Bad news travel fast: a content-based analysis of interestingness on Twitter
By Nasir Naveed, Thomas Gottron, J´erˆome Kunegis, Arifah Che Alhadi. Web Science Conference, 2011
431 citations recorded by the source corpus.
Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f68984b6e364e636ef82040e80476aa6bf495300
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Xiangnan He
- Min-Yen Kan
- Tao Chen
- Tat-seng Chua
- J´erˆome Kunegis
- Arifah Che Alhadi
- Nasir Naveed
- Thomas Gottron
In Sociology and Political Science
In Statistical and Nonlinear Physics
- Xiangnan He
- Min-Yen Kan
- Fabrício Benevenuto
- Tao Chen
- J´erˆome Kunegis
- Arifah Che Alhadi
- Nasir Naveed
- Thomas Gottron
Citations in the corpus
- Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp (2017, neutral): “Research has shown that on Twitter, in part due to these characteristics, content is mainly negatively valenced even when it concerns positive events (Naveed et al., 2011; Thelwall et al., 2011).”
- Context-aware Image Tweet Modelling and Recommendation (2016, background): “We first describe the four strategies that construct contexts from different data sources (cf. Figure 2), and then discuss the rules to fuse the contexts which help to improve text quality and save the acquisition cost.”
- Demographics of News Sharing in the U.S. Twittersphere (2017, background): “Naveed et al. [29] showed that bad news tends to spread faster in systems like Twitter.”
- Searching microblogs: coping with sparsity and document quality (2011, methodology): “We follow our approach from [12] to determine interestingness via the probability of a tweet being retweeted.”