Explore your Social Circle, improve your Research

If you have a Twitter account and want to register in Grabeeter (or already have), you can now try out and take part in the evaluation of the “Researcher Affinity Browser“. Grabeeter is a tool to archive and search your tweets. The Researcher Affinity Browser is one of the first to expose affinities between Twitter users and is the first web application built on top of a semantic profiling framework. It is intended for researchers who are using Twitter for microblogging and are tweeting about their research, their interests or the conferences they are attending or tracking.

Researcher Affinity Browser

Researcher Affinity Browser

The semantic profiling framework grew throughout this year as my thesis project and information about the evolutions is presented here on this blog.

So if you are a researcher and are using Twitter, you are most welcome to explore people who have already registered themselves in Grabeeter. You can register from inside the application or here. After registration you have to wait a few hours before your data is analysed and suggestions can be made for you. Once you are registered you can start exploring people by selecting your username.
Important Notes

1. You can evaluate the application by filling out the form that can be accessed by clicking on the “Evaluate”-button inside the application or by using this form.

2. You can also evaluate the application with a default user if you feel uncomfortable or don’t want to wait for your data to be analyzed. Just click the Load Persons button without selecting a user in the list. The default user is selected because it has the most conferences.


Are hashtags a good choice as linked data identifiers?

In this project hashtags are used as the most important identifiers to link users. The tags are considered as good identifiers because the user intends and attaches the hash to engage in a conversation in which others use this hashtag. But does it make sense to conclude that they are also good identifiers in linked data?

In the paper “Making Sense of Twitter” [1], David Laniado and Peter Mika first took a look at whether hashtags behave as strong identifiers, and thus whether they could serve as identifiers for the Semantic Web. Twitter users have adopted the convention of adding a hash at the beginning of a word to turn it into a hashtag. Hashtags are meant to be identifiers for discussions that revolve around the same topic. When used appropriately, searching on these hashtags would return messages that belong to the same conversation (even if they don’t contain the same keywords), and thereby solving the aggregation prob- lem. Coincidentally, this is the same function that strong identifiers (URIs) play in the Semantic Web. The questions they asked then is which hashtags behave as strong identifiers (if any), and if they could be mapped to concept identifiers in the Semantic Web? Read more of this post

Profiling and Discovery API functions for Grabeeter (TUGraz), 1st version

A short user guide on how to use the API for the Semantic Profiling framework (more details are following), please note that the “Profiling” and “Discovery” functions used in step 3 and 4 are under construction. Every time you check the results may differ strongly.

Read more of this post

Real Time Interlinking of Tweets (API)

In the previous post, I described a demonstration of a HTTP Stream with realtime annotated tweets. We improved the annotation by trying to identify concepts and linking them to resources in the LOD cloud. For example DBPedia, GeoNames and FreeBase.

http://socialweb.semanticprofiling.net/client Read more of this post

Realtime Social Semantic Web

In another similar project we are trying to bring realtime RDF annotation to the Social Web! Read more of this post

User Analysis possibilities: first simple Demo!

I implemented a first very basic demonstration to show the possibilities for analysis of the annotated tweet data. This first demo makes no use of any domain knowledge or linked data. It is just uses the annotated user data and associated tags.

It matches two users based on similar hashtags. Of course this can be done on many ways. But the semantic profiling framework (in its current state) made an implementation for the logic possible in just half an hour with 3 lines of code on top of the framework.

The next and final improvement for this simple demo will be to identify scientific conferences in the list of common tags.

Try it yourself

If you are not in the database yet, you can do it by using this link:



First analysis demo

First analysis demo