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These are the data preparation and analysis Jupyter notebooks accompanying Nicholls (2019) Detecting Textual Reuse at Scale, in the International Journal of Communication. The first notebook shows the steps for building the database of news content data which this notebook relies upon, the second carries out the analyses from the paper. Although this sets out all the steps required to implement the method, there are two important issues to be aware of: The source data (newspaper articles) are not included as they are copyright encumbered There are many things that could be done better a second time around If you want to reimplement the method, please do be in touch: tom.nicholls@politics.ox.ac.uk
This work was supported by a grant from Google UK as part of the Digital News Initiative (CTR00220).
computational methods, online news, automated content analysis, churnalism, news agency, news production
computational methods, online news, automated content analysis, churnalism, news agency, news production
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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