Shawndra Hill

The myth of the double-blind review?: author identification using only citations

Prior studies have questioned the degree of anonymity of the double-blind review process for scholarly research articles. For example, one study based on a survey of reviewers concluded that authors often could be identified by reviewers using a combination of the authors reference list and the referees personal background knowledge. For the KDD Cup 2003 competitions Open Task, we examined how well various automatic matching techniques could identify authors within the competitions very large archive of research papers. This paper describes the issues surrounding author identification, how these issues motivated our study, and the results we obtained. The best method, based on discriminative self-citations, identified authors correctly 40--45% of the time. One main motivation for double-blind review is to eliminate bias in favor of well-known authors. However, identification accuracy for authors with substantial publication history is even better (60% accuracy for the top-10% most prolific authors, 85% for authors with 100 or more prior papers).

TV's Dirty Little Secret: The Negative Effect of Popular TV on Online Auction Sales

An ongoing debate questions whether TV viewers can spread their attention across multiple devices while watching TV. As a result, recent research has focused on understanding media cross-effects, particularly the relationship between TV viewing and simultaneous Internet usage. However, little attention has been given to TV viewership’s relationship with a very important economic activity: online shopping. This study examines whether online sellers need to account for exogenous effects, particularly popular TV, when predicting online sales. Applying an instrumental variable (IV) regression, where the presence of an unexpected disaster that is highly televised is used as the IV, we find evidence of a significant spillover effect between TV consumption and online sales. A lower level of interest in TV on the part of viewers leads to a significant increase in online sales, indicating that TV consumption and online sales for a large online retailer are substitutes for each other, rather than complements.

Tuning representations of dynamic network data

A dynamic network is a special type of network which is comprised of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to effcient large-scale analysis is a challenge. In this paper, we represent dynamic graphs using a data structure introduced by Cortes et. al. [3]. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task.

Post Purchase Search Engine Marketing

Although consumer behavior in response to search engine marketing has been studied extensively, few efforts have been made to understand how consumers search and respond to ads post purchase. Advertising to existing customers the same way as to prospective customers inevitably leads to wasteful and inefficient marketing. Employing a unique dataset that combines both search query and purchase data, we examine consumers searching behavior and response to search engine marketing after purchase. We study large advertising campaigns for two popular technology products. We find that over half of the branded keyword searches come from consumers who already purchased the products, and that advertising response varies based on whether searchers are pre- or post-purchase. In general, post-purchase searchers are less likely to click on focal brand ads (i.e., they are less responsive to ads for products they already own). However, post-purchase searchers are still responsive to advertising and much more likely to click on ads for complementary products (i.e., they are more responsive to ads for relevant products other than the focal product).

Using Big Data and Algorithms to Determine the Effect of Geographically Targeted Advertising on Vote Intention: Evidence From the 2012 U.S. Presidential Election

We develop a new conceptualization of political advertising effects by looking at the effect of the marginal advertising dollar during the heat of presidential campaigns. We argue that in contrast to other studies investigating effects of political ads, our approach is more apt to capture the natural environment in which political ads are encountered during a presidential campaign. We focus on the intense inundation of political ads voters are confronted with in swing states in the weeks leading up to the presidential election, and argue that it is unclear a priori whether we should expect advertising to affect vote intention in that critical circumstance. We empirically validate this hypothesis using a trove of data from the 2012 campaign: daily polling in media markets around the country, detailed data on all registered voters in the country, all TV advertisements by market and exact airtime, and the entire Twitter corpus. We find that neither overall increases in advertising spending nor partisan imbalances in spending expanded the candidates’ electorate. In fact, total Designated Market Area (DMA)-level spending significantly moderates a negative relationship between spending advantages and advantages in vote intention, suggesting a boomerang effect of additional spending late in the campaign. In closing, we discuss the ramifications of our findings for future research, and stress the importance of research tracking advertising effects.

Addressing Selection Bias in Event Studies with General-Purpose Social Media Panels

Data from Twitter have been employed in prior research to study the impacts of events. Conventionally, researchers use keyword-based samples of tweets to create a panel of Twitter users who mention event-related keywords during and after an event. However, the keyword-based sampling is limited in its objectivity dimension of data and information quality. First, the technique suffers from selection bias since users who discuss an event are already more likely to discuss event-related topics beforehand. Second, there are no viable control groups for comparison to a keyword-based sample of Twitter users. We propose an alternative sampling approach to construct panels of users defined by their geolocation. Geolocated panels are exogenous to the keywords in users’ tweets, resulting in less selection bias than the keyword panel method. Geolocated panels allow us to follow within-person changes over time and enable the creation of comparison groups. We compare different panels in two real-world settings: response to mass shootings and TV advertising. We first show the strength of the selection biases of keyword panels. Then, we empirically illustrate how geolocated panels reduce selection biases and allow meaningful comparison groups regarding the impact of the studied events. We are the first to provide a clear, empirical example of how a better panel selection design, based on an exogenous variable such as geography, both reduces selection bias compared to the current state of the art and increases the value of Twitter research for studying events. While we advocate for the use of a geolocated panel, we also discuss its weaknesses and application scenario seriously. This article also calls attention to the importance of selection bias in impacting the objectivity of social media data.