Question To An Expert: Is It True That Social Networks Are Following Us

A life 2023

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Question To An Expert: Is It True That Social Networks Are Following Us
Question To An Expert: Is It True That Social Networks Are Following Us
Video: Question To An Expert: Is It True That Social Networks Are Following Us
Video: You Will Wish You Watched This Before You Started Using Social Media | The Twisted Truth 2023, February
Anonim
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Dmitry Kurkin

ANSWERS TO MOST OF THE QUESTIONS ARE EXCITING TO US we used to search online. In the new series of materials, we ask just such questions: burning, unexpected or common - to professionals in various fields.

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The flash mob 10 Year Challenge, launched on social networks at the beginning of the year, not only gave rise to conspiracy theories, according to which the purpose of the action is to collect photos of users and use them to train the face recognition system, but also once again made you think about how much they know about us social networks and third parties working with them (from commercial companies to government agencies).

It's no secret that tech giants are collecting and analyzing the so-called digital footprints left by billions of users every day. And this realization creates a new kind of fear of "big brother": social networks know a lot about us, but what if they know too much about us? Can big data be used to find out all the connections, tastes, habits of a person, his past and present? And if so, what harm can our desire to socialize online, in the name of which we voluntarily share information about ourselves, do us?

We asked experts about how user data is processed by large companies and how great the danger is to be inherited on social media.

Lilia Zemnukhova

Research Fellow, Center for Science and Technology Research, European University at St. Petersburg

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The digital footprint contains all possible types of data - these are texts, pictures, audio and video recordings, geolocation, as well as a huge amount of metadata (for example, gadget model, mobile operator, operating system, dynamics and duration of visits, etc.). And it's not just us who add to our digital footprint. Social media shape us as users using three sources of data: what we ourselves report about ourselves; what others say about us; and what is collected most often without our knowledge. Especially the opaque last one. We generally do not read user agreements and personal data collection and use policies. We only notice that this "black box" somehow affects our user experience: targeted advertising, friend suggestions, music recommendations, news order … We construct a small part of this experience ourselves, when we manually build a news feed, but mostly algorithms perform functions built into the default profiles. That is why we will never get rid of PPC advertising or intrusive offers from groups or (un) acquaintances. Social networks as corporations use data about their users for commercial purposes, offering their platform for selling targeted content. And along the way, they continue to collect data about us: for example, if you paid for advertising at least once, then the bank card and transaction data also remain with the company. Data can also be provided to government agencies when necessary: ​​for example, Facebook regularly collaborates with US government agencies, in accordance with its transparency policy.

In addition to internal social media policies, there is another important detail: accounts can be linked to hundreds of thousands of other applications and functions. This, for example, sparked a lot of discussion last year about third-party access to user data. An important attempt to regulate developer freedom has been made in the European Union - last year the General Data Protection Regulation (GDPR) came into force. He did not solve the problem of data transmission, but drew the attention of users to this issue. This does not oblige us to read all user agreements, but it makes us think and at least be more responsible for our digital footprints and observe basic rules of digital hygiene.

Valeria Karavaeva

data scientist at Spiking

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Sometimes we don’t think about how many traces we leave on the Web and how much it later helps companies, not just social networks - although social networks too. Social networks collect data not only for themselves, they can sell it - I know about this because I worked in an advertising agency, and we bought data from Facebook. And more often than not, we, users, give consent to this, without noticing it ourselves. People spend half their lives on social networks and give a lot of information about themselves.

But data could have been collected before - so why has big data been talked about only recently? First of all, because computing power is growing and, accordingly, becoming cheaper. The main question of big data is not how to collect data - in principle, each of us today can collect and store terabytes of information - but how to work with it. Most of the data received from social networks (text, voice, images, videos) is not structured in any way, so without machine learning, big data is useless. Now, due to the fact that the power and memory have become cheaper, the demand for neural networks and deep learning has grown - we have finally learned how to process large amounts of data.

Take pictures, for example - and these are really big data, they can give a lot of information. There are millions of pictures, but what to do with them? How can you benefit from them? What patterns do they reveal? Machine learning hasn't really gotten that far. This is not as easy a process as it seems: there is no such thing that you press a button and get full calculations in a week.

Machine learning is preceded by more complex tasks. The same pictures first need to be properly processed (for example, crop, center photos; this is important for training) - this is the first stage, which usually takes a lot of time. The second step is to choose a network architecture suitable for solving the problem. Roughly speaking, you build ten different neural networks and they produce ten different results. Then the results obtained need to be evaluated somehow. And after that, you, most likely, return to the first stage. It is unrealistic to build one universal network for any task: you either build it from scratch or modify the existing one. Facial recognition is one task, cat recognition is another.

We also participate in the process of machine learning without knowing it. For example, by entering captcha on sites: using captcha, Google trained neural networks to digitize books.

It should be understood that companies collecting big data are not interested in our personal profiles. They want data on a lot of different people who are interested in something in particular. As for the intelligence services, I believe they can collect data without resorting to social networks. I think our fears about being followed will soon pass. This is the new world: you can not inherit on the Web, but it is difficult. It's easier not to appear on the web at all.

PHOTOS: antonsov85 - stock.adobe.com

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