The annual Web Science Trust (WST) Test of Time Award has been presented to Nasir Naveed, Thomas Gottron, Jérôme Kunegis, and Arifah Che Alhadi for their paper titled “Bad news travel fast: A content-based analysis of interestingness on twitter”.
The authors are the fifth group of researchers to receive the WST Test of Time Award, which was announced during the 2026 ACM Web Science Conference in Braunschweig, Germany.
The General Chair of this year’s conference, Professor Wolf-Tilo Balke, said:
“While the paper may seem dated, being as it was specifically about Twitter, it had a much more enduring point to make. As social media has expanded, and become an increasing component of the traffic on the web (and thus also often the inputs to AI LLMs), the point of this paper is evergreen and expanding – in exploring what types of information propagates, and especially noticing the tendency for bad news to propagate faster than many other types of media.”
The prize-winning paper was first presented at the 2011 ACM Web Science Conference in Koblenz, Germany, and was selected by a WST Test of Time Award Committee chaired by Professor Jim Hendler who explained what had impressed the judging panel:
“This paper embodies the spirit of the Test of Time Award – not only does it present an interesting set of conclusions, but the methodology combined both data analytics and social science – embodying the spirit of web science. The committee was also impressed by the depth of their analysis and the validation methods used. In this it is a model of the “science” aspect of web science.”
Dame Wendy Hall, Chair of the Web Science Trust, added:
“The Web Science Trust is proud to recognise this work, which has not only stood the test of time but has also demonstrated interdisciplinarity without any sacrifice of rigour – a strong example of what the Trust stands for.”
The authors commented:
We are deeply honoured to receive this recognition — it is not something any of us anticipated when we first sat down to look at what constitutes interestingness on Twitter back in 2011. What began as an exploration of content dynamics on Twitter has proven to provide much broader insights about how information spreads across the social web. In an era of generative AI, algorithmic feeds, and global misinformation challenges, understanding what news travels fast (and why) seems more critical than ever. We hope this work continues to inspire rigorous, interdisciplinary research into the forces shaping our digital world today.