micro-TWONy Demonstrator
Our micro-TWONy demonstrator visualises how we use generative agents in TWON – and it shows the impact of recommendation algorithms of online social networks on the emotionality of users’ feeds. The prototype allows users to choose whether posts in a feed should simply be ranked chronologically, or whether they should be ranked by an algorithm that rewards emotional posts. After making the choice for a recommendation algorithm, TWONy users can simulate a feed with the help of autonomous generative agents. They can then observe which effect the chosen recommendation algorithm has on the emotionality of the resulting feed: the visualised network metrics show whether positive or negative emotions prevail in the feed and how emotionality evolves over time.
More technically speaking: TWONy is a micro-simulation prototype designed to show the impact of online social network (OSN) mechanics — particularly recommendation algorithms — on emotional contagion and discourse dynamics. By harnessing the capabilities of Large Language Models (LLMs), the system generates an ecosystem of synthetic, politically engaged digital personas that interact within a controlled social media environment. These autonomous agents emulate human behaviours through in-context prompting techniques, enabling users to observe emotional transmission patterns under systematically varied conditions while circumventing the constraints inherent to real-user experimentation. The prototype implements two distinct recommendation paradigms: a baseline chronological feed and an emotion-prioritising ranking mechanism that amplifies content based on emotional intensity, allowing the examination of the formation and reinforcement of echo chambers. Emotional valence is quantified via a fine-tuned BERT model, while network-level and agent-level visualisations track emotional cascades and polarisation dynamics throughout the lifecycle. The system is architected as a browser-based application leveraging modern web technologies and decentralised APIs.
How to use micro-TWONy
By default, you see the “News Feed” page first. As a user, you can type in a post and post it to the feed (by clicking on the blue “Post” button). Subsequently, you can start a simulation based on your own post (by clicking on the green “Start” button). You can also choose the language model which is to be used for the simulation (Llama, Mistral, Deepseek). The resulting feed is populated by generative agents, which emulate human behaviour. Under each post, the positive (joy, optimism, trust) and negative (anger, fear, pessimism) emotions are displayed.
On the right of the page, under “Network Metrics”, a measure of the overall positive and negative emotions in the feed is displayed over time. Under “User Metrics”, the positive and negative valence per user is displayed. You, as the author of the initial post, are the “Human”. The others, such as “ProgressiveRage” and “FactChecker”, are the generative agents. To the left of the page, you can access the menu (News Feed, Ranking Settings, Agent Settings). Under “Ranking Settings”, you can choose the recommendation algorithm with which you want to run the simulation. You can choose between a plain chronological ranker and an emotion-based ranking. For the emotion-based ranking you can choose whether negative and positive emotionality of posts should be rewarded (weighting increased) or not (weighting decreased). These settings are then displayed at the bottom right of the “News Feed” page, under “User Metrics”.
Under “Agent Settings”, you can see how the generative agents in the feed are prompted, and customise the instructions if you wish to. Under “Customize Personas”, you can view the prompted humour style, communication pattern, emotional expression, values and beliefs, interests and hobbies, social interactions, personality traits and cultural background for each generative agent. You can also customise them, add new personas and delete existing ones for your simulation.
Try micro-TWONy ↗ (external website, hosted on GitHub Pages)