 This research output is a joint industry co-sponsored project between Elsevier Labs and the Insight Centre for Data Analytics GoAway. The project builds altmetric networks of researchers and institutions to develop our understanding of how research outcomes are propagated in society and to experiment with metrics that quantify the authority, centrality and influence of researchers and institutions for a given topic. For example, a network of researchers and institutions interested in avian flu will link to quote and mention each other. This project takes an index of web documents for the given topic and builds a network of quotes, mentions and links over time. Sophia is our altmetrics network and analysis demonstrator. It is currently at version 2. Sophia displays information on co-mentions between entities mentioned in web blogs, online news sources and so-called grey literature, documents produced by government, not-for-profits and charities. We display information in three ways. One, by using an explorable network visualization of documents, individuals and organizations. Two, by presenting the contents of the articles which mention these entities in the forms of terms extracted from those sources. And three, using terms extracted from those sources to create a word cloud. As of version 2, searches can now be made for both scientific authors and for organizations. An entity can be searched for by first ensuring that the correct radio button is selected, following which the name of the author or organization is entered into the search bar. After entering the first two characters, the search bar will provide a list of suggested authors or organizations from those that the application currently holds in its database. The names are accompanied by the number of documents that the entity appears in and the number of different sources that the documents come from, for example, news and blogs. As can be seen, the results are presented in the form of a detailed network visualization, an entity mentioned network and a term cloud, showing key terms found within the mentioning documents. The network visualization shows detailed information on entity mentions and co-mentions, displaying web blogs, news documents and grey literature documents as squares, colored, green, pink and purple respectively. Authors are displayed as dull green diamonds and organizations as orange circles. Ties between these nodes indicate a mention of a scientific entity, i.e. scientists or institution, or a co-mention between scientific entities in a document. Nodes in the network are scaled by the number of mentions, a highly mentioned author or node would be large for example, and a document which does not make many mentions will be small in scale. The network is zoomable, draggable and panable for ease of exploration. Clicking on a document will bring up a text box displaying the content of that document with entity mentions highlighted. To the lower left, we can see a visualization showing a network of documents and entities. The documents are shown at the center of this network and are colored depending on their source, green for weblog, yellow for news sources and grey for government literature. Clicking on a document brings up a text box displaying the content of that document. Entity mentions in the document are highlighted for the convenience of the user in the color related to the mentioned entity. Around these documents is a ring of entities mentioned in the documents. These entities can be either organizations or scientists with organizations displayed in blue and scientists entities displayed in red. Links can be seen throughout the graph as edges between documents and entities. When an entity is selected, the documents in which that entity appears remain clearly visible along with co-mentioned entities and the relevant links between them. Non-related entities and documents are faded, and this clearly shows entities that are co-mentioned and the documents in which they are mentioned. As mentioned previously, the terms in the tag cloud are extracted from the documents in which the selected author appears. These tags can be used to filter the documents within the entity mentioned network visualization to show documents containing only a certain term. As can be seen, the entity mentioned network can be filtered by keyword to get a better view of the network of the selected author pertaining to a specific topic. This concludes the demo of version 2 of SOFIA. Thank you for watching.