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Twarql: Twitter Feeds through SPARQL

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Twarql Demonstration


Every day, Web users are using Twitter to simultaneously publish millions of microblog posts (microposts or ``tweets) with opinions, observations and suggestions that may represent invaluable information for businesses and researchers around the world <ref>See recent statistics from Twitter at</ref> Taking advantage from this "wisdom of the crowd" --- which refers to "the process of taking into account the collective opinion of a group of individuals rather than a single expert to answer a question"<ref>From Wikipedia, see \url{</ref> ---, Twitter data has been successfully used, for example, to forecast box-office revenues for movies <ref name=corr10asur>S. Asur and B. A. Huberman. Predicting the future with social media. CoRR, abs/1003.5699, 2010.</ref> or to manage earthquakes detection <ref name=www-earthquake/>. However, analyzing the vast amount of microblog data published each second can be extremely challenging, especially in situations where it has to be done in real-time.

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.

Our approach encompasses the following steps:

  • extract content (entity mentions, hashtags and URLs) from microposts;
  • encode content in a structured format (RDF) using shared vocabularies (FOAF, SIOC, MOAT, etc.);
  • enable structured querying of microposts (SPARQL);
  • enable subscription to a stream of microposts that match a given query (Concept Feeds);
  • enable scalable real-time delivery of streaming data (SparqlPuSH).

Twarql is available at 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.


We have two demonstration videos. The first video demonstrates the user perspective, interacting with the system to formulate a query and obtain microblog posts that match that query. The second video focuses on the server side and demonstrates the modules of our architecture at work, distributing the microposts via pubsubhubbub.

Brand Tracking Scenario: IPad
For the Triplify Challenge 2010 we have collected tweets mentioning iPad from June 3rd until Jun 8th to demonstrate our system in a brand tracking scenario.

You can download a sample of the triples generated at:

  • Total Number of tweets : 511,147
  • Sentiment: 53,237 positive; 6,739 negative; 451,171 neutral.


See the workflow between the components of the architecture:

Tweet Annotation

  • extract content from microposts;
    • entity mentions (e.g. from DBpedia)
    • hashtags
    • URLs
    • user mentions
  • encode content in a structured format (RDF) using shared vocabularies (FOAF, SIOC, MOAT, etc.);

We offer Twarql Annotation both as REST and Java APIs. You can download our source code and easily extend the annotation pipeline with your own extractors.

Concept Feeds

  • enable structured querying of microposts (SPARQL);
  • enable subscription to a stream of microposts that match a given query;


Twarql API

REST Endpoints


  • URL scheme: http://<base-url>/<operation>/?<parameter>=<value>&...&output=<output format>
  • Base URL:
  • Operations: search, register, stream, query
  • Output formats: twitter-json, sparql-json, entities


  •,...,kn&output=<output type>
    • input: keywords, output type (tweets, entities, sparql)
    • output: tweets, entities, triples

Output Formats

We also provide output according to the format presented on the twitter-api-announce message.

"text" : "hey @raffi tell @noradio to check out #hot",
"entities" : {
 "user_mentions" : [
   "id" : 8285392,
   "screen_name" : "raffi",
   "indices" : [4, 9]
   "id" : 3191321,
   "screen_name" : "noradio",
   "indices" : [16, 23]
"urls" : [
 {      "url" : "",
   "indices" : [38, 64]
"hashtags" : [
 {      "text" : "#hot",
   "indices" : [66, 69]
   "url" : ""

Error/Warning Messages


  • Unknown Stream: You are requesting a stream id that was not registered.
  • Invalid Query: You are trying to register an invalid SPARQL query.
  • Unsupported Content-type: The requested content type is not supported.


  • No Results: There are no results for the query.

Supported Clients

  • SPARQL Protocol-compliant Clients
    • Cuebee is a SPARQL query formulation and results exploration engine. We provide a TweetExplorer that can be directly plugged into Cuebee.

  • RSS/Atom clients
    • View SparqlPuSH


You may contact us if you have any questions about the implementation or API. We have listed our major contributions below our names so that you know to whom you should direct your question.

  • Pablo Mendes (@pablomendes)
    • Architecture, SPARQL client (Cuebee), Social Sensor (Extraction, Annotation), API, Documentation
  • Pavan Kapanipathi (@pavankaps)
    • Application Server, Semantic Publisher, Streaming SPARQL, API Content Negotiation
  • Alex Passant (@terraces)
    • SparqlPuSH, Annotation Vocabularies