Changes In Information Consumption
This Digital Era is characterised by changing technology and the widespread impacts that this has on altering our lives. The objective of this article is to highlight one particular ramification of these changing technology trends — change in the information consumption patterns of a typical user. The information consumption patterns that are being discussed here is the manner in which this ever-growing pile of data is presented to us. In recent times, this has been possible particularly due to the recent advances in some specific domains such as — algorithm design, machine learning, computer networks, communication technologies and storage technologies. A detail that may not seem very important, but cannot be ignored, is that it is not the advancement of a singular domain, that has led to the development of these modified information consumption patterns, but the unified effect due to the rapid progress seen in all of them simultaneously. Since all of these technologies need to work in tandem to perform the required task with high efficiency and at lightning fast-speeds; In such a situation, any component of the system being extremely inefficient (or slow) causes it to become the limiting factor of the system causing a steep overall drop in efficiency and speed. To ensure that such instances are avoided, the simultaneous progress of these domains is of utmost importance.
Before any further arguments or points are presented, there is a need to highlight the importance of these information consumption patterns. The earlier explanation that described them as the ‘manner in which the ever-growing pile of data is presented to us’. Expanding on this slightly abstract explanation — the manner that is being talked about here is essentially the various mediums through which we consume information, whether it be on our mobiles, tablets, PCs, etc. The data here talks about not just data that is needed for official purposes or academic data, but also concerns news that we consume everyday, the data we consume in the form of our friends’ posts on various social media sites (such as Facebook, Twitter, etc.). Data is now not limited to text, it also encompasses video data that we consume in the form of the series that we love to binge on, the songs that we keep playing on loop, the forums that we visit just to look at memes or just mundane cute cat videos. In a nutshell, these consumption patterns are a signal of how our generation spends most of our time on the internet. In particular, information consumption involves an analysis of our browsing habits, general likes/dislikes and preferences with respect to consumption of any data. This makes them important, because all the sites that serve content to you in any format, study these patterns and morph their policies and algorithms with an aim to manipulate the particular user to perform certain activities that are aligned to specific commercially or politically motivated interests. This makes it important for us as the user to know about how these information consumption patterns have changed and perhaps adapt our own habits and practises to ensure a good, personal internet experience.
The basic flow of the article revolves around the two main elements that have contributed vastly to the change in the information consumption patterns that concern us. These elements are the Absolute Quantity Of Information that exists and the Ease Of Access To Information that the typical user has. Furthermore, an attempt has been made to highlight the trends within each of these elements while trying to analyse how they have changed over the past few decades and the impacts of the same.
Talking about the former among those two, it is not tough to note, how we have (in general) transitioned from an environment which was characterised by a lack of adequate information, to one which is characterised by a great abundance of the same. Think about it, gone are the times when someone who needed to find details on a certain topic would have to spend hours in the halls of huge libraries going through limited books and references that are constrained by budget woes of that particular library/institution. In today’s times, the more one wants to know about a certain topic, the more likely it is that he/she will find a better source via a Google Search. A vivid change can be seen by how Google is our first approach to every question, before asking our peers and definitely before visiting the library. Furthermore, the net also promotes sources that are not worthy of being published to share their views and content in the public domain which often provides us with new perspective that is not mainstream and in-demand and hence is not feasible to be published on a larger scale in general.Besides this, the sudden increase in the Absolute Quantity Of Information can mainly be attributed to a technological factor — the development of increasingly efficient storage techniques. Digital Memory is becoming cheaper and this promotes two malpractices that have mainly caused this upsurge in quantity of information — Storage Of Redundant Information and Storage Of Sub-Quality Information.
Firstly, let us address the storage of redundant information. We live in a time where the ‘freedom of expression’ is a fundamental right in most countries (or is it really?). This leads to an often unchecked effect which can be simply put in this manner — If Everyone Is Allowed To Have An Opinion, More Often Than Not Everybody Wants To Have One. This tendency to have a vocal opinion is then combined with the improved mechanisms that allow us to disseminate and consume information. This is where the role of virtually infinite storage capacities and increasing avenues of online publishing platforms come into place. Now such avenues (say Wordpress) are considered as the de facto tool to exercise one’s fundamental right to freedom of expression, but on a slight change in perspective (say the collection of Facebook statuses) it is not tough to notice that this exercise of our fundamental right leads to a large database of information that is essentially irrelevant to everybody other than a small group of people after some time. Moreover, the increasing trends of making an online post for every event that happens around a person leads to most of the information being made public, either repetitive (think of posts condemning a recent law) or too personal (people who like to tell everybody about their daily food intake) to affect a large number of us. As an end-result, it is obvious to notice how this large pile of information leads to generation of a lot of noise which hides the important data and information under a inundation of redundant and utterly comparatively useless data.
This being said, there is a need to clarify that — By no means does any of this mean to imply that the right to freedom expression needs to be curbed; the ill-effect that is being highlighted here rather tries to target those who fail to see a line between sharing their views, opinion and some information online and confuse it with adding virtual waste to the internet. In this manner, everyday there are copious amounts of Redundant Information added to the Internet, which accumulates to a whole lot of data that the world can very well do without.
While the presence of Redundant Data is a big concern. There is another related issue that requires equal attention. One that concerns the existence of Sub-Quality Information. While everybody tries to judge the quality of information using different methods, most of the criteria revolve around the relevance and verity of the data in question. In other terms, no one wants to consume information that fails to address the topic in question. Moreover, we tend to dislike information and data that provides us with unverified or unsupported opinions and statistics. The biggest sources of Sub-Quality Information are seen in the form of internally biased blogs, partial news-sources or poorly moderated forum threads. This information can be considered worse when compared to redundant information as mentioned above. This is because, for a casual reader, redundant information can serve as a waste of time as it does not give him anything new any only reiterates something that is already known to him. On the other hand, such Sub-Quality sources of information that are referred here, give the reader only half-knowledge, or even wrong-knowledge about the subject in question, through the presentation of biased views and twisted statistics. This can be very well seen on the most famous forums in today’s times, where the role of moderators has been reduced from being a device of quality control, to someone that merely checks the use of indecent language and ensures that nobody’s sentiments are hurt, even if it is (and it usually is) at the cost of the quality of the discussion or the reputation of the forum itself. This kind of moderation really ignores the real aim of the presence of active moderators in a discussion and such threads turn into a huge source of Sub-Quality Information instead of acting as mediums of discourse.
Concluding, The idea of this increase in the Absolute Amount Of Information can be highlighted using a very straightforward idea, which is the loss of the need for ‘deleting information’. A lot of us have not been a part of those times where prudent use of storage was a need and not an option. Nonetheless, it is certain that we have all seen one change which should highlight the same sentiment — the trivialisation of a terabyte. A decade back, possessing memory in the units of terabytes was not just rare, but almost unheard of. Forward to today where a terabyte of memory is affordable for every typical computer user, that is if his system doesn’t already have in-built memory of 1 TB in the first place.
Now that we have seen how there is an increase in the Absolute Amount Of Information due to the increase in the amount of Redundant and Sub-Quality information supported by technological factors like the development of efficient storage techniques, reduction of cost of per unit memory and so on. Now, there is a need to emphasise the latter of the two points which are main elements that lead to the change in information consumption patterns. This point concerns the Ease Of Access To Information by a typical computer user in today’s world. Consider this — Not long ago, it was not uncommon to ask a bystander the way to a famous landmark; In contrast, in recent times when there is easy and free access to detailed and regularly updated maps right in your hand, the first thought comes to mind when you lose your way around town is to use the nifty map application in your smartphone and avoiding interaction with any stranger. This change is perhaps the most notable feature for every frequent user of the internet and especially those users that have been using it for quite sometime. While the previous points, were mainly an outcome of the increase in efficient storage techniques, the Ease Of Access that is being talked about here is essentially an end-effect due to the rapid advances in the field of computer networks and communication technologies. The current state of technology puts a virtually infinite amount of information right in our hands (or most of the time, in our pockets). This kind of technology has made it extremely simple to access information about any subject in seconds. More so, increasing connectivity makes it easy to access information and data from virtually any corner of the world. Technologies that involve things being done on the Cloud are playing a vital role in this domain and increasingly making it easier and easier to perform complex operations without access to a powerful computer and thus further increasing this ease that is being talked about here, helping the end user immensely.
Not So Bad, Eh? Agreed, the two aforementioned points are not that bad when considered to be separate effects. But if you combine the two of them, and add to that the ill-effects caused due to improved algorithm design techniques coupled with the widespread use of machine learning paradigms which enable the controllers of such technologies (usually MNCs) to process larger amounts of data in faster and more detailed ways, which further help generate more data that is used again used to profile and manipulate our online (and offline) experience of computer interactions, mostly to support commercial or political propaganda. Then, it is not very tough to note the way things have changed. Consider the points that have been talked about above; If the essence of all of them were to combined, it could be asserted that we have transitioned from an environment which was characterised by a small amount of information that was not accessible to everyone conveniently, to one which is a polar opposite, characterised by the existence of a cosmic amount of data that can be accessed with great ease from anywhere. Extending upon the combined effect of all of these advances, let us add to the list above the effects of the advances in algorithm design and machine learning. Most of these advances have affected two fields — Predictive Analytics and Searching Algorithms.
One of the most widely used paradigms in current times is that of predictive analytics. Just as the name suggests, predictive analytics involve the use of various modern techniques (like machine learning, data mining and computational modelling) with an aim to predict events that are most likely to take place in the near future. Predictive Algorithms are very widely used due to their applications in two very fundamental tasks — Recommendation Algorithms and Rating Algorithms. Every site that provides information is expected to perform these tasks, in the form of recommendation of new content, rating of comments, informing the user about related links that he might be interested in and also to highlight the content that is ‘trending’. For example YouTube’s recommendation for related videos and ratings of content, or Amazon’s recommendation for things that buyers might be interested and rating of top commodities in each category. This leads to predictive algorithms being a widely used phenomenon in all the current online sites and platforms such functions are not only a fancy feature, but a core part of their functionality.
Searching Algorithms are equally important (if not more) when compared to algorithms that involve predictive analytics. This reason is intuitive, because how else is it possible to find the desired data in all the mountains of information out there in the data centres behind the internet? When such an increase in information is noted, the search algorithms are tasked with a more daunting task of not only wading through a lot ‘virtual waste’ which consists of redundant or irrelevant information. So, not only do the algorithms need to wade through a bulk of irrelevant and sub-par results but also need to find the perfect search result for the user’s query. Due to these reasons, there are a lot of very sophisticated search algorithms that perform this task millions of times a day, across the world wide web; the most notable of these algorithms being PageRank and Hummingbird, the most famous Google Search Algorithms.
Now that some light has been shed on the real state of technological affairs in current times, it is not very tedious to show the ill-effects it has caused. The combined effects of all of these factors has led to the creation of a larger problem that trumps(!) any other individual side-effect that these factors might cause separately; To describe that simply — ‘there is just too much information, a lot of it is not of good quality and the technology that we use provides us with all such information instantly because the algorithms and techniques that are used are faulty too which leads to most of our views and opinions being uninformed, biased or misunderstood.’
To extend this idea, the problem that must be recognised is that these seemingly harmless elements when come into effect together cause the change in the way we consume information. A lot of these problems start with the functioning of the searching algorithms. Searching algorithms (just like any other algorithm) are prone to a huge number of biases, human errors, and features like commercial and political agendas that fail to ensure its objectivity. There are various examples that highlight the difference between the way computers and humans interpret queries and data; A common one being the Paris Hilton dilemma which involves the computer being forced to decide that when a query includes the keywords ‘Paris Hilton’, is it supposed to be a query about the the hotel in France or the socialite in the US. Combine this with the difficulty of the task that it is put to — to wade through cosmic amounts of redundant and sub-quality results and find the most appropriate result to the search query and what you end up with is a search result that is just one of many alternate results for the same query, presented in a certain way, written from a certain perspective by a particular author, propagating certain views etc. Similar biases are known to exist in most recommendation/rating software that promote higher visibility of certain kinds of posts when compared to other based on questionable criteria and logic. This is the first stage where a user interacts with the technology and that, in itself is flawed. This can be understood better by focusing on how most things only have data regarding a certain demographic, especially technologies like image recognition. If most of the data that is fed is relevant only to the western demographic, then the technology essentially does not cater to anyone who is not a part of that demographic since it is not optimised to work with such users in the first place. This is seen a lot when programs misbehave if not given a diverse set of training data which leads to them not being able to understand the needs of a user who gives it inputs absent from the program’s narrow training data set.
After this comes the idea of how the end-user seeks information quickly and conveniently. This thought, when in tandem with the above problem often leads to a more subtle issue — we complacently accept the information that we obtain to be the best available option, in each and every case. This need not be the case, as stated earlier, when there is such a large amount of information to go through, it is not tough to see how the time and convenience constraint, may lead us to use the first result or link that we get. Take a moment to think, when was the last time you visited the second page of a Google Search Result? How many people have actually ever gone to the second page? This is where the consumption patterns have changed; Where previously a person would go to the library, ask his/her friends, refer to multiple sources with the aim to seek the perfect solution, when now a Wikipedia lookup seems to be the ultimate answer.
As an auxiliary point, when a typical user wishes to search for or consume any kind of data available on the internet, one thing that is even more worse than getting an inaccurate answer is knowing that he/she is not able to get the most appropriate answer possible. Unfortunately, this is often the case in promoted trends and suggestions that aim to create buzz around a certain topic or media through forceful recommendation of the same in the case of disconnected or remotely connected topics which leads to the promoted content to have higher chances of becoming a ‘trending’ topic.
On a concluding note, the purpose of highlighting all these questionable elements and problems is very straightforward; We need to understand the effect of rapidly growing technologies have had on the internet, especially the way we consume information. On understanding these effects properly, we must must rethink and reorient our browsing, searching and viewing habits to ensure that you are not affected by it. More so, in these times where data and information tries to distract and influence every user of the internet, we must perpetually aim to have a personal opinion that is unbiased and logical and most importantly make sure that those thoughts are our own.