Replication Data for: Right and left, partisanship predicts vulnerability to misinformationhttps://doi.org/10.7910/DVN/6CZHH5Dimitar NikolovAlessandro FlamminiFilippo MenczerHarvard Dataverse2020-10-072021-08-12T11:11:53Z<p>The dataset consists of two files:</p>
<p>1. <em>anonymized_shares.json</em>: A collection of sharing actions, each corresponding to a tweet posted in June 2017 in which one or more URLs were shared. The format is as follows:</p>
<p>
<pre>
{
uid1: [
{"domains": ["domain1", "domain2"]},
{"domains": ["domain3"], "retweeted": retweeted_uid},
{"domains": ["domain4"], "quoted": quoted_uid},
...
],
uid2: [
...
],
...
}
</pre>
</p>
<p>
That is, for each sharing action we have:
</p>
<p>
<ul>
<li>the list of domains from which links were shared</li>
<li>if the tweet was a retweet, the ID of the user who created the original tweet</li>
<li>if the tweet was quoting another tweet, the ID of the user who is being quoted</li>
</ul>
</p>
<p>
All user IDs were anonymized and will not be traceable to Twitter user IDs.
</p>
<p>
The tweets were collected from the <a href="https://osome.iu.edu/tools/">Social Media Observatory at Indiana University</a>.
</p>
<p>
2. <em>anonymized_friends.json</em>: For each user in the dataset, the list of their friends (followees) as given by the <a href="https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/follow-search-get-users/api-reference/get-friends-ids">friends/ids Twitter API endpoint</a>.
</p>
<p>
The format is as follows:
</p>
<p>
<pre>
{
uid1: [friend_uid1, friend_uid2, ...],
uid2: [...],
...
}
</pre>
</p>
<p>3. <em>measures.tab</em>: TAB-separated file with partisanship and misinformation scores for each anonymized user.
<p>
All user IDs were anonymized and will not be traceable to Twitter user IDs.
</p>Computer and Information ScienceSocial SciencesMisinformationEcho ChambersBiasPolarizationDimitar Nikolov, Alessandro Flammini, Filippo Menczer; Right and left, partisanship predicts vulnerability to misinformation; 2021, arXiv, 2010.01462, https://arxiv.org/abs/2010.014622020-10-07Nikolov, Dimitar2021-01-11Observatory on Social Media, https://osome.iu.edu/tools/Twitter, https://developer.twitter.com/CC0 1.0