TEXT: Content Attention and Engagement

Projects in the Text theme aim to develop a deeper understanding of how users engage with media content, what content factors may predict content success, and how to create content summaries.

We use controlled lab experiments and other social science methods, as well as machine learning, natural language processing, and statistical modeling to analyze human attention and interaction as it occurs in the real world.

Text Theme News

Text Theme Publications

Using BERT Performance in Propaganda Analysis

Yiqing Hua

Presented at the 2nd Workshop on NLP for Internet Freedom

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Understanding Reader Backtracking Behavior in Online News Articles

Uzi Smajda, Max Grusky, Yoav Artzi, and Mor Naaman

Presented at WWW 2019.

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The Role of Source, Headline, and Expressive Responding in Political News Evaluation

Maurice Jakesch, Moran Koran, Anna Evtushenko, and Mor Naaman

Presented at Computation & Journalism 2019, Feb 1-2, Miami, FL

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Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies

Max Grusky, Mor Naaman, and Yoav Artzi

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics.

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Modeling Sub-Document Attention Using Viewport Time

Max Grusky, Jeiran Jahani, Josh Schwartz, Dan Valente, Yoav Artzi, and Mor Naaman

Proceedings of the 2017 CHI Conference on Human Factors in Comput Systems, p 6475.

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A Data-Driven Study of View Duration on YouTube

Minsu Park, Mor Naaman, Jonah Berger

Proceedings of the International Conference on Weblogs and Social Media (ICWSM) 2016.

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