Positivity Bias in Customer Satisfaction Ratings

Customer ratings are valuable sources to understand their satisfaction and are critical for designing better customer experiences and recommendations. The majority of customers, however, do not respond to rating surveys, which makes the result less representative. To understand overall satisfaction, this paper aims to investigate how likely customers without responses had satisfactory experiences compared to those respondents. To infer customer satisfaction of such unlabeled sessions, we propose models using recurrent neural networks (RNNs) that learn continuous representations of unstructured text conversation. By analyzing online chat logs of over 170,000 sessions from Samsung's customer service department, we make a novel finding that while labeled sessions contributed by a small fraction of customers received overwhelmingly positive reviews, the majority of unlabeled sessions would have received lower ratings by customers. The data analytics presented in this paper not only have practical implications for helping detect dissatisfied customers on live chat services but also make theoretical contributions on discovering the level of biases in online rating platforms.Comment: This paper will be presented at WWW'18 conferenc

Positivity Bias in Customer Satisfaction Ratings

Customer ratings are valuable sources to understand their satisfaction and are critical for designing better customer experiences and recommendations. The majority of customers, however, do not respond to rating surveys, which makes the result less representative. To understand overall satisfaction, this paper aims to investigate how likely customers without responses had satisfactory experiences compared to those respondents. To infer customer satisfaction of such unlabeled sessions, we propose models using recurrent neural networks (RNNs) that learn continuous representations of unstructured text conversation. By analyzing online chat logs of over 170,000 sessions from Samsung's customer service department, we make a novel finding that while labeled sessions contributed by a small fraction of customers received overwhelmingly positive reviews, the majority of unlabeled sessions would have received lower ratings by customers. The data analytics presented in this paper not only have practical implications for helping detect dissatisfied customers on live chat services but also make theoretical contributions on discovering the level of biases in online rating platforms.Comment: This paper will be presented at WWW'18 conferenc

Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the information required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier capable of combining them. In this paper we explore several methods of creating such a classifier, including early, late, hybrid fusion and representation learning using multimodal graphs. We evaluate performance on a real world dataset obtained from a live citizen reporting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demonstrate that our hybrid combination of early and late fusion with multimodal embeddings performs best in classification of urban micro-events

Establishing a Legitimate Expectation of Privacy in Clickstream Data

This Article argues that Web users should enjoy a legitimate expectation of privacy in clickstream data. Fourth Amendment jurisprudence as developed over the last half-century does not support an expectation of privacy. However, reference to the history of the Fourth Amendment and the intent of its drafters reveals that government investigation and monitoring of clickstream data is precisely the type of activity the Framers sought to limit. Courts must update outdated methods of expectation of privacy analysis to address the unique challenges posed by the Internet in order to fulfill the Amendment\u27s purpose. Part I provides an overview of the Internet and cickstream data collection, and explains the value of this data to law enforcement. Part II discusses general Fourth Amendment principles, then explores how these principles have been, and are likely to be, applied to the Internet. Part III explores the intent of the Fourth Amendment\u27s drafters, analogizes clickstream searches to the general searches the Framers sought to prohibit, and argues that the values underlying the Fourth Amendment require courts to eschew the traditional two-prong expectation of privacy test in favor of a normative inquiry which recognizes a legitimate expectation of privacy in clickstream data

Cyberspace and Real-World Behavioral Relationships: Towards the Application of Internet Search Queries to Identify Individuals At-risk for Suicide

The Internet has become an integral and pervasive aspect of society. Not surprisingly, the growth of ecommerce has led to focused research on identifying relationships between user behavior in cyberspace and the real world - retailers are tracking items customers are viewing and purchasing in order to recommend additional products and to better direct advertising. As the relationship between online search patterns and real-world behavior becomes more understood, the practice is likely to expand to other applications. Indeed, Google Flu Trends has implemented an algorithm that accurately charts the relationship between the number of people searching for flu-related topics on the Internet, and the number of people who actually have flu symptoms in that region. Because the results are real-time, studies show Google Flu Trends estimates are typically two weeks ahead of the Center for Disease Control. The Air Force has devoted considerable resources to suicide awareness and prevention. Despite these efforts, suicide rates have remained largely unaffected. The Air Force Suicide Prevention Program assists family, friends, and co-workers of airmen in recognizing and discussing behavioral changes with at-risk individuals. Based on other successes in correlating behaviors in cyberspace and the real world, is it possible to leverage online activities to help identify individuals that exhibit suicidal or depression-related symptoms? This research explores the notion of using Internet search queries to classify individuals with common search patterns. Text mining was performed on user search histories for a one-month period from nine Air Force installations. The search histories were clustered based on search term probabilities, providing the ability to identify relationships between individuals searching for common terms. Analysis was then performed to identify relationships between individuals searching for key terms associated with suicide, anxiety, and post-traumatic stress

Challenges in Short Text Classification: The Case of Online Auction Disclosure

Text classification is an important research problem in many fields. We examine a special case of textual content namely, short text. Examples of short text appear in a number of contexts such as online reviews, chat messages, twitter feeds, etc. In this research, we examine short text for the purpose of classification in internet auctions. The “ask seller a question” forum of a large horizontal intermediary auction platform is used to conduct this research. We describe our approach to classification by examining various solution methods to the problem. The unsupervised K-Medoids clustering algorithm provides useful but limited insights into keywords extraction while the supervised Naïve Bayes algorithm successfully achieves on average, around 65% classification accuracy. We then present a score assigning approach to this issue which outperforms the other two methods. Finally, we discuss how our approach to short text classification can be used to analyse the effectiveness of internet auctions

Virtual Playgrounds and Buddybots: A Data-Minefield for Tweens

This article examines the online places where tweens play, chat, and hang out. We argue that the vision behind these places is defined by commercial imperatives that seek to embed surveillance deeper and deeper into children’s playgrounds and social interactions. Online marketers do more than implant branded products into a child’s play; they collect the minute details of a child’s life so they can build a ‘‘relationship’’ of ‘‘trust’’ between the child and brand. Although marketing to children is not new, a networked environment magnifies the effect on a child’s identity because it opens up a child’s private online spaces to the eye of the marketer in unprecedented ways. Online marketers accordingly invade the child’s privacy in a profound sense, by artificially manipulating the child’s social environment and communications in order to facilitate a business agenda. We start by examining five of the Web sites that have been identified by tweens as ‘‘favorites’’. Each site contains examples of marketing practices that are typical of virtual playgrounds, and which turn kids’ online play into a continuous feedback loop for market research. After looking at the places where tweens play, we turn to one of the places where tweens talk. We examine how the principles of human-computer interaction have been used in an instant messaging environment to create virtual ‘‘people’’ that interact with kids, for all intents and purposes, like a real person. By logging the interactions, these BuddyBot programs are able to ‘‘learn’’ about the child and create the illusion of friendship between it and the child. This perfects the relationship between the child and the brand by introducing a virtual person into the equation, a person who is able to give the child ideas about what clothes to wear, what movies to see, what products to buy. Finally, we provide a brief overview of American and Canadian legislation dealing with children’s online privacy, and assess whether or not current laws have been able to protect children’s privacy in the online environment. We also examine the ways in which electronic commerce legislation has addressed the role of virtual agents, and assess how well fair information practices can protect kids from the invasive nature of child-bot relationships

Establishing a Legitimate Expectation of Privacy in Clickstream Data

This Article argues that Web users should enjoy a legitimate expectation of privacy in clickstream data. Fourth Amendment jurisprudence as developed over the last half-century does not support an expectation of privacy. However, reference to the history of the Fourth Amendment and the intent of its drafters reveals that government investigation and monitoring of clickstream data is precisely the type of activity the Framers sought to limit. Courts must update outdated methods of expectation of privacy analysis to address the unique challenges posed by the Internet in order to fulfill the Amendment\u27s purpose. Part I provides an overview of the Internet and cickstream data collection, and explains the value of this data to law enforcement. Part II discusses general Fourth Amendment principles, then explores how these principles have been, and are likely to be, applied to the Internet. Part III explores the intent of the Fourth Amendment\u27s drafters, analogizes clickstream searches to the general searches the Framers sought to prohibit, and argues that the values underlying the Fourth Amendment require courts to eschew the traditional two-prong expectation of privacy test in favor of a normative inquiry which recognizes a legitimate expectation of privacy in clickstream data

Mathematical modelling of the statistics of communication in social networks

PhDChat rooms are of enormous interest to social network researchers as they are one of the most interactive internet areas. To understand the behaviour of users in a chat room, there have been studies on the analysis of the Response Waiting Time (RWT) based on traditional approaches of aggregating the network contacts. However, real social networks are dynamic and properties such as RWT change over time. Unfortunately, the traditional approach focuses only on static network and neglecting the temporal variation in RWT which may have lead to misrepresentation of the true nature of RWT. In order to determine the true nature of RWT, we analyse and compare the RWT of three online chat room logs (Walford, IRC and T-REX) putting into consideration the dynamic nature of RWT. Our research shows that the distribution of the RWT exhibits multi-scaling behaviour, which signi cantly a ects the current views on the nature of RWT. This is a shift from simple power-law distribution to a more complex pattern. The previous study on users RWT between pairs of people claims that the RWT has a power-law distribution with an exponent of 1. However, our research shows that multi-scaling behaviour and the exponent has a wider range of values which depend on the environment and time of day. The di erent exponents observed on di erent time scales suggest that the time context or environment has a signi cant in uence on users RWT. Furthermore, using the chat characterise, we predicted the factors which could minimize response waiting time and improving the friendship connection during online chat sessions. We apply our ndings to design an algorithm for chat thread detection. Here, we proposed two variations of cluster algorithm. The rst algorithm involves the traditional approach while in the second one, the temporal variations in RWT was taken into consideration to capture the dynamic nature of a text stream. An advantage of our proposed method over the previous models is that previous models have involved highly computationally intensive methods and often lead to deterioration in the accuracy of the result whereas our proposed approach uses a simple and e ective sequential thread detection method, which is less computationally intensiveSAS Graduate Research Fellowshi