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Endorsements via Instagram Influencers

This article from the New York Times, examines the role of Instagram posts as a form of currency for those with an influential social media presence. In essence, companies strike deals with Instagram influencers in which they pay them or give them an in kind contribution, in  exchange for a post on Instagram from that individual.This system of exchange has developed into a “thriving economy” which marketing units have embraced  in an attempt to appeal to younger consumers who are active on social media. These “influencers” are more important than celebrities for many companies, because social media traffic is so heavy and has such a large influence.

We can analyze this relationship in terms of link analysis using hubs and authorities. The Instagram pots act as hubs, and by promoting a certain product, link to the authorities selling or promoting them. Additionally, we can explore these posts as “endorsements”. We know that a page is important if it is cited by other pages, and this is often the dominant mode of endorsement. This trend on social media in the form of Hashtags (#). If a hashtag or product is used by an influencer in an Instagram post, it could cause increased traffic to that hashtag or product, therefore expanding the endorsements. By having influencers “repeatedly pass endorsements across their out-going links” the weight of the endorsement gets stronger. Nodes which are viewed as important get to make stronger endorsements. In this sense, the influencers are the nodes and the weight of their message is incredibly influential- hence the emphasis placed on Instagram endorsements by the marketing strategies of companies.

 

 

 

Page Rank in Our Everyday Lives

One of the most widely used sites in our everyday lives, Google.com, uses PageRank when it comes to their search engine optimization (SEO). SEO is defined as a methodology of strategies, techniques and tactics used to increase the amount of visitors to a website by obtaining a high-ranking placement in the search results page of a search engine, which includes the popular search engines: Google, Bing, Yahoo and other search engines.

Contrary to popular belief, PageRank is alive and well and will always be used as one of the most critical factors attributing to a domain’s ability to rank.

In order to clarify, PageRank is an algorithm used by Google Search to rank websites in their search engine results.  PageRank is a way of measuring the importance of website pages. According to Google: PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is.

I mention this to come to a very important decision in businesses. How important or valuable is your PageRank score on different search engines?

If Competitor A and Competitor B both have businesses in which they market apples. Then if you search the word “apple” in Google (or any search engine) and Competitor A’s link comes up before Competitor B’s link, then A has a higher PageRank score relative to B’s.

How refining advertising in Europe is sparking controversy

Link:https://themediaonline.co.za/2018/10/digital-advertising-rise-of-the-algorithm-raises-ethical-and-legal-issues/

The article focuses on the digital advertising market in Europe, and more specifically, France. It is reported that the digital advertising market in France is now at 3.5 billion euros. Perfect matching for ads has always been an issue, since the main types of ads are either impressions or clicks, and while impressions give more ad exposure, it might not get people interested enough, and click ads lose their value because they only get exposure if people decide to click on them. Currently, though, the web and improvements in technology allow “the profiling of internet users” which “is carried out using traces of their web activity, which makes it possible to predict their interest in an ad at any given time”. This allows websites to not only display impression ads, but display impression ads that more accurately reflect people’s interests, which in turn causes whoever sold the ad to get almost guaranteed interest and profit from web users. The two major factors that cause controversy, though, are the extent that sites “mine date” from their users, and the “discriminatory” ads that websites display since “the algorithms sometimes benefit from exaggerated confidence”.

In my opinion, algorithms like the one’s used in Europe are necessary in our day and age, where websites and advertisers really have no choice but to compete for more exposure and money. One of the biggest hurdles in advertising is matching a perfect market matching for websites and ads based off the demographics of the website’s users. The values for selling ads really depends on the demographic of the websites that the ads are displayed on, and although the demographic for website users usually follows a structure, that doesn’t take away the fact that the demographic of any website is dynamic to an extent; there is really no perfect matching for the ad market unless constant information about who uses the site is known. This is where the need for quick algorithms like the European ad algorithms come in; by constantly collecting only necessary user data in real time, algorithms can update their ads on a website just as constantly as how much the demographic changes on the websites. On top of this, it encourages more truthful bidding for advertisers about their values due to the fact that since ad preferences are constantly changing on each website, just because one advertiser has the competitive edge over other advertisers at one time by broadcasting an untrue value doesn’t mean they’ll continue to have the competitive edge as time goes on. So algorithms that can update and perfectly match markets in real time not only increase ad revenue for websites and gain more exposure for advertisers which allows for more efficient advertising, but truly cement truthful bidding as (almost) always the dominant strategy.

Although new advances in ad algorithms are certainly beneficial for websites and advertisers, I still believe that whoever makes these algorithms have to make these more transparent and show the extent of data they are collecting in order to perfectly match the market. Even though on paper these algorithms definitely help both websites and advertisers, the moral and ethical consequences of data collection could deter users from going to sites that they know use such methods to maximize revenue, which could in turn lower the payoff for every website, since the total amount of website users would decrease and perfect market matching would only help to an extent.

Switch to first-price auctions causing buyers to pay more, call to replace with redesigned second-price auction model

In this article, the author discusses that a recent switch from second-price auctions to first-price auctions that has recently been implemented by a lot of different groups is causing buyers to pay more for advertisements than they previously were significantly. This not a small increase in price either, but rather it has found that this can drive up the prices of CPM ( cost per mille,  a marketing term used to denote the price of 1,000 advertisement impressions on one webpage) by 50%. 

Based on what we learned in class regarding first and second price auctions, this makes sense.  If there is a switch from second to first price auctions, the price of the highest bid will be paid for the ad as opposed to the second highest bid. Although everyone will still bid their true value, the case in which one bids their true value and then ends up paying less than what they bid does not happen anymore, so then in theory the price at which buyers pay will increase.

In response to this, the Omnicom agency, a marketing and communications group, wants to create a new model of second-price auctions different from the old second-price model, which had issues regarding transparency.The goal of this agency is to create a clean and authentic second-price auction system in order to remove fraud and decrease the premiums that marketers pay. This system of second price auctions would has to meet the condition that the people running the auctions are not artificially boosting the price.

This is very relevant to our coursework as it discusses the advantages and disadvantages of a second-price vs a first-price auction. This article exemplifies that although second-price auctions are often better for the buyer, they come with issues such as artificial price inflation and less transparency and need to be re designed in order to be as efficient and trustworthy methods for ad purchasing as possible

More information can be found here:

Link:https://adexchanger.com/online-advertising/hearts-science-calls-to-replace-first-price-auction-with-reformed-second-price-model/

How Instagram is Changing Travel Advertising by Utilizing the Authority-Giving Abilities of Hubs

Link: https://www.nytimes.com/2018/10/19/travel/instagram-hotels-parks.html

This article explores how Instagram is changing the way that people pick and plan vacations and how the travel industry is adapting to this change.  Nowadays, many travelers design their travels around Instagram destinations or start with Instagram when looking for travel inspiration.  The curated visual nature of the content makes Instagram more like a “modern magazine” than the larger Facebook, ephemeral Snapchat, or text-based Twitter.  Hotels, resorts, and tourism bureaus find that “Instagram bait” like murals, skylines, and absurd foods serve as particularly strong draws and have been forced to adjust their strategies to provide these types of appealing images.

These relationships are examples of hubs and authorities, which we discussed in class.  Influencers act as hubs, linking to a specific hotel though a location tag or @ mention.  A super-popular influencer posting with a high hub score will confer a lot of authority to a destination.  But Instagram’s aggregation methods themselves act as hubs.  People might explore the hashtag #travel or #Australia when looking to vacation.  Looking at the page of a location (the collection of public photos tagged at a specific location), or the hashtag of a hotel such as #HotelFigueroa that has more posts will give planners more confidence in that destination.  Even if each individual post comes from less famous accounts or just regular people, a lot of hubs with lower hub scores linking to a node will confer authority.  Different places take advantage of different strategies: some offer free trips to popular accounts in exchange for favorable posts, while others try to feature insta-worthy views and activities that many people are more likely to post about.

Sometimes, the hub can give too much authority to a node.  The article mentions an ultra-popular hashtag #thatwanakatree celebrating a unique tree in New Zealand.  This has created such a large draw that the health of the tree is now in danger.  Too much traffic can be undesirable, especially when internet posts translate to real-world foot traffic.

Hubs and Authorities Relating Universities and Prestigious Alumni

Hubs and Authorities are present all throughout the internet.  The valuation for each is extremely important in determining the value of the content/links within.  Universities and their sites follow the same rules and valuation effects.  Prestigious schools have extremely high valuations because of the also prestigious links it holds.  For example, highly esteemed alumnus present considerable reputation boosts to the schools.  Following this, as the schools gain prestige from their alumni, more promising potential students will enroll, seeing the valuation in the university over the competitors.

 

This trend continues and is repetitive to the point that the separation of value and prestige is so massive that students from all over the world compete viciously to get into the top valued schools (Ivy League), while others are forced to accept almost any that apply.  It should also be noted that the admission slots for each university do not regularly expand, so higher valued colleges supply increased competition for dwindling spots, while less desirable places struggle to fill the regulated admission spots with students of high academic performance.  The students act as Hubs that connect (through enrollment/graduation) to each university (Authority).  The achievements the alumni incur over their lifetime acts to increase the value of the universities.

 

The article discusses the issue of increasing desire to be enrolled correlated to the stagnant number of admission spots.  More importantly, the applications for enrollment tend to bunch at the top of the university hierarchy.  This is a result of the potential students seeing the valuations of the college and choosing the higher value over the lesser (one’s with less reputable alumni connecting).

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3046867/

Amazon.com recommendations and Hub-Authority Networks

The article linked below titled “Amazon’s Recommendation Secret” claims that Amazon.com’s (Amazon) increase in growth is due to its successful integration of recommendations on its website. To summarize their recommendation method when looking at their site: a typical user could search for a certain keyword and then a list of items will show up. In addition, when an item is clicked, on that site there will be another list of items at the bottom of page under “frequently bought together” or “sponsored products related to this item”. The article lists that Amazon tracks metrics such as “open rate” and “click rate”. In addition, their hidden secret is that they also try to provide recommendations that would encourage the highest revenue.

For a more thorough explanation, the document titled “Amazon.com Recommendations” (http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf) actually delves extensively into the specific recommendation algorithm Amazon uses. It is called “Item-to-Item Collaborative Filtering”. It highlights that Amazon doesn’t use the typical method that groups customers by their purchase and click patterns and then subsequently predicting what the customer might want to purchase. Instead, they focus on the similarity of items being purchased and then create a matrix of data to find other items that are similar based off of past purchases. That matrix includes different vectors of information between items, including the item’s rating, price, and popularity.

This process can be simplified to represent hub-authority network (obviously it is much more complex than this). You can think of a click or purchase as an edge pointing from an item to another item. And like the document argues, the algorithm focuses more on item similarity rather than user action similarity, so the nodes would be items to purchase rather than customers. So, with this network, the higher the clicks to an item, the higher the normalized authority score. And then with these scores, Amazon uses other computation methods to provide influence over ratings and potential profit in the final recommendation list. They include methods such as calculating the angles between edges to judge similarity between items. After all this computation, then Amazon has a recommendation list that is similar to items you purchased, thus leading to a higher potential profit.

http://fortune.com/2012/07/30/amazons-recommendation-secret/

Using Strongly connected components in social media

https://pdfs.semanticscholar.org/fd6e/5b06b689cc331974f92fe03bfeb47af53053.pdf

This articles defines different procedures on locating a strongly connected component in a social media network. While we moved to web pages to discuss something different from social networks, in social media the same linking mechanics can happen based on users following each other, which is a one way link that can be reciprocated to form a two way link. The question then comes up is how one could detect strongly connected components and what that could be useful for. The article itself details many algorithms on how to detect the components and then implements it on a clone of the social media site, Twitter. It discussed how the isolation of a strongly connected component can be used for a company to target an audience that already follows its page. It can target these uses and other users in the SCC (Strongly Connected Component) with community messages or some sort of advertising.

This is useful because if a users is in the SCC they may not necessarily follow the companies community page there is a path from them to the page so those users may be more likely to engage with the company over other users not in the SCC. This is the key potential in detecting SCCs: being able to identify a community in a social network and target it in order to increase engagement and the value of targeted engagement. Further discussed in the article above this allows for the cost of advertisement to lower by up to a factor of 10. This is extremely significant and can either drive lower costs in advertisements for companies or increase the profit margin for advertisement providers.

Wisdom of Crowds Effect

Source: https://phys.org/news/2010-09-scientist-braess-paradox-high-traffic.html

Purported to save travel times, the addition of a new road to an urban area would surprisingly create longer travel times. Braess’s Paradox states that when a new route to a transportation network is added, it would increase travel times. However, within the past few years, scientists have been able to provide evidence that the paradox does not hold true to all cases. Professed by the article, Anne Nagurney has proven that Braess’s Paradox does not occur when the demand for travel increases. Within her derived formula, the paradox holds true only to a specific range of demand. Though it may seem counterintuitive, the abandonment of the paradox suggests that the network would become more crowded. As a result, this would seemingly incur an increase in travel times. However, Nagurney asserts that the “Wisdom of Crowds” effect can explain the results. There are two types of travel behavior: user-optimizing behavior and system-optimizing behavior. User-optimizing behavior is concerned with making choices that benefit the individual, whilst system-optimizing behavior is concerned with making choices that benefit the group. When drivers make the choice of driving down a new promising road to save time, it would work at first. However, as more drivers choose to drive down the same road, Braess’s paradox manifests. Over time, as drivers realize that they are incurring a greater travel time, they choose to drive down the old road. By committing to choices that saves everyone more time, drivers are becoming more system-optimizing.

The article presents a view of Braess’s Paradox that has not been covered in class. Though we have performed calculations of choices drivers make under the paradox, we haven’t been able to discuss if it would had been more optimal for the drivers to drive down the old road. If we had considered a case in which there are considerably more drivers, we would have realized that Braess’s paradox would not have manifested. Since driving down the new road would incur a greater travel time, overtime, the drivers would choose to drive down the old road to collectively save more time. Instead of pursuing their self-interests, they become wise and choose to cooperate in driving down the old road.

Official US Government Sites Ranked Lower than Imitator Companies by Google Search

https://www.bbc.com/news/technology-45913581

Recently, Google’s search rankings became the subject of a BBC article, specifically regarding misleading results for travel visas to the US.

Some context:

British travelers to the US must apply for authorization through the US Dept. of Homeland Security’s ESTA  (Electronic System for Travel Authorization). The US Government has an official website for this process, and charges $14.

However, many companies have popped up that charge from $80 to $100 for the same application, which they simply forward to the US Government and make the difference in profit. In addition, they are intentionally misleading consumers by having official-sounding names and logos that imitate the official logos.

The Story:

The recent controversy arose when the BBC discovered that those imitator companies were ranked higher than the official government website, leading to a lot of travelers being misled into paying higher prices. When pressed by BBC, Google admitted that the search results were determined by their algorithm, and they did not take special effort to pin official/government sites to the top of search results. The BBC reports that some of the ads have since been removed, but some remain.

This is related to the class material because the misleading search results rankings are due to various SEO techniques to increase the PageRank, as well as these companies spending a lot of money to purchase the ad slots that come right before actual search results.

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