Anticipating Attention: On the Predictability of News Headline Tests

References

• Anderson, C. 2011. “Between Creative and Quantified Audiences: Web Metrics and Changing Patterns of Newswork in Local US Newsrooms.” Journalism: Theory, Practice & Criticism 12 (5): 550–566.
   Google Scholar
• Arapakis, I., B. B. Cambazoglu, and M. Lalmas. 2017. “On the Feasibility of Predicting Popular News at Cold Start.” Journal of the Association for Information Science and Technology 68 (5): 1149–1164.
   Web of Science ®Google Scholar
• Belyaeva, Evgenia, Aljaž. Košmerlj, Dunja Mladenić, and Gregor Leban. 2018. “Automatic Estimation of News Values Reflecting Importance and Closeness of News Events.” Informatica 42 (4): 527–533.
   Web of Science ®Google Scholar
• Berger, J., and K. L. Milkman. 2012. “What Makes Online Content Viral?” Journal of Marketing Research 49 (2): 192–205.
   Web of Science ®Google Scholar
• Blom, J. N., and K. R. Hansen. 2015. “Click Bait: Forward-Reference as Lure in Online News Headlines.” Journal of Pragmatics 76: 87–100.
   Web of Science ®Google Scholar
• Boukes, Mark, Bob van de Velde, Theo Araujo, and Rens Vliegenthart. 2020. “What’s the Tone? Easy Doesn’t Do It: Analyzing Performance and Agreement between off-the-Shelf Sentiment Analysis Tools.” Communication Methods and Measures 14 (2): 83–104.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.
   Web of Science ®Google Scholar
• Chan, C.-H., J. Bajjalieh, L. Auvil, H. Wessler, S. Althaus, K. Welbers, W. van Atteveldt, et al. 2021. “Four Best Practices for Measuring News Sentiment Using ‘off-the-Shelf’ Dictionaries: A Large-Scale p-Hacking Experiment.” Computational Communication Research 3 (1): 1–27.
   Google Scholar
• Coddington, M., S. C. Lewis, and V. Belair-Gagnon. 2021. “The Imagined Audience for News: Where Does a Journalist’s Perception of the Audience Come from?” Journalism Studies 22 (8): 1028–1046.
   Web of Science ®Google Scholar
• Conneau, A., H. Schwenk, L. Barrault, and Y. Lecun. 2017. “Very Deep Convolutional Networks for Text Classification.” In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Valencia, Spain, 2017, 1107–1116. Association for Computational Linguistics.
   Google Scholar
• di Buono, M. P., J. Šnajder, B. Dalbelo Basic, G. Glavaš, M. Tutek, and N. Milic-Frayling. 2017. “Predicting News Values from Headline Text and Emotions.” In: Proceedings of the 2017 EMNLP Workshop: Natural Language Processing Meets Journalism, Copenhagen, Denmark, 2017, 1–6. Association for Computational Linguistics.
   Google Scholar
• Diakopoulos, N. 2019. Automating the News: How Algorithms Are Rewriting the Media. Cambridge, Massachusetts: Harvard University Press.
   Google Scholar
• Dor, D. 2003. “On Newspaper Headlines as Relevance Optimizers.” Journal of Pragmatics 35 (5): 695–721.
   Web of Science ®Google Scholar
• England, G. W., M. Thomas, and D. G. Paterson. 1953. “Reliability of the Original and the Simplified Flesch Reading Ease Formulas.” Journal of Applied Psychology 37 (2): 111–113.
   Web of Science ®Google Scholar
• Fast, E., B. Chen, and M. S. Bernstein. 2016. “Empath: Understanding Topic Signals in Large-Scale Text.” In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose California USA, 7 May 2016, 4647–4657. ACM.
   Google Scholar
• Fischer, Peter, Eva Jonas, Dieter Frey, and Stefan Schulz-Hardt. 2005. “Selective Exposure to Information: The Impact of Information Limits.” European Journal of Social Psychology 35 (4): 469–492.
   Web of Science ®Google Scholar
• Flaxman, S., S. Goel, and J. M. Rao. 2016. “Filter Bubbles, Echo Chambers, and Online News Consumption.” Public Opinion Quarterly 80 (S1): 298–320.
   Web of Science ®Google Scholar
• Fürst, S. 2020. “In the Service of Good Journalism and Audience Interests? How Audience Metrics Affect News Quality.” Media and Communication 8 (3): 270–280.
   Web of Science ®Google Scholar
• Garrett, R. K., and N. J. Stroud. 2014. “Partisan Paths to Exposure Diversity: Differences in Pro- and Counterattitudinal News Consumption.” Journal of Communication 64 (4): 680–701.
   Web of Science ®Google Scholar
• Gentile, A. L., D. Gruhl, P. Ristoski, and S. Welch. 2019. “Explore and Exploit. Dictionary Expansion with Human-in-the-Loop.” In The Semantic Web, edited by P Hitzler, M Fernández, K Janowicz, et al., Cham, 2019, 131–145. Springer International Publishing.
   Google Scholar
• Hagar, N., and N. Diakopoulos. 2019. “Optimizing Content with a/B Headline Testing: Changing Newsroom Practices.” Media and Communication 7 (1): 117–127.
   Web of Science ®Google Scholar
• Hagar, N., J. Bandy, D. Trielli, Y. Wang, and N. Diakopoulos. 2020. “Defining Local News: A Computational Approach.” In Computation + Journalism Symposium, Boston, MA, 2020.
   Google Scholar
• Harcup, T., and D. O’Neill. 2017. “What is News?: News Values Revisited (Again).” Journalism Studies 18 (12): 1470–1488.
   Web of Science ®Google Scholar
• Hastie, T., R. Tibshirani, and J. Friedman. 2009. “Random Forests.” In The Elements of Statistical Learning, 587–604. New York, NY: Springer.
   Google Scholar
• Hindman, M. 2015. “Building Better Models: Prediction, Replication, and Machine Learning in the Social Sciences.” The ANNALS of the American Academy of Political and Social Science 659 (1): 48–62.
   Web of Science ®Google Scholar
• Honnibal, M., I. Montani, S. Van Landeghem, and A. Boyd. 2020. SpaCy: Industrial-Strength Natural Language Processing in Python. Zenodo
   Google Scholar
• Hutto, C. J., and E. Gilbert. 2014. “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text.” In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media.
   Google Scholar
• Ifantidou, E. 2009. “Newspaper Headlines and Relevance: Ad Hoc Concepts in Ad Hoc Contexts.” Journal of Pragmatics 41 (4): 699–720.
   Web of Science ®Google Scholar
• Iyengar, S., and K. S. Hahn. 2009. “Red Media, Blue Media: Evidence of Ideological Selectivity in Media Use.” Journal of Communication 59 (1): 19–39.
   Web of Science ®Google Scholar
• Jungherr, A., O. Posegga, and J. An. 2019. “Discursive Power in Contemporary Media Systems: A Comparative Framework.” The International Journal of Press/Politics 24 (4): 404–425.
   Web of Science ®Google Scholar
• Karnowski, V., D. J. Leiner, A. Sophie Kümpel, and L. Leonhard. 2021. “Worth to Share? How Content Characteristics and Article Competitiveness Influence News Sharing on Social Network Sites.” Journalism & Mass Communication Quarterly 98 (1): 59–82.
   Web of Science ®Google Scholar
• Kenter, T., and M. de Rijke. 2015. “Short Text Similarity with Word Embeddings.” In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM ’15, Melbourne, Australia, 2015, 1411–1420. ACM Press.
   Google Scholar
• Kessler, S. H., and I. Engelmann. 2019. “Why Do we Click? Investigating Reasons for User Selection on a News Aggregator Website.” Communications 44 (2): 225–247.
   Google Scholar
• Kim, J. H., A. Mantrach, A. Jaimes, and A. Oh. 2016. “How to Compete Online for News Audience: Modeling Words That Attract Clicks.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, San Francisco, California, USA, 2016, 1645–1654. ACM Press.
   Google Scholar
• Klinenberg, E. 2005. “Convergence: News Production in a Digital Age.” The ANNALS of the American Academy of Political and Social Science 597 (1): 48–64.
   Web of Science ®Google Scholar
• Kormelink, T. G., and I. C. Meijer. 2018. “What Clicks Actually Mean: Exploring Digital News User Practices.” Journalism (London, England) 19 (5): 668–683.
   PubMedGoogle Scholar
• Kuiken, J., A. Schuth, M. Spitters, and M. Marx. 2017. “Effective Headlines of Newspaper Articles in a Digital Environment.” Digital Journalism 5 (10): 1300–1314.
   Web of Science ®Google Scholar
• Lamberson, P. J., and S. Soroka. 2018. “A Model of Attentiveness to Outlying News.” Journal of Communication 68 (5): 942–964.
   Web of Science ®Google Scholar
• Lamprinidis, S., D. Hardt, and D. Hovy. 2018. “Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning.” In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018, 659–664.
   Google Scholar
• Lau, J. H., and T. Baldwin. 2016. “An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation.” In: Proceedings of the 1st Workshop on Representation Learning for NLP, Berlin, Germany, 2016, 78–86. Association for Computational Linguistics.
   Google Scholar
• Lerman, K., and T. Hogg. 2010. “Using a Model of Social Dynamics to Predict Popularity of News.” In: Proceedings of the 19th International Conference on World Wide Web, Raleigh, North Carolina, USA, 29 April 2010, 621–630. Association for Computing Machinery.
   Google Scholar
• Liu, C., R. W. White, and S. Dumais. 2010. “Understanding Web Browsing Behaviors through Weibull Analysis of Dwell Time.” In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, 2010, 379–386. SIGIR ’10. ACM.
   Google Scholar
• Makhortykh, M.,. C. de Vreese, N. Helberger, J. Harambam, and D. Bountouridis. 2020. “We Are What we Click: Understanding Time and Content-Based Habits of Online News Readers.” New Media & Society 23 (9): 2773–2800.
   Web of Science ®Google Scholar
• Margolin, D. B. 2019. “Computational Contributions: A Symbiotic Approach to Integrating Big, Observational Data Studies into the Communication Field.” Communication Methods and Measures 13 (4): 229–247.
   Web of Science ®Google Scholar
• Martin, T., J. M. Hofman, A. Sharma, A. Anderson, and D. J. Watts. 2016. “Exploring Limits to Prediction in Complex Social Systems.” In: Proceedings of the 25th International Conference on World Wide Web - WWW ’16, Montreal, Quebec, Canada, 2016, 683–694. ACM Press.
   Google Scholar
• McCombs, M. E., and J. P. Winter. 1981. “Defining Local News.” Newspaper Research Journal 3 (1): 16–21.
   Google Scholar
• Messing, S., and S. J. Westwood. 2014. “Selective Exposure in the Age of Social Media: Endorsements Trump Partisan Source Affiliation When Selecting News Online.” Communication Research 41 (8): 1042–1063.
   Web of Science ®Google Scholar
• Panigrahi, A., H. V. Simhadri, and C. Bhattacharyya. 2019. “Word2Sense: Sparse Interpretable Word Embeddings.” In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019, 5692–5705. Association for Computational Linguistics.
   Google Scholar
• Parks, P. 2019. “Textbook News Values: Stable Concepts, Changing Choices.” Journalism & Mass Communication Quarterly 96 (3): 784–810.
   Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–2830.
   Web of Science ®Google Scholar
• Petre, C. 2015. The Traffic Factories: Metrics at Chartbeat, Gawker Media, and The New York Times. 7 May. Tow Center for Digital Journalism. Accessed 10 December 2018. https://www.cjr.org/tow_center_reports/the_traffic_factories_metrics_at_chartbeat_gawker_media_and_the_new_york_times.php/.
   Google Scholar
• Rayson, S. 2017. We Analyzed 100 Million Headlines. Here’s What We Learned (New Research). Accessed 15 October 2018. https://buzzsumo.com/blog/most-shared-headlines-study/.
   Google Scholar
• Reimers, N., and I. Gurevych. 2019. “Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks.” In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, 3980–3990. Association for Computational Linguistics.
   Google Scholar
• Ross, A. A. 2017. “If Nobody Gives a Shit, is It Really News?”: Changing Standards of News Production in a Learning Newsroom.” Digital Journalism 5 (1): 82–99.
   Web of Science ®Google Scholar
• Salganik, M. J., P. S. Dodds, and D. J. Watts. 2006. “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market.” Science (New York, N.Y.) 311 (5762): 854–856.
   PubMed Web of Science ®Google Scholar
• Shin, J., A. Madotto, and P. Fung. 2018. “Interpreting Word Embeddings with Eigenvector Analysis.” In Workshop on Interpretability and Robustness in Audio, Speech, and Language (IRASL), 2018. NeurIPS IRASL.
   Google Scholar
• Shmueli, G. 2010. “To Explain or to Predict?” Statistical Science 25 (3): 289–310.
   Web of Science ®Google Scholar
• Soroka, S.,. P. Fournier, and L. Nir. 2019. “Cross-National Evidence of a Negativity Bias in Psychophysiological Reactions to News.” Proceedings of the National Academy of Sciences of the United States of America 116 (38): 18888–18892.
   PubMed Web of Science ®Google Scholar
• Steensen, S.,. R. Ferrer-Conill, and C. Peters. 2020. “(against a) Theory of Audience Engagement with News.” Journalism Studies 21 (12): 1662–1680.
   Web of Science ®Google Scholar
• Szabo, G., and B. A. Huberman. 2010. “Predicting the Popularity of Online Content.” Communications of the ACM 53 (8): 80–88.
   Web of Science ®Google Scholar
• Szymanski, T., C. Orellana-Rodriguez, and M. T. Keane. 2016. “Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines.” Natural Language Processing Meets Journalism. IJCAI.
   Google Scholar
• Tatar, Alexandru, Panayotis Antoniadis, Marcelo Dias de Amorim, and Serge Fdida. 2014. “From Popularity Prediction to Ranking Online News.” Social Network Analysis and Mining 4 (1): 174.
   Google Scholar
• Tausczik, Y. R., and J. W. Pennebaker. 2010. “The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods.” Journal of Language and Social Psychology 29 (1): 24–54.
   Web of Science ®Google Scholar
• Tetlock, P., and D. Gardner. 2015. Superforecasting: The Art and Science of Prediction. New York, NY: Crown Publishers.
   Google Scholar
• Trilling, D., P. Tolochko, and B. Burscher. 2017. “From Newsworthiness to Shareworthiness: How to Predict News Sharing Based on Article Characteristics.” Journalism & Mass Communication Quarterly 94 (1): 38–60.
   Web of Science ®Google Scholar
• Valenzuela, S., M. Piña, and J. Ramírez. 2017. “Behavioral Effects of Framing on Social Media Users: How Conflict, Economic, Human Interest, and Morality Frames Drive News Sharing: Framing Effects on News Sharing.” Journal of Communication 67 (5): 803–826.
   Web of Science ®Google Scholar
• Waldherr, A. 2014. “Emergence of News Waves: A Social Simulation Approach: Emergence of News Waves.” Journal of Communication 64 (5): 852–873.
   Web of Science ®Google Scholar
• Webster, J. G. 2016. “The Marketplace of Attention.” In The Marketplace of Attention: How Audiences Take Shape in a Digital Age. Cambridge: The MIT Press.
   Google Scholar
• Zillmann, D., L. Chen, S. Knobloch, and C. Callison. 2004. “Effects of Lead Framing on Selective Exposure to Internet News Reports.” Communication Research 31 (1): 58–81.
   Web of Science ®Google Scholar