Open Terms Archive
Follow the changes to the Terms of Service
Explore the contractual documents of the main online service providers and compare their evolution through time.
Improve your practices and toolset
Assess the legality of political ads
Collaboratively detect, qualify and react to disinformation campaigns
Identify clusters of bots on Twitter
A crawler that navigates Twitter follower relationships to identify clusters of suspicious accounts from a given seed account. It uses the free Twitter API and can thus be easily deployed.
Based on a manual annotation of over 400 accounts, we have also used that dataset to assess the reliability of usual bot detection tools such as Botometer.
Compare an article's visibility on social media to other known events visibility in its local reference media to give some perspective to the number of reactions.
When fighting disinformation, analyses and fact-checks produced often relay - when they are not based on - figures to measure the impact of a content on a given society.
Among such data can be the number of reactions to said content, the number of comments it yields, or the number of times it has been shared.
However, this data us usually not contrasted nor compared. Rather than focusing on their quantitative aspect, we offer an alternative: to organize data according to a scale of relevance. Thus, in considering the topic of the content instead of how many times it was shared, one reaches a qualitative scale of reference.