DART: a Dataset of Arguments and their Relations on Twitter

By Tom Bosc, Elena Cabrio, Serena Villata. 2016

43 citations recorded by the source corpus.

The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. John Lawrence
  2. Jeff Wu
  3. Jan Leike
  4. Chris Reed
  5. William Saunders
  6. Steven Bills
  7. Richard Ngo
  8. Luke S. Zettlemoyer

In Information Systems

  1. Chris Reed
  2. John Lawrence
  3. Jeff Wu
  4. Richard Ngo

In Natural Language Processing

  1. Chris Reed
  2. John Lawrence
  3. Jan Leike
  4. Jeff Wu
  5. Steven Bills
  6. Richard Ngo
  7. Christopher R'e
  8. Andrew McCallum

In Social Sciences & Humanities

  1. Chris Reed
  2. John Lawrence
  3. Jeff Wu

Citations in the corpus