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Contents of this directory

This directory contains information about events that produced coverage spikes on Twitter, on mainstream media (GDELT), or both. It also includes a list of keywords (for Twitter) and a list of themes/taxonomies (for GDELT)

Events list file: 'covered_climate_events.csv'

Contents: This file contains information about 350 events that received medium to high coverage in Twitter, mainstream media, or both, covering a period of 17 months in 2013 and 2014, and labeled by relevance to climate-change, triggers, actions, and 6 news values (i.e. extraordinary, unpredictable, high magnitude, negative, conflictive, and related to elite persons). Each label is the result of the majority voting among at least 5 crowdsourcing workers (3 crowdsourcing workers for the easier task of false positives removal).

Data Format: One event per line with the following 16 comma-separated fields: Media, Event, Time Interval, Sample URLs, Relatedness (crowd), Relatedness (authors), Primary Type (or trigger), Primary Sub-type (or action), Secondary Type (or trigger), Secondary Sub-type (or action), Negativity, Magnitude, Predictability, Elite persons, Conflict, Unexpectedness

Labels: The file contains the following elements for each event:

  • Media: News or Twitter
  • Event: the event name in the form of a headline
  • Time Interval: the day or the interval in which we detected coverage spikes for the respective media and corresponding to the same event
  • Sample URLs: URLs of articles or tweets about the event
  • Relatedness (crowd): related to climate change; weakly related to climate change; not related to climate change
  • Relatedness (authors): yes (related to climate change); borderline (weakly related to climate change); no (not related to climate change)
  • Primary/Secondary Type (or trigger): disaster; for-profit (excl. media); govt (exec & legislative); individuals; media; ngos/groups of people
  • Primary/Secondary Sub-type (or action): natural hazards; human-induced hazards; legal actions; meetings/conferences; generic (campaigns/statements/other); publication/studies/research
  • Negativity: this is good news; this is neither good nor bad news; this is bad news
  • Magnitude: the magnitude of this event is high; the magnitude of this event is moderate; the magnitude of this event is low
  • Predictability: a member of the public could not have know this will happen; a member of the public could have know this will happen
  • Elite persons: this involves someone rich, powerful, or famous; this does not involve someone rich, powerful, or famous
  • Conflict: this depicts a conflict between two opposing persons/groups; this does not depict a conflict between two opposing persons/groups
  • Unexpectedness: this is an ordinary event; this is an extraordinary event

A description of the instructions given to crowdsourced workers in the news values annotation task can be found in the 'news_values_annotation_tasks_summary.txt'.

Twitter keywords file 'twitter_climate_keywords.txt'

Contents: This file contains a list of climate-change related keywords.

Data Format: plain text with one keyword per line (this format is also compatible with the one required by our collection scripts).

GDELT Themes/Taxonomies file 'gdelt_climate_themes.txt'

Contents: This file contains a list of climate-change related themes/taxonomies.

Data Format: plain text with one GDELT theme/taxonomy per line. The initial/bootstrap theme, the themes list and taxonomies list are marked by a line starting with "#".

Crowdsourcing examples file 'news_values_annotation_tasks_summary.txt'

Contents: This file contains example instructions and of correctly annotated events.

Data Format: plain text.


[Olteanu et al. 2015] Alexandra Olteanu, Carlos Castillo, Nicholas Diakopoulos, Karl Aberer. 2015. Comparing Events Coverage in Online News and Social Media: The Case of Climate Change. In Proceedings of the AAAI Conference on Web and Social Media (ICWSM'15). AAAI Press, Oxford, UK.


For inquiries please contact Alexandra Olteanu, or Carlos Castillo, or Nicholas Diakopoulos, or Karl Aberer.

Version history

  • 2015-04-06: v1.0, initial release.