Twitter Content Eliciting User Engagement-Reference-Cited by-同舟云学术

Twitter Content Eliciting User Engagement

Published:2017 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion
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Author:

Kim Sunghwan Mac¹,Kageura Kyo¹,McHugh James¹,Nepal Surya¹,Paris Cécile¹,Robinson Bella²,Sparks Ross¹,Wan Stephen¹

Affiliation:

1. CSIRO, Sydney, Australia

2. CSIRO, Canberra, Australia

Publisher

ACM Press

Reference8 articles.

1. C. Bauckhage, K. Kersting, and F. Hadiji. Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes. ICWSM, 2013.

2. M. M. Bradley and P. J. Lang. Affective norms for english words (ANEW): Instruction manual and affective ratings. Technical report, 1999.

3. Y. Hu, S. Farnham, and K. Talamadupula. Predicting user engagement on twitter with real-world events. ICWSM, 2015.

4. N. Naveed, T. Gottron, J. Kunegis, and A. Che Alhadi. Bad news travel fast: A content-based analysis of interestingness on twitter. WebSci, 2011.

5. O. Owoputi, B. O'Connor, C. Dyer, K. Gimpel, N. Schneider, and N. A. Smith. Improved part-of-speech tagging for online conversational text with word clusters. NAACL-HLT, pages 380--390, 2013.

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