Gaining Historical and International Relations Insights from Social Media: Spatio-Temporal Real-World News Analysis using Twitter.

Abstract

<div> <div> <p>This dataset consists of metadata related to 24,508 news events, collected from Twitter spanning from August 2013 to June 2015. The events encompasses a total of 193,445,734 tweets produced by 26,127,624 different users.</p><p>The files contain different aspects of the data.<br></p><p>- components.tsv consists of the description of the events (components) of our dataset, consisting of 4 columns separated by tabs. The columns correspond to the component ID, the date of an event, the amount of tweets and a set of keywords describing the event, separated by commas (having a minimum of 2).<br></p><p>- componentlocation.tsv consists of the description of the locations where the events happened (“protagonist locations”). The columns correspond to an ID, the component ID, the names of the locations, the frequency (how many times that location was mentioned in the component), the country code, and six more non-relevant columns. Note that one component can be in several rows, one per location being mentioned for that component.</p><p>- country_protagonized-events.csv consists of the amount of events that one specific country is a protagonist of. It contains two columns, separated by comma, being the first the country code and the second the amount of events (components) that country is a protagonist of.<br></p><p>- country_tweets.csv consists of the amount of tweets that one specific country has issued along all the events. It contains two columns, separated by comma, being the first the country code and the second the amount of tweets that country has issued.</p><p>- participation_data.txt contains a matrix indicating the amount of tweets per country, per event. It contains one row per component ID, and one column per country (plus one column for the component ID); the cell value is the amount of tweets that country has issued for that event.<br></p><p>- similarities_no_reciproco_percentile.csv corresponds to the similarity between co-protagonist countries. The columns are in the following order: Country 1, the amount of events Country 1 is a protagonist of, Country 2, the amount of events Country 2 is a protagonist of, the Jaccard Similarity between the two countries (where the country is represented by the set of the component IDs that country is a protagonist of), and the percentile of that similarity value (ranging from 0 to 1).</p><p>- users_events_distinct.txt corresponds to the amount of unique users participating in an event. The columns are separated by tabs. The first columns is the component ID, the second is the amount of different users for that event, and the third is the amount of of different news sources for that event.</p><p>- countries.txt is the mapping between country code and country name, separated by space.</p> </div> </div