Saved datasets
Last updated
Download format
Usage rights
License from data provider
Please review the applicable license to make sure your contemplated use is permitted.
Cost to access
Described as free to access or have a license that allows redistribution.
2 datasets found
  1. z

    PAN19 Author Profiling: Bots and Gender Profiling

    Updated Feb 18, 2019
  2. z

    PAN19 Authorship Analysis: Bots and Gender Profiling

    Updated Feb 18, 2019
  3. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Click to copy link
Link copied
Rangel, Francisco; Rosso, Paolo (2019). PAN19 Author Profiling: Bots and Gender Profiling [Dataset].

PAN19 Author Profiling: Bots and Gender Profiling

Explore at:
5 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 18, 2019
Dataset provided by
Universitat Politècnica de València
Rangel, Francisco; Rosso, Paolo

Social media bots pose as humans to influence users with commercial, political or ideological purposes. For example, bots could artificially inflate the popularity of a product by promoting it and/or writing positive ratings, as well as undermine the reputation of competitive products through negative valuations. The threat is even greater when the purpose is political or ideological (see Brexit referendum or US Presidential elections). Fearing the effect of this influence, the German political parties have rejected the use of bots in their electoral campaign for the general elections. Furthermore, bots are commonly related to fake news spreading. Therefore, to approach the identification of bots from an author profiling perspective is of high importance from the point of view of marketing, forensics and security.

After having addressed several aspects of author profiling in social media from 2013 to 2018 (age and gender, also together with personality, gender and language variety, and gender from a multimodality perspective), this year we aim at investigating whether the author of a Twitter feed is a bot or a human. Furthermore, in case of human, to profile the gender of the author.

The uncompressed dataset consists in a folder per language (en, es). Each folder contains:

  • A XML file per author (Twitter user) with 100 tweets. The name of the XML file correspond to the unique author id.
  • A truth.txt file with the list of authors and the ground truth.
Clear search
Close search
Google apps
Main menu