YouTube uploaders are the central agents in the YouTube phenomenon. We conduct extensive measurement and analysis of YouTube uploaders. We estimate YouTube scale and examine the uploading behavior of YouTube users. We demonstrate the positive reinforcement between on-line social behavior and uploading behavior. Furthermore, we examine whether YouTube users are truly broadcasting themselves, via characterizing and classifying videos as either user generated or user copied.

With the growing pervasiveness of the Internet, online search for products and services is constantly increasing. Most product search engines are based on adaptations of theoretical models devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects.

With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.

The phenomenon of sponsored search advertising - where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results - is gaining ground as the largest source of revenues for search engines. Despite the growth of search advertising, we have little understanding of how consumers respond to contextual and sponsored search advertising on the Internet. Using a unique panel dataset of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different metrics such as click-through rates, conversion rates, bid prices and keyword ranks. Our paper proposes a novel framework and data to better understand what drives these differences. We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. We empirically estimate the impact of keyword attributes on consumer search and purchase behavior as well as on firms' decision-making behavior on bid prices and ranks. We find that the presence of retailer-specific information in the keyword increases click-through rates, and the presence of brand-specific information in the keyword increases conversion rates. We also demonstrate that as suggested by anecdotal evidence, search engines like Google factor in both the auction bid price as well as prior click-through rates before allotting a final rank to an advertisement. To the best of our knowledge, this is the first study that uses real world data from an advertiser and jointly estimates the effect of sponsored search advertising at a keyword level on consumer search, click and purchase behavior in electronic markets

The phenomenon of sponsored search advertising is gaining ground as the largest source of revenues for search engines. Firms across different industries have are beginning to adopt this as the primary form of online advertising. This process works on an auction mechanism in which advertisers bid for different keywords, and final rank for a given keyword is allocated by the search engine. But how different are firm's actual bids from their optimal bids? Moreover, what are other ways in which firms can potentially benefit from sponsored search advertising? Based on the model and estimates from prior work [10], we conduct a number of policy simulations in order to investigate to what extent an advertiser can benefit from bidding optimally for its keywords. Further, we build a Hierarchical Bayesian modeling framework to explore the potential for cross-selling or spillovers effects from a given keyword advertisement across multiple product categories, and estimate the model using Markov Chain Monte Carlo (MCMC) methods. Our analysis suggests that advertisers are not bidding optimally with respect to maximizing profits. We conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement. We find that there exists significant potential for cross-selling through search keyword advertisements in that consumers often end up buying products from other categories in addition to the product they were searching for. Latency (the time it takes for consumer to place a purchase order after clicking on the advertisement) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet.

With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums.

The Internet has brought about a fundamental change in the way consumers obtain and process information. It is well documented now that the online retailing revolution has established a new distribution channel that represents a fundamental paradigm shift in consumer buying patterns. Hence, firms are realizing that reaching the increasing number of online users by attracting clients to their websites is the key to bolstering online sales. In this regard, search engines are able to leverage the value as information location tools by selling advertising linked to search terms entered by online users and referring them to the advertisers. The phenomenon of sponsored search advertising - where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results - is gaining ground as the largest source of revenues for search engines. The global paid search advertising market is predicted to have a 37 percent compound annual growth rate (CAGR), to more than $33 billion in 2010 and has become a critical component of firm's marketing campaigns. This is not surprising given that 94% of consumers use search engines to find information on the Web (Nielson-Net Ratings), and 81% who use search engines find the information they are looking for every time they search.

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data.

Internet referral services, hosted either by independent third-party infomediaries or by manufacturers serve as lead-generators in electronic marketplaces, directing consumer traffic to particular retailers. In a model of price dispersion, we investigate the competitive implications of these institutions on retailer prices and their impact on channel structures and distribution of profits. Offline, retailers face a higher customer acquisition cost. In return, they obtain full information about consumer valuations. Online, they save on the acquisition costs, but lose the ability to charge prices based on consumer valuation. This critical tradeoff drives equilibrium profits. The establishment of a referral service is a strategic decision by the manufacturer, in response to a third-party infomediary. It leads to an increase in channel profits and a reallocation of the increased surplus to the manufacturer, via the franchise fees. The impetus to increased profits comes from both retailers' ability to price discriminate between informed and uninformed consumers, and by the lowering of acquisition costs due to diversion of traffic from the offline to the online channel. Our model highlights that online prices are lower than offline prices for the retailer enrolled with the infomediary. We point out that in equilibrium, the retailer associated with the infomediary, has higher expected sales and gross profits. We also show that some of the online cost savings are transferred to the consumer, leading to an overall increase in consumer welfare.