Getting to the Core of Algorithmic News Curators: A Case Study of - PowerPoint PPT Presentation

Getting to the Core of Algorithmic News Curators: A Case Study of Apple News Jack Bandy (Northwestern University) @jackbandy

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My Research Ideation Machine (Beta) (platform or technology) (vice or trait) Facebook Google Search antisocial Netflix intolerant Google Ads biased Is making us ? GPS impatient YouTube naive Reddit uninformed Apple News � 3

Apple News � 4

Related Work • Apple News • Columbia Journalism Review (Brown) • New York Times (Nicas) • Algorithm Audits (Sandvig) • Underrepresentation (Kay) • Filter Bubbles (Bakshy) • News Platform Audits • Google News (Haim; Nechushtai) � 5

Research Questions • How do news curation systems like Apple News influence the public’s media intake? • What is the system’s mechanism ? • How often does it update? • Does it localize or personalize? • What content does it direct attention to? • What sources does it feature? • What topics does it feature? � 6

Black Box Dilemma • Proprietary code • No public APIs or endpoints • SSL Pinning • Possible data collection methods: • Apple News Twitter (Brown) • Email Newsletters (Brown) • Crowdsource $ ./scrape_apple_news ERROR $ ./scrape_apple_news ERROR $ ./scrape_apple_news ERROR � 7

Methods: The Crowd • Amazon Mechanical Turk • Pros • Circumvents black box • Real-world data • High parallelism/throughput • Cons • Data Verification • Inconsistent coverage � 8

Methods: Appium • Automated App Control • Pros • Lower cost • Sustained coverage • No manual inspection • Cons • Single channel • Data points in vitro � 9

Findings: Source Concentration Relative Distribution of Trending Stories Combined: December 12th-20th, 2018; January 4th-12th, 2019 Fox News CNN People Hu ff Post Politico Newsweek BuzzFeed Vanity Fair Vox Washington Post 0 5 10 15 20 25 % of Trending Stories (n=576) � 10

Findings: The Human Touch Trending Stories Top Stories Algorithmic Editorial Sta ff Curation Headlines Displayed 4 (6 on big screens) 5 Localization National National Personalization No No Avg. Stories / Day 31 16 Total Stories 279 144 Total Sources 28 40 Avg. Stories / Source 9.9 3.6 Stdev. Stories / Source 14.6 3.3 #1 Source % 20.1% (Fox) 9.0% (WaPo) #1-#3 Sources % 50.5% 25.7% #1-#10 Sources % 85.7% 55.6% � 11 Data collected January 4th-12th, 2019

Conclusions & Next Steps How does Apple News affect Local and Regional news outlets? • How do people actually use the app? Do they prefer one section? • Do similar patterns (source concentration, the human touch) show • up in other aggregators? Have ideas? Reach out! @jackbandy jackbandy.com • Computational Journalism Lab � 12

Sandvig , C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing Algorithms: Research Methods for • Detecting Discrimination on Internet Platforms. In Data and discrimination: converting critical concerns into productive inquiry (pp. 1--23). Retrieved from https://pdfs.semanticscholar.org/ b722/7cbd34766655dea10d0437ab10df3a127396.pdf Kay , M., Matuszek, C., & Munson, S. A. (2015). Unequal Representation and Gender Stereotypes in Image • Search Results for Occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI ’15 (pp. 3819–3828). https://doi.org/10.1145/2702123.2702520 Bakshy , E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on • Facebook (supplementary materials). Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160 Haim , M., Graefe, A., & Brosius, H. B. (2018). Burst of the Filter Bubble?: Effects of personalization on the • diversity of Google News. Digital Journalism, 6(3), 330–343. https://doi.org/10.1080/21670811.2017.1338145 Nechushtai , E., & Lewis, S. C. (2019). What kind of news gatekeepers do we want machines to be? Filter • bubbles, fragmentation, and the normative dimensions of algorithmic recommendations. Computers in Human Behavior, 90, 298–307. https://doi.org/10.1016/j.chb.2018.07.043 Brown , P . (2018). Study: Apple News’s human editors prefer a few major newsrooms. Columbia Journalism • Review. Retrieved from https://www.cjr.org/tow_center/study-apple-newss-human-editors-prefer-a-few-major- newsrooms.php Nicas , J. (2018). Apple News’s Radical Approach: Humans Over Machines. New York Times. Retrieved from • https://www.nytimes.com/2018/10/25/technology/apple-news-humans-algorithms.html � 13