http://orcid.org/0000-0002-6365-0321
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Abstract
The rise of digital journalism allows users to access regional news content from anywhere in the world. In this situation the content is local, but the audience may not be. Drawing on the sociological concept of translocality, the communicative linkage of dispersed geographic locations, this study investigated these emerging long-distance audiences. We combined automated and manual content analyses to (a) investigate the Twitter followership of 146 regional German newspapers and (b) explain the proportion of translocal followers they attract. Roughly one third of followers came from a different German federal state than the newspaper they followed. Papers with a high percentage of translocal followers tended to have older Twitter accounts and came from larger cities than those with fewer translocal followers. Furthermore, they covered regional sports and human-interest stories more, while those with fewer translocal followers placed greater emphasis on infrastructure, event notes, and current traffic information.
ACKNOWLEDGEMENTS
We would like to thank Lena Frischlich, Annie Waldherr, Wai Yen Tang, and our two anonymous reviewers for their thoughtful feedback on this paper.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
SUPPLEMENTARY MATERIAL
Supplementary material is available for this article at: http://dx.doi.org/10.1080/1461670X.2018.1520609
Notes
1. The final list is available online at http://bit.ly/2fJWHvm and in the supplementary material of this article (see above).
2. The selected cases had to match further criteria: Circulation exclusively in one federal state, no obvious use of a bot, geographic distribution throughout the German territory.
3. The IDs of these tweets are available online at http://bit.ly/2fJWHvm and in the supplementary material of this article (see above).
4. The translated codebook is available online at http://bit.ly/2fJWHvm and in the supplementary material of this article (see above).
5. The script is available online at http://bit.ly/2fJWHvm and in the supplementary material of this article (see above).
6. Concerning naming of places, we follow Kinsella, Murdock, and O’Hare (Citation2011) and understand city and town interchangeably as “a major populated place” whereas we understand a federal state as “a primary administrative area”.
7. We cleaned the list of double entries to make the automated coding explicit. If there were two or more towns with the same name, we kept the one with the highest population. If towns had roughly the same population we deleted all. We also stripped the list of town names with double meanings, like Norden (“North”) and Sachsen (“Saxony”) in Bavaria. After modification, the list contained 14,786 entries.
8. In Hecht et al.’s (Citation2011) sample of 10,000 English-speaking Twitter users, 82.0 percent filled in the location field. In Kinsella, Murdock, and O’Hare’s (Citation2011) analysis, 80.3 percent of users tweeting in English provided this information.
9. In comparison, Hecht et al. (Citation2011) could identify 51.5 percent of user location entries on the city level with manual coding.
10. Definitive statements about the causal direction of this relationship are not possible. Nevertheless, it seems more likely that translocal followers are attracted by certain topics than that newspapers are basing their coverage style on how translocal their readership is.
11. In contrast to influential American titles (Herdağdelen et al. Citation2013), foreign audiences are expected to be limited as content is exclusively offered in German language.
12. Hecht et al. (Citation2011) showed that the Yahoo! Geocoder also assigned geographic coordinates to freeform location entries such as “Middle Earth”, “somewhere ova the rainbow” and “Wherever yo mama at”.