Social media platforms, taken in conjunction, can be seen as complex networks; in this context, understanding how agents react to sentiments expressed by their connections is of great interest. Here, the authors show how Network Knowledge Bases help represent the integration of multiple social networks, and explore how information flow can be handled via belief revision operators for local (agent-specific) knowledge bases. They report on preliminary experiments on Twitter data showing that different agent types react differently to the same information-this is a first step toward developing tools to predict how agents behave as information flows in their social environment.
Reasoning about Sentiment and Knowledge Diffusion in Social Networks
Simari G.;Martinez M. V.;
2017-01-01
Abstract
Social media platforms, taken in conjunction, can be seen as complex networks; in this context, understanding how agents react to sentiments expressed by their connections is of great interest. Here, the authors show how Network Knowledge Bases help represent the integration of multiple social networks, and explore how information flow can be handled via belief revision operators for local (agent-specific) knowledge bases. They report on preliminary experiments on Twitter data showing that different agent types react differently to the same information-this is a first step toward developing tools to predict how agents behave as information flows in their social environment.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
