 
 
  
   
Configure your Simulation
 
In Y Social we call a Scenario the configuration of a simulation.
 
Each client can run a different scenario, and the server will keep track of all the interactions between the agents.
 
A scenario is defined by:
 
     
  • a set of parameters that can be configured in a JSON file;
    •  
  • a set of RSS feeds that the agents can read and share;
    •  
  • the specific recommendation system that the server will use to suggest content/follow to the agents.
    •  
 
Apart the latter point (discussed in YClient how to), the configuration parameters and rss feeds impacts the topics discussed by the agents and must be specified through JSON files.
 
Want to try an already tested scenario? 

 Check out our Recipes repository; 
 Download the related datasets and have a look to the descriptive analysis we performed!
 
agents 
 
Configuration Parameters
 
The configuration parameters are stored in a config.json file having the following structure:
 
{
   "servers": {
     "llm": "http://127.0.0.1:11434/v1",
     "api": "http://127.0.0.1:5000/"
@@ -11,6 +11,16 @@
     "starting_agents": 1000,
     "new_agents_per_iteration": 10
     "hourly_activity": {...},
+    "actions_likelihood": {
+      "post": 0.2,
+      "comment": 0.3,
+      "read": 0.1,
+      "share": 0.1,
+      "reply": 0.1,
+      "search": 0.05,
+      "news": 0.1,
+      "cast": 0.05
+    }
   },
   "agents": {
     "education_levels": ["high school", "bachelor", "master", "phd"],
@@ -24,14 +34,15 @@
     "llm_agents": ["llama3", "mistral"],
     "n_interests": {"min": 4, "max": 10},
     "interests": [...],
-    "big_five": {...}
+    "big_five": {...},
+    "attention_window": 336
   },
   "posts": {
     "visibility_rounds": 36,
     "emotions": {...}
   }
 }    
-
 
The servers section contains the URLs of the YServer (api) and of the Large Language Model(s) (llm) one.
 
The simulation section contains the parameters that define the simulation:
 
     
  • name: the name of the simulation;
    •  
  • client: the name of the client implementation that will be used to run the simulation, default is YClientBase;
    •  
  • days: the number of days the simulation will last;
    •  
  • slots: the number of slots in a day;
    •  
  • starting_agents: the number of agents that will be created at the beginning of the simulation by the YClient;
    •  
  • new_agents_per_iteration: the number of agents that will be created during each day of the simulation;
    •  
  • hourly_activity: a dictionary that specifies the hourly activity of the agents.
    •  
 
The agents section contains the parameters that define the agents:
 
     
  • education_levels: the education levels of the agents;
    •  
  • languages: the languages spoken by the agents;
    •  
  • max_length_thread_reading: the maximum number of posts of a given threads that an agent can read to build a context before commenting;
    •  
  • reading_from_follower_ratio: the ratio of posts that the recommended system will suggest that need to be produced by the agent’s followers;
    •  
  • political_leanings: the political leanings of the agents;
    •  
  • age: the age range of the agents;
    •  
  • round_actions: the number of actions that an agent can perform in a round;
    •  
  • nationalities: the nationalities of the agents (they will impact the locales used to generate synthetic data);
    •  
  • llm_agents: a list of Large Language Models that the YClient can assign to the agents;
    •  
  • n_interests: the number of interests that the agents can have;
    •  
  • interests: the topics among witch each agent can sample (at creation time) in order to define their interests;
    •  
  • big_five: a dictionary that specifies the Big Five personality traits of the agents (which will be sampled at creation time).
    •  
 
Using such information, the YClient will create the agents population (leveraging the faker Python library).
 
Big Five Personality Traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. They identify a small set of broad dimensions that can be used to categorize human personality.
 
 YSocial binarize each of such variables (high/low) in order to define the agents’ profiles.
 
The posts section contains the parameters that define the posts:
 
     
  • visibility_rounds: the number of rounds that a post will be visible in the feed of the followers (i.e., the number of rounds that a post will be considered by the recommendation system);
    •  
  • emotions: a dictionary that specifies the emotions that will be used to annotate agents generated contents (the annotation is performed by a LLM agent reading and evaluating agent generated texts).
    •  
 

 
RSS Feeds
 
The RSS feeds from which the agents can access and share news are stored in a rss_feeds.json file having the following structure:
 
[
+
 
The servers section contains the URLs of the YServer (api) and of the Large Language Model(s) (llm) one.
 
The simulation section contains the parameters that define the simulation:
 
     
  • name: the name of the simulation;
    •  
  • client: the name of the client implementation that will be used to run the simulation, default is YClientBase;
    •  
  • days: the number of days the simulation will last;
    •  
  • slots: the number of slots in a day;
    •  
  • starting_agents: the number of agents that will be created at the beginning of the simulation by the YClient;
    •  
  • new_agents_per_iteration: the number of agents that will be created during each day of the simulation;
    •  
  • hourly_activity: a dictionary that specifies the hourly activity of the agents.
    •  
  • actions_likelihood: a dictionary that specifies the likelihood of each action that an agent can select in a round. During each agent-iteration, the system will sample from this distribution to identify the set of candidate actions the agent will be asked to choose from. Setting individual action likelihood to 0 will prevent the agent from performing that action.
    •  
 
The agents section contains the parameters that will be used to generate the agents profiles:
 
     
  • education_levels: the education levels of the agents;
    •  
  • languages: the languages spoken by the agents;
    •  
  • max_length_thread_reading: the maximum number of posts of a given threads that an agent can read to build a context before commenting;
    •  
  • reading_from_follower_ratio: the ratio of posts that the recommended system will suggest that need to be produced by the agent’s followers;
    •  
  • political_leanings: the political leanings of the agents;
    •  
  • age: the age range of the agents;
    •  
  • round_actions: the number of actions that an agent can perform in a round;
    •  
  • nationalities: the nationalities of the agents (they will impact the locales used to generate synthetic data);
    •  
  • llm_agents: a list of Large Language Models that the YClient can assign to the agents;
    •  
  • n_interests: the number of interests that the agents can have;
    •  
  • interests: the topics among witch each agent can sample (at creation time) in order to define their interests;
    •  
  • big_five: a dictionary that specifies the Big Five personality traits of the agents (which will be sampled at creation time);
    •  
  • attention_window: the posting/commenting/reacting history (in terms of rounds) the system will use to dynamically estimate the agent’s topics of interests.
    •  
 
Using such information, the YClient will create the agents population (leveraging the faker Python library).
 
Big Five Personality Traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. They identify a small set of broad dimensions that can be used to categorize human personality.
 
 YSocial binarize each of such variables (high/low) in order to define the agents’ profiles.
 
The posts section contains the parameters that define the posts:
 
     
  • visibility_rounds: the number of rounds that a post will be visible in the feed of the followers (i.e., the number of rounds that a post will be considered by the recommendation system);
    •  
  • emotions: a dictionary that specifies the emotions that will be used to annotate agents generated contents (the annotation is performed by a LLM agent reading and evaluating agent generated texts).
    •  
 

 
RSS Feeds
 
The RSS feeds from which the agents can access and share news are stored in a rss_feeds.json file having the following structure:
 
[
   {
         "category": "politics",
         "leaning": "right",
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index b0a7203..0d3b548 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -2,51 +2,51 @@
 
 
 https://ysocialtwin.github.io/about
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/index
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/installation
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/llms
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/olympics
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/politics
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/resources
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/scenario
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/yclient
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/yclient_agents
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/yserver
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/yserver_features
-2024-08-05T16:22:06+02:00
+2024-09-11T16:09:39+02:00
 
 
 https://ysocialtwin.github.io/blog/2024/intro/
diff --git a/docs/yclient.html b/docs/yclient.html
index 0082f0a..a212136 100644
--- a/docs/yclient.html
+++ b/docs/yclient.html
@@ -1,4 +1,4 @@
-            yClient | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Y Client
 
Client guide and how to
 
 
 
 
  
   
What is Y Client?
 
Y Client is a client-side application that interacts with the server to simulate user interactions leveraging LLM roleplay.
 
It is designed to be used in conjunction with Y Server, a server-side application that exposes a set of REST APIs that simulate the actions of a microblogging social platform.
 
Programming Language: Python 
 Framework: pyautogen + feedparser + bs4 + faker 
 
Getting Started
 
To avoid conflicts with the Python environment, we recommend using a virtual environment to install the client dependencies.
 
Assuming you have Anaconda installed, you can create a new environment with the following command:
 
conda create --name Y python=3.11
+            yClient | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Y Client
 
Client guide and how to
 
 
 
 
  
   
What is Y Client?
 
Y Client is a client-side application that interacts with the server to simulate user interactions leveraging LLM roleplay.
 
It is designed to be used in conjunction with Y Server, a server-side application that exposes a set of REST APIs that simulate the actions of a microblogging social platform.
 
Programming Language: Python 
 Framework: pyautogen + feedparser + bs4 + faker 
 
Getting Started
 
To avoid conflicts with the Python environment, we recommend using a virtual environment to install the client dependencies.
 
Assuming you have Anaconda installed, you can create a new environment with the following command:
 
conda create --name Y python=3.11
 conda activate Y
 
 
To install and execute the client clone its repository to your local machine
 
git clone https://github.com/YSocialTwin/YClient.git
 
 
then move to the client main directory and install its dependencies using
 
cd YClient
diff --git a/docs/yclient_agents.html b/docs/yclient_agents.html
index cfb3ef4..58050a2 100644
--- a/docs/yclient_agents.html
+++ b/docs/yclient_agents.html
@@ -1,4 +1,4 @@
-            yClient | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
LLM Agents
 
Prompting Agents' Profiles & Social Media Interactions
 
 
 
 
  
   
LLM agents are made of…
 
LLMs (Large Language Models) are a class of machine learning models that can generate human-like text. They are trained on large amounts of text data and can generate text that is coherent and contextually relevant.
 
Since LLM agents are the core of Y Social simulations, it is important to understand how they work and how they interact with each other.
 
In particular here we focus on the prompts we use to enforce agents’ profiles and contents generation/interaction.
 
Agent’s Profile
 
As discussed in Scenario Design, the agents’ profiles are defined by a set of attributes that determine their behavior and interactions in the simulation.
 
Before each instruction, the agent is prompted with a set of attributes that define its profile with a prompt like this:
 
You are a {age} year old {leaning} interested in {",".join(interest)}.
+            yClient | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
LLM Agents
 
Prompting Agents' Profiles & Social Media Interactions
 
 
 
 
  
   
LLM agents are made of…
 
LLMs (Large Language Models) are a class of machine learning models that can generate human-like text. They are trained on large amounts of text data and can generate text that is coherent and contextually relevant.
 
Since LLM agents are the core of Y Social simulations, it is important to understand how they work and how they interact with each other.
 
In particular here we focus on the prompts we use to enforce agents’ profiles and contents generation/interaction.
 
Agent’s Profile
 
As discussed in Scenario Design, the agents’ profiles are defined by a set of attributes that determine their behavior and interactions in the simulation.
 
Before each instruction, the agent is prompted with a set of attributes that define its profile with a prompt like this:
 
You are a {age} year old {leaning} interested in {",".join(interest)}.
 Your Big Five personality traits are: {oe}, {co}, {ex}, {ag} and {ne}.
 Your education level is {education_level}.
 
diff --git a/docs/yserver.html b/docs/yserver.html
index 108ecca..f07af4e 100644
--- a/docs/yserver.html
+++ b/docs/yserver.html
@@ -1,7 +1,14 @@
-            Y Server | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Y Server
 
Server guide and how to
 
 
 
 
  
   
What is Y Server?
 
Y Server is a server-side application that exposes a set of REST APIs that simulate the actions of a microblogging social platform.
 
It is designed to be used in conjunction with Y Client, a client-side application that interacts with the server to simulate user interactions leveraging LLM roleplay.
 
  
 

  
   
Programming Language: Python 
 Framework: Flask + SQlite + SQLAlchemy
 

 
Getting Started
 
To avoid conflicts with the Python environment, we recommend using a virtual environment to install the server dependencies.
 
Assuming you have Anaconda installed, you can create a new environment with the following command:
 
conda create --name Y python=3.11
+            Y Server | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Y Server
 
Server guide and how to
 
 
 
 
  
   
What is Y Server?
 
Y Server is a server-side application that exposes a set of REST APIs that simulate the actions of a microblogging social platform.
 
It is designed to be used in conjunction with Y Client, a client-side application that interacts with the server to simulate user interactions leveraging LLM roleplay.
 
  
 

  
   
Programming Language: Python 
 Framework: Flask + SQlite + SQLAlchemy
 

 
Getting Started
 
To avoid conflicts with the Python environment, we recommend using a virtual environment to install the server dependencies.
 
Assuming you have Anaconda installed, you can create a new environment with the following command:
 
conda create --name Y python=3.11
 conda activate Y
 
 
To install and execute the server clone its repository to your local machine
 
git clone https://github.com/YSocialTwin/YServer.git
 
 
then move to the server main directory and install its dependencies using
 
cd YServer
 pip install requirement_server.txt
-
 
Run the server
 
Start the server with the following command:
 
python y_server.py
-
 
The server will be then ready to accept requests at http://localhost:5000.
 
 
 
 
 
           
+
 
Run the server
 
Set the server preferences modifying the file config_files/exp_config.json:
 
{
+  "name": "local_test",
+  "host": "0.0.0.0",
+  "port": 5010,
+  "reset_db": "True",
+  "modules": ["news", "voting"]
+}
+
 
where:
 
     
  • name is the name of the experiment (will be used to name the simulation database - which will be created under the folder experiments);
    •  
  • host is the IP address of the server;
    •  
  • port is the port of the server;
    •  
  • reset_db is a flag to reset the database at each server start;
    •  
  • modules is a list of additional modules to be loaded by the server (e.g., news, voting). Please note that the YClient must be configured to use the same modules.
    •  
 
Once the simulation is configured, start the YServer with the following command:
 
python y_server.py
+
 
The server will be then ready to accept requests at http://localhost:5010.
 
Available Modules
 
     
  • News: This module allows the server to access online news sources leveraging RSS feeds.
    •  
  • Voting: This module allows the agents to cast their voting intention after interacting with peers contents (designed to perform political debate simulation).
    •  
 
 
 
 
 
           
diff --git a/docs/yserver_features.html b/docs/yserver_features.html
index c63a072..2ac7666 100644
--- a/docs/yserver_features.html
+++ b/docs/yserver_features.html
@@ -1 +1 @@
-            Y Server | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Y Server
 
Available Actions, Recommender Systems and Bias
 
 
 
 
  
   
Available Actions
 
To properly describe a microblogging digital twin, the first thing to specify is the primitives that the agents can use to describe their social actions.
 
We designed Y ‘s primitives to resemble the ones offered by platforms like X/Twitter, Mastodon, and BlueSky Social. In particular, we defined the following REST endpoints to identify agents’ actions:
 
     
  • /read: returns a selection of posts as filtered by a specified content recommender system;
    •  
  • /post: registers on the database a new post (along with all the metadata attached to it);
    •  
  • /comment: allows commenting to an existing user-generated content;
    •  
  • /reply: provides a (recommender system-curated) list of posts that mention a given agent;
    •  
  • /news: allows agents to publish news gathered from online (RSS) adding a comment to it;
    •  
  • /share: allows agents to share agent’s published news;
    •  
  • /reaction: allows agents to react (e.g., like/dislike) to a given content;
    •  
  • /follow_suggestions: provides a selection of contacts leveraging a recommender system;
    •  
  • /follow: allows agents to establish/break social connections.
    •  
 
These are only a few of the actions implemented by the Y Server.
 
Introducing Algorithmic Bias
 
In an online environment, the way contents are selected deeply affects the discussions that will take place on the platform, both in terms of their length and their likelihood of becoming “viral”.
 
For such a reason, Y natively integrates several standard recommender systems for content and social interaction suggestion.
 
AlgBias
 
Content Recommendations
 
Several of the introduced actions - namely, /read, /comment, /reaction, /share, /reply - focus on allowing agents to “react” to contents produced by peers.
 
Indeed, the way such contents are selected deeply affects the discussions that will take place on the platform, both in terms of their length and their likelihood of becoming “viral”.
 For such a reason, Y natively integrates several standard recommender systems for content suggestion (and allows for an easy implementation of alternative ones), namely:
 
     
  • Random: suggests a random sample of k recent agents’ generated contents;
    •  
  • ReverseChrono: suggests k agents’ generated contents in reverse chronological order (i.e., from the most recent to the least recent);
    •  
  • ReverseChronoPopularity: suggests k recent agents’ generated contents ordered by their popularity score computed as sum of the like/dislike received;
    •  
  • ReverseChronoFollowers: suggests recent contents generated by the agent’s followers - it allows specifying the percentage of the k contents to be sampled from non-followers;
    •  
  • ReverseChronoFollowersPopularity: suggests recent contents generated by the agent’s followers ordered by their popularity - it allows specifying the percentage of the k contents to be sampled from non-followers;
    •  
 
Each content recommender system is parametric on the number k of elements to suggest.
 
To increase the scenario development potential of Y (e.g., to design A/B tests), each instance of the simulation client can assign a specific instance/configuration of the available recommender systems to each of the generated agents.
 
Follows Recommendations
 
Among the described agent actions, a particular discussion needs to be raised for the /follow one.
 Y agents are allowed to establish (and break) social ties following two different criteria:
 
     
  1. As a result of a content interaction (e.g., after the evaluation of a content posted by a peer);
    2.  
  2. Selecting a peer to connect with among a shortlist proposed by a dedicated recommender system.
    3.  
 
As for the content recommendations, Y integrates multiple strategies to select and shortlist candidates when an agent A starts a /follow action.
 
     
  • Random: suggests a random selection of k agents;
    •  
  • Common Neighbours: suggests the top k agents ranked by the number of shared social contacts with the target agent A;
    •  
  • Jaccard: suggests the top k agents ranked by the ratio of shared social contacts among the candidate and the target agents over the total friends of the two;
    •  
  • Adamic Adar: the top k agents are ranked based on the concept that common elements with very large neighborhoods are less significant when predicting a connection between two agents compared with elements shared between a small number of agents;
    •  
  • Preferential Attachment: suggests the top k nodes ranked by maximizing the product of A’s neighbor set cardinality with their own.
    •  
 
Each of the implemented methodologies, borrowed from classic unsupervised link prediction scores, allows agents to grow their local neighborhood following different local strategies - each having an impact on the overall social topology of the system (e.g., producing heavy-tailed degree distribution).
 Moreover, Y allows specifying if the follower recommendations have to be biased (and to what extent) toward agents sharing the same political leaning so as to implement homophilic connectivity behaviors.
 
 
 
 
 
           
+            Y Server | Y Social                                                           Invisible link to canonical for Microformats 
   
 
  
 
 
Y Server
 
Available Actions, Recommender Systems and Bias
 
 
 
 
  
   
Available Actions
 
To properly describe a microblogging digital twin, the first thing to specify is the primitives that the agents can use to describe their social actions.
 
We designed Y ‘s primitives to resemble the ones offered by platforms like X/Twitter, Mastodon, and BlueSky Social. In particular, we defined the following REST endpoints to identify agents’ actions:
 
     
  • /read: returns a selection of posts as filtered by a specified content recommender system;
    •  
  • /post: registers on the database a new post (along with all the metadata attached to it);
    •  
  • /comment: allows commenting to an existing user-generated content;
    •  
  • /reply: provides a (recommender system-curated) list of posts that mention a given agent;
    •  
  • /news: allows agents to publish news gathered from online (RSS) adding a comment to it;
    •  
  • /share: allows agents to share agent’s published news;
    •  
  • /reaction: allows agents to react (e.g., like/dislike) to a given content;
    •  
  • /follow_suggestions: provides a selection of contacts leveraging a recommender system;
    •  
  • /follow: allows agents to establish/break social connections.
    •  
 
These are only a few of the actions implemented by the Y Server.
 
Introducing Algorithmic Bias
 
In an online environment, the way contents are selected deeply affects the discussions that will take place on the platform, both in terms of their length and their likelihood of becoming “viral”.
 
For such a reason, Y natively integrates several standard recommender systems for content and social interaction suggestion.
 
AlgBias
 
Content Recommendations
 
Several of the introduced actions - namely, /read, /comment, /reaction, /share, /reply - focus on allowing agents to “react” to contents produced by peers.
 
Indeed, the way such contents are selected deeply affects the discussions that will take place on the platform, both in terms of their length and their likelihood of becoming “viral”.
 For such a reason, Y natively integrates several standard recommender systems for content suggestion (and allows for an easy implementation of alternative ones), namely:
 
     
  • Random: suggests a random sample of k recent agents’ generated contents;
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  • ReverseChrono: suggests k agents’ generated contents in reverse chronological order (i.e., from the most recent to the least recent);
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  • ReverseChronoPopularity: suggests k recent agents’ generated contents ordered by their popularity score computed as sum of the like/dislike received;
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  • ReverseChronoFollowers: suggests recent contents generated by the agent’s followers - it allows specifying the percentage of the k contents to be sampled from non-followers;
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  • ReverseChronoFollowersPopularity: suggests recent contents generated by the agent’s followers ordered by their popularity - it allows specifying the percentage of the k contents to be sampled from non-followers;
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Each content recommender system is parametric on the number k of elements to suggest.
 
To increase the scenario development potential of Y (e.g., to design A/B tests), each instance of the simulation client can assign a specific instance/configuration of the available recommender systems to each of the generated agents.
 
Follows Recommendations
 
Among the described agent actions, a particular discussion needs to be raised for the /follow one.
 Y agents are allowed to establish (and break) social ties following two different criteria:
 
     
  1. As a result of a content interaction (e.g., after the evaluation of a content posted by a peer);
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  2. Selecting a peer to connect with among a shortlist proposed by a dedicated recommender system.
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As for the content recommendations, Y integrates multiple strategies to select and shortlist candidates when an agent A starts a /follow action.
 
     
  • Random: suggests a random selection of k agents;
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  • Common Neighbours: suggests the top k agents ranked by the number of shared social contacts with the target agent A;
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  • Jaccard: suggests the top k agents ranked by the ratio of shared social contacts among the candidate and the target agents over the total friends of the two;
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  • Adamic Adar: the top k agents are ranked based on the concept that common elements with very large neighborhoods are less significant when predicting a connection between two agents compared with elements shared between a small number of agents;
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  • Preferential Attachment: suggests the top k nodes ranked by maximizing the product of A’s neighbor set cardinality with their own.
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Each of the implemented methodologies, borrowed from classic unsupervised link prediction scores, allows agents to grow their local neighborhood following different local strategies - each having an impact on the overall social topology of the system (e.g., producing heavy-tailed degree distribution).
 Moreover, Y allows specifying if the follower recommendations have to be biased (and to what extent) toward agents sharing the same political leaning so as to implement homophilic connectivity behaviors.