Unveiling Latent States Behind Social Indicators

The work reported in this paper aims at describing a project that leverages the potential of Information Retrieval and Machine Learning towards novel techniques that unveil the latent states of expert users such as sociologists and economists by means of indicators when the user is accessing large collection of newspapers, blogs, etc. An indicator measures the degree to which a certain latent state is present during interaction when exploring and searching an information repository. In this paper the state of a user is the particular condition that s/he is in at a specific context with reference to a problematic issue induced by the data s/he accessed to; risk is an example of state and a risk indicator aims at providing a measure of the degree to which articles examined by the user evoke risk in her/his mind. Observable attributes, e.g. keywords, clickthrough data or links, are the input data to model states and compute indicators. In this work, starting from some results of a software architecture designed to support sociologists in investigating Techno-scientific Issues in the Public Sphere, we will discuss some challenges and we will present a formal framework to address them where informative objects, e.g. news articles, states and attributes are uniformly modelled as vectors as it is customary in Information Retrieval or Machine Learning. This is our first step towards a long term objective, i.e. generalizing the well known Learning to Rank framework towards a Learning to Search framework which would encompass multiple and simultaneous states.