Toward a Better Understanding of News User Journeys: A Markov Chain Approach

References

• Althoen, S. C., L. King, and K. Schilling. 1993. “How Long Is a Game of Snakes and Ladders?” The Mathematical Gazette 77 (478): 71–76.
   Google Scholar
• Anderson, T. W., and Leo A. Goodman. 1957. “Statistical Inference About Markov Chains.” The Annals of Mathematical Statistics 28 (1): 89–110.
   Google Scholar
• Athey, Susan, and Markus Mobius. 2012. “The Impact of News Aggregators on Internet News Consumption: The Case of Localization.”
   Google Scholar
• Barnhurst, Kevin G. 2010. “The Form of Reports on Us Newspaper Internet Sites, an Update.” Journalism Studies 11 (4): 555–566.
   Web of Science ®Google Scholar
• Billingsley, Patrick. 1961. “Statistical Methods in Markov Chains.” The Annals of Mathematical Statistics 32 (1): 12–40.
   Google Scholar
• Boczkowski, Pablo J., Eugenia Mitchelstein, and Martin Walter. 2011. “Convergence across Divergence: Understanding the Gap in the Online News Choices of Journalists and Consumers in Western Europe and Latin America.” Communication Research 38 (3): 376–396.
   Web of Science ®Google Scholar
• Bruns, Axel. 2018. Gatewatching and News Curation: Journalism, Social Media, and the Public Sphere. New York, NY: Lang.
   Google Scholar
• Bukiet, Bruce, Elliotte Rusty Harold, and Jose Luis Palacios. 1997. “A Markov Chain Approach to Baseball.” Operations Research 45 (1): 14–23.
   Web of Science ®Google Scholar
• De Vreese, Claes H., Mark Boukes, Andreas Schuck, Rens Vliegenthart, Linda Bos, and Yph Lelkes. 2017. “Linking Survey and Media Content Data: Opportunities, Considerations, and Pitfalls.” Communication Methods and Measures 11 (4): 221–244.
   Web of Science ®Google Scholar
• Deuze, Mark. 2003. “The Web and Its Journalisms: Considering the Consequences of Different Types of Newsmedia Online.” New Media & Society 5 (2): 203–230.
   Web of Science ®Google Scholar
• Dvir-Gvirsman, S., Y. Tsfati, and E. Menchen-Trevino. 2014. “The Extent and Nature of Ideological Selective Exposure Online: Combining Survey Responses with Actual Web Log Data from the 2013 Israeli Elections.” New Media & Society 18 (5): 857–877.
   Web of Science ®Google Scholar
• Eirinaki, Magdalini, Michalis Vazirgiannis, and Dimitris Kapogiannis. 2005. “Web Path Recommendations Based on Page Ranking and Markov Models.” In Proceedings of the seventh ACM international workshop on Web information and data management – WIDM ‘05, New York, NY, USA, pp. 2. ACM Press.
   Google Scholar
• Ferrari, Fiorenzo, and Abraham Wyner. 2003. “Estimation of General Stationary Processes by Variable Length Markov Chains.” Scandinavian Journal of Statistics 30 (3): 459–480.
   Web of Science ®Google Scholar
• Flaxman, Seth, Sharad Goel, and Justin M. Rao. 2016. “Filter Bubbles, Echo Chambers, and Online News Consumption.” Public Opinion Quarterly 80: 298–320.
   Web of Science ®Google Scholar
• Fletcher, Richard, and Rasmus Kleis Nielsen. 2018. “Are People Incidentally Exposed to News on Social Media? A Comparative Analysis.” New Media & Society 20 (7): 2450–2468.
   Web of Science ®Google Scholar
• Fygenson, Mendel. 1989. “A Fundamental Matrix for Regular Semi-Markov Processes.” Stochastic Processes and Their Applications 32 (1): 151–160.
   Web of Science ®Google Scholar
• George, Dileep, and Jeff Hawkins. 2009. “Towards a Mathematical Theory of Cortical Micro-Circuits.” PLoS Computational Biology 5 (10): 1–26.
   Web of Science ®Google Scholar
• Gilks, W. R., S. Richardson, and D. J. Spiegelhalter. 1996. Markov Chain Monte Carlo in Practice. New York, NY: Chapman and Hall.
   Google Scholar
• Giomelakis, Dimitrios, Efstathios Sidiropoulos, Sotiria Gilou, and Andreas Veglis. 2019. “The Utilization of Web Analytics in Online Greek Journalism.” Journalism Studies 20 (5): 609–630.
   Web of Science ®Google Scholar
• Hagar, Nick, and Nicholas Diakopoulos. 2019. “Optimizing Content with A/B Headline Testing: Changing Newsroom Practices.” Media and Communication 7 (1): 117.
   Web of Science ®Google Scholar
• Hermida, Alfred, Fred Fletcher, Darryl Korell, and Donna Logan. 2012. “Share, Like Recommend: Decoding the Social Media News Consumer.” Journalism Studies 13 (5-6): 815–824.
   Web of Science ®Google Scholar
• Hiscott, Richard N. 1981. “Chi-Square Tests for Markov Chain Analysis.” Journal of the International Association for Mathematical Geology 13 (1): 69–80.
   Google Scholar
• Hopp, Frederic, Jacob Fisher, and Rene Weber. 2019. “The Dynamic Relationship between News Frames and Real-World Events: A Hidden Markov Model Approach.” Paper presented at the annual meeting of the International Communication Association (ICA), Washington, DC, USA.
   Google Scholar
• Juang, Biing Hwang, and Laurence R Rabiner. 1991. “Hidden Markov Models for Speech Recognition.” Technometrics 33 (3): 251–272.
   Web of Science ®Google Scholar
• Kleppe, Martijn, and Marco Otte. 2017. “Analysing and Understanding News Consumption Patterns by Tracking Online User Behaviour with a Multimodal Research Design.” Digital Scholarship in the Humanities 32 (2): 158–170.
   Google Scholar
• Lazariv, Taras, and Christoph Lehmann. 2018. “Goodness-of-Fit Tests for Large Datasets.”
   Google Scholar
• MacGregor, Phil. 2007. “Tracking the Online Audience.” Journalism Studies 8 (2): 280–298.
   Google Scholar
• Machler, Martin, and Peter Buhlmann. 2004. “Variable Length Markov Chains: Methodology, Computing, and Software.” Journal of Computational and Graphical Statistics 13 (2): 435–455.
   Web of Science ®Google Scholar
• Menchen-Trevino, Ericka, and Chris Karr. 2012. “Researching Real-World Web Use with Roxy: Collecting Observational Web Data with Informed Consent.” Journal of Information Technology & Politics 9 (3): 254–268.
   Google Scholar
• Mobasher, B. 2007. “Web Usage Mining.” In Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, edited by B. Liu, 450–483. New York: Springer.
   Google Scholar
• Möller, Judith, Damian Trilling, Natali Helberger, and Bram van Es. 2018. “Do Not Blame It on the Algorithm: An Empirical Assessment of Multiple Recommender Systems and Their Impact on Content Diversity.” Information, Communication & Society 7: 1–19.
   Google Scholar
• Möller, Judith, Robbert Nicolai van de Velde, Lisa Merten, and Cornelius Puschmann. 2019. “Explaining Online News Engagement Based on Browsing Behavior: Creatures of Habit?” Social Science Computer Review, Advance online publication.
   Web of Science ®Google Scholar
• Mukerjee, Subhayan, Slvia Majo-Vazquez, and Sandra Gonzalez-Bailon. 2018. “Networks of Audience Overlap in the Consumption of Digital News.” Journal of Communication 68 (1): 26–50.
   Web of Science ®Google Scholar
• O’Callaghan, Derek, Derek Greene, Maura Conway, Joe Carthy, and Padraig Cunningham. 2015. “Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems.” Social Science Computer Review 33 (4): 459–478.
   Web of Science ®Google Scholar
• Pearson, George D. H., and Gerald M. Kosicki. 2017. “How Way-Finding Is Challenging Gatekeeping in the Digital Age.” Journalism Studies 18 (9): 1087–1105.
   Web of Science ®Google Scholar
• Prior, Markus. 2005. “News vs. Entertainment: How Increasing Media Choice Widens Gaps in Political Knowledge and Turnout.” American Journal of Political Science 49 (3): 577–592.
   Web of Science ®Google Scholar
• Prior, Markus. 2009. “The Immensely Inflated News Audience: Assessing Bias in Self-Reported News Exposure.” Public Opinion Quarterly 73 (1): 130–143.
   Web of Science ®Google Scholar
• Rai, Prerna, and Arvind Lal. 2016. “Google Pagerank Algorithm: Markov Chain Model and Hidden Markov Model.” International Journal of Computer Applications 138 (9): 9–13.
   Google Scholar
• Sappelli, Maya, Dung Manh Chu, Bahadir Cambel, David Graus, and Philippe Bressers. 2018. “SMART Journalism: Personalizing, Summarizing, and Recommending Financial Economic News.” In The algorithmic personalization and news (APEN18) workshop at ICWSM18.
   Google Scholar
• Scharkow, Michael, and Marko Bachl. 2017. “How Measurement Error in Content Analysis and Self-Reported Media Use Leads to Minimal Media Effect Findings in Linkage Analyses: A Simulation Study.” Political Communication 34 (3): 323–343.
   Web of Science ®Google Scholar
• Schmidt, Jan-Hinrik, Lisa Merten, Uwe Hasebrink, Isabelle Petrich, and Amelie Rolfs. 2019. “How Do Intermediaries Shape News-Related Media Repertoires and Practices? Findings from a Qualitative Study.” International Journal of Communication 13: 853–873.
   Web of Science ®Google Scholar
• Shoemaker, Pamela J., and Akiba A. Cohen. 2005. News Around the World: Content, Practitioners, and the Public. New York, NY: Routledge.
   Google Scholar
• Tandoc, Edson C. 2014. “Journalism Is Twerking? How Web Analytics Is Changing the Process of Gatekeeping.” New Media & Society 16 (4): 559–575.
   Web of Science ®Google Scholar
• Taneja, Harsh, Angela Xiao Wu, and Stephanie Edgerly. 2018. “Rethinking the Generational Gap in Online News Use: An Infrastructural Perspective.” New Media & Society 20 (5): 1792–1812.
   Web of Science ®Google Scholar
• Tauchen, George. 1986. “Finite State Markov-Chain Approximations to Univariate and Vector Autoregressions.” Economics Letters 20 (2): 177–181.
   Web of Science ®Google Scholar
• Tewksbury, David. 2003. “What Do Americans Really Want to Know? Tracking the Behavior of News Readers on the Internet.” Journal of Communication 53 (4): 694–710.
   Web of Science ®Google Scholar
• Thorson, Kjerstin, and Chris Wells. 2016. “Curated Flows: A Framework for Mapping Media Exposure in the Digital Age.” Communication Theory 26 (3): 309–328.
   Web of Science ®Google Scholar
• Trilling, Damian. 2019. “Conceptualizing and Measuring News Exposure as Network of Users and News Items.” In Measuring Media Use and Exposure: Recent Developments and Challenges, edited by Christina Peter, Theresa Naab, and Rinaldo Kuhne, 297–317. Cologne: Halem.
   Google Scholar
• Vermeer, Susan. 2018. “A Supervised Machine Learning Method to Classify Dutch-Language News Items.”
   Google Scholar
• Wells, Chris, and Kjerstin Thorson. 2017. “Combining Big Data and Survey Techniques to Model Effects of Political Content Flows in Facebook.” Social Science Computer Review 35 (1): 33–52.
   Web of Science ®Google Scholar
• Wonneberger, Anke, Klaus Schoenbach, and Lex van Meurs. 2012. “Staying Tuned: TV News Audiences in the Netherlands 1988–2010.” Journal of Broadcasting & Electronic Media 56 (1): 55–74.
   Web of Science ®Google Scholar
• Zamith, Rodrigo, and Seth C. Lewis. 2015. “Content Analysis and the Algorithmic Coder: What Computational Social Science Means for Traditional Modes of Media Analysis.” The ANNALS of the American Academy of Political and Social Science 659 (1): 307–318.
   Web of Science ®Google Scholar