Does Bad News Go Away Faster?


  • Shaomei Wu Cornell University
  • Chenhao Tan Cornell University
  • Jon Kleinberg Cornell University
  • Michael Macy Cornell University


We study the relationship between content and temporal dynamics of information on Twitter, focusing on the persistence of information. We compare two extreme temporal patterns in the decay rate of URLs embedded in tweets, defining a prediction task to distinguish between URLs that fade rapidly following their peak of popularity and those that fade more slowly. Our experiments show a strong association between the content and the temporal dynamics of information: given unigram features extracted from corresponding HTML webpages, a linear SVM classifier can predict the temporal pattern of URLs with high accuracy. We further explore the content of URLs in the two temporal classes using various textual analysis techniques (via LIWC and trend detection). We find that the rapidly-fading information contains significantly more words related to negative emotion, actions, and more complicated cognitive processes, whereas the persistent information contains more words related to positive emotion, leisure, and lifestyle.




How to Cite

Wu, S., Tan, C., Kleinberg, J., & Macy, M. (2021). Does Bad News Go Away Faster?. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 646-649. Retrieved from