Multimodal Analysis 1

Throughout the years, technology has become increasingly advanced. There are so many things one can do on the internet nowadays: make purchases, view content, search the web, etc. However, as technology and the internet have evolved, there is undoubtedly an increase in surveillance and data collection. 

When we go on the internet, everything we do produces data, which has the possibility of being tracked and collected. For example, even a “simple web search from even the most unsophisticated of smart phones generates a lengthy record of new data” (Cheney-Lippold, 2019). Those who can benefit most from this data are the large, well known companies as well as the government, who can use it to do actions such as targeted marketing towards certain groups or, in the case of the government, to stop potential crimes. 

In fact, many big companies, such as Google, Microsoft, and Yahoo, went on to buy digital marketing companies. By doing this, it allowed them to collect even more data and information on their users. 

But what exactly happens with this data that is collected? Using algorithms, companies can observe, analyze, and process the data to profile us and assign us an identity. Algorithms are commonly used by these companies to figure out what people are like based on their data and behaviors online. By finding out more about their users, they can personalize their users’ experiences on their platforms. 

These companies are not really trying to figure out personal information or demographics - that does not matter to them as much. Instead, they want to figure out who people are based on their searches, their online purchases, the websites they visit, and more. 

In We Are Data, Cheney-Lippold explains that to these algorithms, a “user is a ‘man’ according to how closely ‘his’ data stacks up to preexisting models of ‘man’. These models are what [he] call[s] measurable types, or interpretations of data that stand in as digital containers of categorical meaning” (Cheney-Lippold, 2019). To elaborate, a user’s online behavior - including things like searches, visited websites, and everything else they might do on the internet - would be collected and processed. How much this data measures up to a measurable type would determine who that user is seen as on the internet.  

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However, an individual’s measurable type might not actually represent who they are in real life. For instance, in We Are Data, Cheney-Lippold describes how Google assigned his female scientist friend “a ‘gender’ by treating her web-surfing data as inputs. Then, Google subsequently spoke for her data according to how well it statistically stacked up to its quantitative models” (Cheney-Lippold, 2019). This female’s searches and behavior online had been read as masculine, and is profiled as such by Google’s algorithms. Her future online experiences, such as getting advertisements or video recommendations, would be tailored towards this online identity that Google has applied to her. 

Algorithms certainly affect how people view the internet. What one person might see online is completely different from what another might see. There are certainly good parts as well as conveniences when it comes to algorithms and companies collecting data. Users can discover more content that is relevant to them, and their whole online experience is tailored towards what they do online. 

Although the algorithms that companies use do show us offerings that they think we will like and want to see, it can be worrisome to think about the ways they can use this influence over us. Their “economic, political, and cultural agendas behind their suggestions are hard to unravel. As middlemen, they specialize in shifting alliances, sometimes advancing the interests of customers, sometimes suppliers: all to orchestrate an online world that maximizes their own  profits” (Pasquale, 2016). 

One example of this is “Facebook's news algorithm, which enhances the so-called "filter bubble" of information. The social media giant's news algorithm tailors updates and content to each user, which could keep users from receiving information or news from sources that challenge their worldview” (Johnson, 2017). The content and news that people take in on Facebook, as well as other social media platforms, is so personalized to their own opinions and lives that it would be very unlikely for them to see opposing views. This algorithm would be especially controversial when it comes to political or social issues, as it makes the social media platform look very biased. In addition, this could definitely come close to getting into the censorship territory. For many, it seems like “Facebook is becoming an echo chamber that prevents us from being confronted with opinions we don't agree with” (Nejrotti, 2016). 

Here is a video, featuring Eli Pariser, that gives a more in depth view of news algorithms.