Today, collaboration and crowdsourcing are the realities of public Internet. The so-called “Web 2.0” represents a precious repository of thematic information, thanks to the heterogeneous content that is inserted daily and spontaneously updated by its users.
The best way to visualise the concept is to describe the flow of activities, as shown in the Figure below.
Using advanced semantic representation technologies, to capture topics and arguments relevant to the policy and their inter-relations. This is facilitated by the NOMAD visual tool for semantic authoring, a user-friendly environment for the domain expert with limiting expertise in knowledge representation technologies.
Main output: Visual tool for policy argumentation modeling, Policy Data Model
Semantically Driven Data Acquisition:
Focused crawling, capable of accessing a variety of Web 2.0 applications (e.g. social media, wikis and blogs), is applied to retrieving Citizen-created content pertinent to the policy model.
Main output: Data Acquisition Module
Multi-lingual and cross-lingual information extraction technologies are applied to the content retrieved by the data acquisition process, in order to extract structured representations of the Citizens’ arguments. This process also applies statistical and linguistic tools to discover clues about demographic features that can be reasonably associated with the content’s authors.
Main output: Argument Extraction Module
Opinion Mining & Sentiment Analysis:
Opinion Mining technologies are applied to the retrieved content, taking into account the extracted Citizens’ arguments, to detect expressed Citizen’s opinions and analyze their sentiment, with regards to the various dimensions of (pre)policy planning.
Main output: Opinion Mining& Sentiment Analysis module
Summarization technologies, exploiting statistical and semantic information, generate qualitative information about opposing arguments, in the form of anonymity-preserving automatically-generated summaries.
Main output: Argument Summarization Module, Social Argumentation Map
Social Reaction Visualization:
The policy maker through the NOMAD front-end module will be provided with multi-faceted aggregates, exploiting the multi-dimensional geo-demographic features extracted by the Argument Extraction Module. Such aggregates will be intuitively presented using Information Visualization and Visual Analytics techniques.
Main output: NOMAD front-end module, Information visualization module