Tag: Data Collection

A Big Fat Fight Over Consumer Data Privacy: Apple’s IOS Updates Uncover Facebooks Appetite For User Data

by Hillary Hardig

Image taken from The Skier Scribbler

Users of Facebook have long dealt with ongoing changes to the application’s privacy policy. And these changes aren’t necessarily wrong. It’s now been seventeen years since the launch of the platform by creator, Mark Zuckerberg, and seventeen years since Facebook led an explosion within the realm of social media. However, Facebook’s privacy policy is often scrutinized for its language and structure of the terms, making it difficult for users to clearly choose what data they are or are not willing to share with the company. The company’s data collection policies have not gone unnoticed and users are becoming increasingly aware of internet privacy. In turn, large corporations like Apple are making strides to ensure customers that their privacy is being prioritized. This has led Facebook and Apple, two of tech’s top companies, into a war over privacy.

In 2018, Facebook rewrote its Terms of Service (TOS) and Data Use Policy (DUP) to use more plain language to clearly illustrate to users how their data is collected and used. This was likely due to theories that Facebook collects data from user microphones and SMS messaging. In 2021 TOS and DUP changed again, this time revamping the layout of data settings on the platform and decentralizing the settings into multiple different places. In a statement explaining the renovation of settings, Facebook commented, “Settings are now grouped into six broad categories, each containing several related settings: Account, Preferences, Audience and Visibility, Permissions, Your Information, and Community Standards and Legal Policies … We’ve unbundled the Privacy Settings category and moved the settings previously contained within it into other categories.” Now, if users want to change a privacy setting, they must navigate multiple pages and scan through each page individually looking for specific toggles to turn off and on. Regarding this change, Facebook stated in the same statement that “we’re confident this new settings page will make it easier for people to visit their settings, find what they came for, and make the changes they want.” But to users, controlling  their privacy settings just got a lot more difficult.

In a 2018 article published in the journal of  Social Media + Society, authors Jennifer Jiyoung Suh and Eszter Hargittai asked participants to answer questions about their Facebook use and attitudes towards privacy. Interestingly, the study found that there is a considerable discrepancy between participants’ intended online privacy management and its actual effectiveness. The study stated that “despite participants’ perceived high level of knowledge and skills of online privacy management on Facebook, the majority of the posts collected for this study were shared with more people than the participants had intended” (p. 9). Since then, Facebook has been fighting a battle with Apple who, with the release of newer iOS versions, requires that app developers explicitly ask for permission to track users’ behavior across Apple’s App Store and the internet. But gaps in Apple’s push to increase privacy don’t fully limit Facebook’s ability to collect location information from your iPhone without your awareness. 

In 2021, researchers Anatoliy Gruzd, Joanne McNeisha, Lilach Dahoah Halevib,  and Martin Phillipsa studied the change in user privacy concerns and behavior on Facebook after exposure to a privacy literacy intervention called Data Selfie. The study found that after viewing their privacy information from Data Selfie, participants became increasingly more concerned about their online privacy. However, privacy intervention tools like these are likely underutilized. Platforms like Facebook require contracts of adhesion to be signed as written when a user creates a profile. This research suggests that because the contract must be signed as stated, users may be less likely to read and understand these contracts when knowing they cannot implement changes. Additionally, there are no regulations that protect users against the actions of Facebook and address users’ perceptions to motivate privacy-protective behavior.

In September of 2021, Facebook announced new capabilities for business owners following the danger to its ad targeting by Apple’s IOS 14 privacy initiative. Facebook testified that Apple’s changes would have a negative impact on the small businesses that rely on Facebook ad targeting to find and attain customers. In an interview with The New York Times, Apple’s CEO Tim Cook came back at Facebook’s comment saying, “I think that you can do digital advertising and make money from digital advertising without tracking people when they don’t know they’re being tracked.” However, Apple didn’t budge on any of its IOS privacy updates. Instead, Facebook was forced to implement a new way of connecting businesses to their target audience. 

Among some of the changes, Facebook has made is a way for customers to explore businesses from their News Feed posts. When a user clicks on a topic like beauty, fitness, or home decor, users will be redirected to explore businesses related to their interests. This new system has two main advantages. The first is that users may stumble across a new business that relates to their interests. The second is that this feature allows Facebook to create its data set of users who like certain types of content. Eventually, businesses may be able to pay for higher placement on the News Feed, working a similar system to Google’s search bar advertising. Along with this roll out in 2021 were multiple other new business advertising tools like organic lead generation tools on Instagram, and quote requests via Messenger.

Changes to Facebook’s TOS and DUP are likely to continue. Efforts made by companies like Apple aren’t keeping Facebook from collecting data. The company continues to try and find ways to extract data from users, regardless of the effect on individual users or businesses. Luckily, battles between companies like Facebook and Apple are likely causing users to become more mindful of their personal data and different perspectives between companies bring the conversation of data collection to the consciousness of the public.

Algorithmic Curation of Culture on Netflix

by: Anna Transou

Within the recent decades, humankind has experienced a massive shift from how we expect media content to be presented to us. Alongside the introduction of artificial intelligence and supercomputers, the modern human seems to be quite comfortable with its current relationship with the algorithms that act as our own personal shopping or movie recommendation assistants. But how much do we really know about exactly how we’re being prescribed content, and why haven’t we all taken a step back to even think about the further implications of a computer generated culture such as the one we’re headed to, if not already in now? To answer some of these questions I have homed in on Netflix, the film and television platform with a whopping 213.6 million subscribers, to dive into the implications of humanity complacently entering into an agreement with computerized technologies in the supposed “co-creation” of culture. 

Despite Netflix users interacting with and benefitting from the personalized suggestions that the Netflix Recommendation System (NRS) offers to us, what do we really know about how it works? Regarded as “trade secrets”, the complex algorithms are technically incomprehensible by the average consumer, but we do know some things about how they are designed and intended to function. In a 2014 article containing an interview with Todd Yellin, VP of Netflix product innovation, The Atlantic discovered more about how the media platform has accrued a “stockpile of data about Hollywood entertainment that is absolutely unprecedented.” In a process to reverse-engineer Hollywood, Netflix used large teams of people to deconstruct a myriad of elements within a vast collection of film and television to create microtags which would then help sort content into roughly 76,897 microgenres. Yellin boasted to The Atlantic about the efficacy of the algorithm’s personalization which aims at “putting the right title in front of the right person at the right time,” illustrating the formula’s analogous nature to that of Facebook’s highly customized NewsFeed. In conjunction with the accumulation and implementation of user data in creating personalized suggestions, the creation of NRS involved a process of probing the content individually, inspiring an investigation for the implications for algorithmically curated content. 

The overarching concerns lie in the vexing unknown of what it means to involve computerized algorithms in the creation, definition, and curation of culture, a domain that has historically been reserved for humans. Netflix and many other algorithmically controlled platforms are primarily withholding a majority of the power in the distribution of cultural content, despite what their parent companies advertise to users. In the specific case of Netflix (but also applicable to many other media platforms), “reality gets coded into data for the machines, and then decoded back into descriptions for humans.” Such concerns over who has the power and authority to shape culture and ideas dates back at least to Marx and Engels’ discussion of the ruling class’ control of media and mental production. Whereas the curation of culture was once solely determined by the upper classes of whom held the means to produce media, today we are placing the significant task of cultural prescription into the inanimate hands of artificial intelligence. The construction and suggestion of cultural tastes by algorithms repositions human autonomy as, potentially, a secondary force, in which Netflix users provide the system with ample consumer data to facilitate the operation of recommendations in a one-way distribution of media content. The imbalance of power between human and machine occurs in the restraints placed upon users in that there is no true recourse for the consumer to edit their viewing history, interact socially with others on the platform, as well as the limited binary rating system which merely asks for a user to give content a thumbs up or down. Communication scholar, Fatima Gaw illustrates this dynamic further in pointing to the “restrictions [that] exist in the same paradigm where users are supposedly empowered by free choice and self-determination of their cultural experiences.” After all, Netflix promotes that “our members control what they watch” despite the constraints implemented within the platform to limit user agency in constructing their own individualized taste preferences apart from the algorithm’s prescriptions. The rigidity of the NRS “restrict[s] and often supersede[s] user agency” as it operates neither in an emancipatory or deterministic manner, but instead “inhibits users from deviating from the formula it prescribes.”

With proprietary goals in mind, Netflix’s recommendation system was not built to encourage diversity of media within the platform, instead, the algorithm operates to feed content to users similar to what they have already been favorable towards. The system’s primary functions bring up concerns of the development of cultural echo-chambers, such as has been discovered on social media apps like Facebook and Instagram. Further research must be done in order to discern what effect media echo-chambers will have on the development of culture and society, and how humans can fight back against an algorithmically produced mono-culture of the future that lacks cultural diversity.