[wiki][open source]
We developed Twarql to enable annotations and management of streaming tweets in order to alleviate information overload. Twarql encodes information from microblog posts as Linked Open Data in order to enable flexibility for those interested in collectively analyzing microblog data for sensemaking. Instead of requiring the use of keywords or custom software for filtering information, Twarql leverages a full fledged query language (SPARQL) that is much more expressive than keywords. Twarql is available as open source and can be easily extended and deployed to enable Twitter monitoring systems that can be used in various contexts: brand tracking, disaster relief management, stock exchange monitoring, etc., as it flexible architecture makes easy to write such components. A screen capture demonstration is available here.

• Continuous Semantics -- Knoesis [wiki][tech report]
After Twarql, we were able to retrieve microblogs with more expressive filters (SPARQL Queries) than keyword or hashtag search. Our next thought slipped to retrieve tweets for a dynamically evolving event. In this work we dynamically evolve event models as and when the event changes. This evolving event model is used by Twarql to continuously filter relevant tweets. The evolving event model helps to keep abreast with the information related to the dynamic event.
• Personalized Social Stream -- Knoesis, DERI [wiki]
This work provides an architecture for filtering the public Twitter stream and delivering the interesting tweets directly to the users according to their multi-domain user profile of interests. We generate comprehensive user profiles of interests by fetching and aggregating user information from different sources (i.e. Twitter, Facebook and LinkedIn). Then, extract entities from tweets to model them using Semantic Web technologies reusing Twarql. Dynamic groups of users based on their interests are automatically created and interesting tweets are "pushed" to them using the Semantic Hub architecture.

[wiki][application]
The objective of this project is twofold: first, to analyze the association between Web documents and themes extracted from tweets. Secondly to evaluate this association over the time dimension. In other words, we're interested in what is being said about a document. This requires that we first extract document mentions (URLs) from tweets. This analysis is useful from three perspectives. Publisher perspective, User Perspective and the Search Engine perspective. The publisher gets to know where and how the document he has published is being viewed, the User can choose the urls which are most talked about regarding his theme of interest and the search engine can use the analysis for better search.

The objective to this work is to provide user controlled dissemination of content in Distributed Social Networks. Microblogging platforms such as Twitter, Diaspora provide very less or no control over the dissemmination of the content once the user publishes. Here, we extend Google’s PubSubHubbub protocol to a more privacy aware protocol, using Semantic Web technologies, solving scalability and the privacy issues in Distributed Social Networks. We enhanced the traditional features of PubSubHubbub in order to allow content publishers to decide whom they want to share their information with, using semantic and dynamic group-based definition. This protocol is implemented and used in SMOB (our Semantic Microblogging framework). The implementation is application agnostic, and can be adopted by any system requiring scalable and publisher controlled content broadcasting.

• SMOB -- DERI
[open source]
SMOB is an open-source microblogging framework that allows anyone to install her/his personal hub, whereas hubs consequently interact in a distributed manner on the Web. Started in 2008, it follows the vision of a federated Social Web, where people own their data and can openly share it without the constraints of a third-party provider. SMOB combines various ideas and technologies from the Semantic Web / Linked Data world to achieve this vision.