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How Does Facebook Trends Affect News Exposure?

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How Does Facebook Trends Affect News Exposure? How does Facebook Trends Affect News Exposure? Shin Lee Advised by Professor Nicholas Diakopoulos Northwestern University Mathematical Methods and Social Sciences Thesis Acknowledgement I would like to greatly thank my advisor, Professor Nicholas Diakopoulos for providing helpful feedback and guidance during my journey producing and writing this thesis. This thesis would not be where it is today without his wisdom and weekly guidance. I would also like to thank Professor Joseph Ferrie and Nicole Schneider for being great resources whenever I had questions or concerns. Lastly, I would like to thank Professor Jeffry Ely for his leadership overseeing the MMSS program and its students. Abstract Facebook has recently been a topic of hot discussion regarding fake news and algorithms that present bias posts. I wanted to discover whether Facebook is truly posting bias new trends and whether this may heavily influence the new sources exposure. Using programming and analytical methods, I investigated personalization by geographic location and demographics in Facebook’s Trending Topics. I further analyzed the degree of which Facebook personalizes the news trends per person. I also discover whether Facebook gives priority to certain new sources or if it gives equal opportunity for all news sources. Lastly, I investigated how often Facebook’s algorithm updates its news trends and if there were any differences in behavior at different days of the week. The data collection and analysis were conducted with Python scripts I wrote. My results show Facebook does not personalize by geo-location and personalizes very slightly by demographics. Furthermore, my analysis showed Facebook gives more news exposure to liberal than conservative news sources. Lastly, Facebook’s Trending Topics does not have a consistent behavior and tends to have a higher number of new cumulative news trends when there is a lot of news at the time in the world. I would like to note that this paper goes into detail of basic programming concepts as my primary audience’s expertise are not in computer science and programming. Another note is that my advisor plans on furthering my research in the future. Thus, I include some tips for future development of my thesis throughout this paper. 1 I. Introduction Facebook has received high levels of attention after the Facebook founder and CEO sat in front of Congress to answer questions about Facebook and the safety of its user following the Cambridge Analytica scandal1. The Cambridge Analytica scandal began when Facebook allowed a professor from University of Cambridge to collect information about its users for research purposes. However, the data which consisted of over 50 million profiles including answers to a personality test, location, friends list, and “liked” content, was handed over to Cambridge Analytica, a UK-based political data company that was working on Donald Trump’s campaign2. It’s clear Facebook has its individual users personalized data, but do they use these data to present certain types of new trends to its users? Cass Sunstein believes Facebook feeds are “echo chambers” that only show posts by people who are and think like us3. He claims this hurts our democracy because Facebook users only read articles that align with their beliefs. As a result, people become more extreme and when they see people who do not share the same beliefs, they become enemies who are “crazy”4. Sunstein predicted that Facebook will begin to experiment with the algorithm that determines which news and posts are presented to its users; and he was correct. In 2018, Facebook announced they will present “less public content, including videos and other posts from publishers or businesses” and increase the visibility of local news. Mark Zuckerberg states local news help us understand issues that affect our lives.5 Thus, it’s apparent that Facebook algorithms have a significant role on what its users see, and Facebook is currently making effort to increase news that are relevant to its users. However, when Zuckerberg said he wanted to increase news that are relevant to its users, will that also increase bias news sources as Sunstein warned us about? Algorithm auditing in theory is relatively simple: It is to examine the inputs, outputs, and outcomes of some problem6. However, in practice, it is a much harder to achieve. The algorithm is replacing the human involvement of data collection, data analysis, and human input, which may be faster and more efficient since the human brain has limited computational abilities. However, it comes with its own struggles such as consistencies, intention behind the algorithm, unintentional or intentional biases, making sure the algorithm is behaving the way the programmer expected, and many more. In this thesis, we dive deep into programming and take a closer look at how Facebook interacts with automatic scraping of its data, the struggles computationally behind Facebook data collection and analysis, and the process of creating Python and Selenium scripts to automatically gather large amounts of data. Up to this point, relevant research has investigated algorithms and the role they play on reporting and analyzing information and whether an algorithm is accountable and to what 1 Tuesday, For five hours on. “Your Facebook Data Scandal Questions Answered.” CNNMoney, Cable News Network, 11 Apr. 2018, money.cnn.com/2018/04/11/technology/facebook-questions-data-privacy/index.html. 2 Riley, Charles. “Cambridge Analytica, Facebook and Your Data: Here's What to Know.” CNNMoney, Cable News Network, 20 Mar. 2018, money.cnn.com/2018/03/19/technology/facebook-data-scandal-explainer/index.html? iid=EL 3 “'#Republic' Author Describes How Social Media Hurts Democracy.” NPR, NPR, 20 Feb. 2017, www.npr.org/2017/02/20/516292286/-republic-author-describes-how-social-media-hurts-democracy. 4 “'#Republic' Author Describes How Social Media Hurts Democracy.” NPR, NPR, 20 Feb. 2017, 5 Brown, Pete. “Facebook Struggles to Promote 'Meaningful Interactions' for Local Publishers, Data Shows.” Columbia Journalism Review, 18 Apr. 2018, www.cjr.org/tow_center/facebook-local-news.php. 6 Rosén, Josefin. “What Every Business Manager Should Know about Algorithm Audits.”SAS Learning Post, 16 Oct. 2017, blogs.sas.com/content/hiddeninsights/2017/10/16/algorithm-audits/. 2 degree.7 Other studies have investigated how algorithms on social media are becoming echo chambers because the algorithms decide what to present to its users8. However, because Facebook and online personalization is still an innovative concept, there lacks research exploring if Facebook News Trends are personalized by demographic and geo-location, how often trends update, and whether Facebook gives favoritism to certain news sources. This paper studies personalization of Facebook news trends by geolocation and demographic as well as if there is any favoritism of specific news sources by Facebook. Additionally, it analyzes how often Facebook updates its news trends. I hypothesize that Facebook News Trends are personalized by different demographics and geo-location. Furthermore, I hypothesize that Facebook may give some favoritism to certain news sources that are more liberal than conservative because Facebook is known be more liberal as a company, but only slight favoritism. Lastly, I hypothesize that Facebook News Trends will not have a consistent update schedule as news change depending on what happens around the world. The data used for my thesis was collected by Python and Selenium scripts that I developed for this project. There are four categories: Trends, Trends and Tabs, Geo-location, and Personal vs Puppet. Each category has its set of datasets that were collected in real-time from Facebook. The data is stored into a database and further processed by algorithms to answer the following questions: • How often are trends updating? • Is there different news trend behavior on the weekend verse the weekday? What about different days on the weekdays? • Which news sources does Facebook give more exposure to? • How many news articles from external news sources does Facebook publish? • Which news sources does Facebook include in the “Trending” section? • Do news trends differ depending on geographic location? If so, how? • Does Facebook personalize news trends by user? More specifically, do news trends differ depending on the Facebook account? To my best understanding and knowledge, there are no research that answered the questions listed above. This paper investigates and answers the questions listed and provides insight on how the end users experience Facebook’s Trending Topic news. This paper and is structured as follows: Section II consists of a summary of relevant research on social media, traditional news, and news sources, Facebook news trends, and recent Facebook events. Section III presents the methodology on how data was collected, the technology used, details of the raw datasets, and descriptions of the methods used to analyze the data. Section IV presents the results; Section V offers the discussion which includes the limitations and implications of my thesis as well as future research areas. Lastly, Section VI presents the conclusion. 7 Diakopoulos, Nicholas. “Algorithmic Accountability.” Digital Journalism, vol. 3, no. 3, 2014, pp. 398–415., doi:10.1080/21670811.2014.976411. 8 Alvarado, Oscar, and Annika Waern. “Towards Algorithmic Experience.” Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18, 2018, doi:10.1145/3173574.3173860.
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