We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Online publishing, social networks, and web search have dramatically lowered the costs of producing, distributing, and discovering news articles. Some scholars argue that such technological changes increase exposure to diverse perspectives, while others worry that they increase ideological segregation. We address the issue by examining web-browsing histories for 50,000 US-located users who regularly read online news. We find that social networks and search engines are associated with an increase in the mean ideological distance between individuals. However, somewhat counterintuitively, these same channels also are associated with an increase in an individual’s exposure to material from his or her less preferred side of the political spectrum. Finally, the vast majority of online news consumption is accounted for by individuals simply visiting the home pages of their favorite, typically mainstream, news outlets, tempering the consequences—both positive and negative—of recent technological changes. We thus uncover evidence for both sides of the debate, while also finding that the magnitude of the effects is relatively modest.

Filter Bubbles, Echo Chambers, and Online News Consumption

The Long Tail: How Technology is turning mass markets into millions of niches. (p. 15). This passage from The Long Tail, pretty much sums it all up. The Long Tail by Chris Anderson is a good and worthwhile read for information scientists, computer scientists, ecommerce researchers, and others interested in all areas of Web research. The central premise of the book is that the combination of (1) the Pareto or Zipf distribution (i.e., power law probability distribution) that is characteristic of Web traffic and (2) the direct access to consumers via Web technology has opened up new business opportunities in the ''long tail''. Producers and advertisers no longer have to target ''the big hits'' at the head of the distribution. Instead, they can target the small, niche communities or even individuals in the tail of the distribution. The long tail is has been studied by Web researchers and has been noted in term usage on search engines, access times to servers, and popularity of Web sites. Andersen points out that the long tail also applies to products sold on the Web. He recounts that a sizeable percentage of Amazon sales come from books that only sell a few copies, a large number of songs from Rhapsody get downloaded only once in a month, and a significant number of movies from Netflix only get ordered occasionally. However, since the storage is in digital form for the songs and music (and Amazon out sources the storage of books) there is little additional inventory cost of these items. This phenomenon across all Web companies has led to a broadening of participation by both producers and consumers that would not have happened without the Web. The idea of the long tail is well known, of course. What Anderson has done is present it in an interesting manner and in a Web ecommerce setting. He applies it to Web businesses and then relates the multitude of other factors ongoing that permit the actual implementation of the long tail effect. Anderson also expands on prior work on the long tail by introducing an element of time, given the distribution a three dimensional effect. All in all, it is a nifty idea. The book is comprised of 14 chapters, plus an Introduction. Chapter 1 presents an overview of what the long tail is. Chapter 2 discusses the ''head'', which is the top of the tail where the …

The Long Tail: Why the Future of Business is Selling Less of More

https://cronfa.swan.ac.uk/Record/cronfa54400/Download/54400__17763__d6602f13cc44449b8145bc15173fc903.pdf

Setting the future of digital and social media marketing research: Perspectives and research propositions

Eli Pariser coined the term 'filter bubble' to describe the potential for online personalization to effectively isolate people from a diversity of viewpoints or content. Online recommender systems - built on algorithms that attempt to predict which items users will most enjoy consuming - are one family of technologies that potentially suffers from this effect. Because recommender systems have become so prevalent, it is important to investigate their impact on users in these terms. This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users. To the best of our knowledge, it is the first paper to measure the filter bubble effect in terms of content diversity at the individual level. We contribute a novel metric to measure content diversity based on information encoded in user-generated tags, and we present a new set of methods to examine the temporal effect of recommender systems on the user experience. We do find that recommender systems expose users to a slightly narrowing set of items over time. However, we also see evidence that users who actually consume the items recommended to them experience lessened narrowing effects and rate items more positively.

Exploring the filter bubble: the effect of using recommender systems on content diversity

We describe the effects of social media advertising content on customer engagement using Facebook data. We content-code more than 100,000 messages across 800 companies using a combination of Amazon Mechanical Turk and state-of-the-art Natural Language Processing and machine learning algorithms. We use this large-scale dataset of content attributes to describe the association of various kinds of social media marketing content with user engagement - defined as Likes, comments, shares, and click-throughs - with the messages. We find that inclusion of widely used content related to brand-personality - like humor, emotion and brand’s philanthropic positioning - is associated with higher levels of consumer engagement (like, comment, share) with a message. We find that directly informative content - like mentions of prices and availability - is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand-personality content. We also find certain directly informative content such as the mention of deals and promotions drive consumers’ path-to-conversion (click-throughs). These results hold after correcting for the non-random targeting of Facebook’s EdgeRank (News Feed) algorithm, so reflect more closely user reaction to content, rather than Facebook’s behavioral targeting. Our results suggest therefore that there may be substantial gains from content engineering by combining informative characteristics associated with immediate leads (via improved click-throughs) with brand-personality related content that help maintain future reach and branding on the social media site (via improved engagement). These results inform content design strategies in social media. Separately, the methodology we apply to content-code large-scale textual data provides a framework for future studies on unstructured data such as advertising content or product reviews.

/pdf/advertising-content-and-consumer-engagement-on-social-media-3xhxg1i22p.pdf

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement—defined as Likes, comments, shares, and click-throughs—with the messages. We find that inclusion of widely used content related to brand personality—like humor and emotion—is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content—like mentions of price and deals—is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality–related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers’ path to conversio...

/pdf/advertising-content-and-consumer-engagement-on-social-media-5g1e8jzq62.pdf

Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer's preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has drawbacks. By making the Web hyperspecific to our interests, does it fragment Internet users, reducing shared experiences and narrowing media consumption? We study whether personalization is in fact fragmenting the online population. Surprisingly, it does not appear to do so in our study. Personalization appears to be a tool that helps users widen their interests, which in turn creates commonality with others. This increase in commonality occurs for two reasons, which we term volume and product-mix effects. The volume effect is that consumers simply consume more after personalized recommendations, increasing the chance of having more items in common. The product-mix effect is that, conditional on volume, consumers buy a more simil...

/pdf/will-the-global-village-fracture-into-tribes-recommender-31lsgpd2w8.pdf

Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation

How consumers use review content has remained opaque due to the unstructured nature of text and the lack of review-reading behavior data. The authors overcome this challenge by applying deep learni...

Large Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning.

Recommender systems appear all across the internet. For e-retailers, this represents an opportunity to get more and niche products before customers’ eyes. However, we find that while implementing r...

/pdf/how-do-recommender-systems-affect-sales-diversity-a-cross-2qdk376qqo.pdf

Dokyun Lee

Papers

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation

Large Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning.

How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment