Predicting Responses to Microblog Posts

By Yoav Artzi, Patrick Pantel, Michael Gamon. 2012

96 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jure Leskovec
2. Yoshua Bengio
3. Zhe Gan
4. Charu C. Aggarwal
5. Mohit Bansal
6. Quoc V. Le
7. Richard Socher
8. Chunyuan Li

Citations in the corpus

• Smart Reply: Automated Response Suggestion for Email (2016, background): “Much work exists on analyzing natural language dialogues in public domains such as Twitter, but it has largely focused on social media tasks like predicting whether or not a response is made [3], predicting next word only [14], or curating threads [4].”
• Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “Keywords user behavior; cohorts; computational social science; Reddit; temporal”
• Interpretation of Semantic Tweet Representations (2017, background): “Response Prediction: This is a social task to identify if a given tweet can receive a response [25].”
• The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter (2014, background): “Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 175–185, Baltimore, Maryland, USA, June 23-25 2014. c©2014 Association for Computational Linguistics”
• Characterizing and curating conversation threads: expansion, focus, volume, re-entry (2013, background): “How fast a piece of information spreads or diffuses [3, 4, 16, 18, 21] is another important aspect of interestingness.”