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Erik Borra

Researcher at University of Amsterdam

Publications -  20
Citations -  771

Erik Borra is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Social media & Web query classification. The author has an hindex of 11, co-authored 18 publications receiving 680 citations.

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Journal ArticleDOI

Programmed Method: Developing a Toolset for Capturing and Analyzing Tweets

TL;DR: The authors argue that the type of data used for, as well as the methods encoded in, computational systems have epistemological repercussions for research, and introduce the Digital Methods Initiative Twitter Capture and Analysis Toolset.
Journal ArticleDOI

Twitter, YouTube, and Flickr as platforms of alternative journalism: the social media account of the 2010 Toronto G20 protests

TL;DR: This article examined the appropriation of social media as platforms of alternative journalism by the protestors of the 2010 G20 summit in Toronto, Canada, and found that social media did not facilitate the crowd-sourcing of alternative reporting, except to some extent for Twitter.
Proceedings ArticleDOI

Mining web query logs to analyze political issues

TL;DR: Evidence that "lies are catchy" and that queries pertaining to false statements are more likely to attract large volumes are found, and the observation that the more right-leaning a query it is, the more negative sentiments can be found in its search results are found.
Proceedings ArticleDOI

Societal Controversies in Wikipedia Articles

TL;DR: Contropedia as mentioned in this paper augments Wikipedia articles and gives insight into the development of controversial topics using an efficient language agnostic measure based on the edit history that focuses on wiki links to easily identify which topics within a Wikipedia article have been most controversial and when.
Proceedings ArticleDOI

Societal Controversies in Wikipedia Articles.

TL;DR: Contropedia as discussed by the authors augments Wikipedia articles and gives insight into the development of controversial topics using an efficient language agnostic measure based on the edit history that focuses on wiki links to easily identify which topics within a Wikipedia article have been most controversial and when.