Sequential Voting Promotes Collective Discovery in Social Recommendation Systems

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.Comment: To be published in the 10th International AAAI Conference on Web and Social Media (ICWSM) 201

Visualizing Contextual Information in Aggregated Web Content Repositories

Understanding stakeholder perceptions and the impact of campaigns are key insights for communication experts and policy makers. A structured analysis of Web content can help answer these questions, particularly if this analysis involves the ability to extract, disambiguate and visualize contextual information. After summarizing methods used for acquiring and annotating Web content repositories, we present visualization techniques to explore the lexical, geospatial and relational context of entities in these repositories. The examples stem from the Media Watch on Climate Change, a publicly available Web portal that aggregates environmental resources from various online sources

An Experimental Study of Cryptocurrency Market Dynamics

As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect market behavior. One existential threat to cryptocurrencies is dramatic fluctuations in traders' willingness to buy or sell. Using a novel experimental methodology, we conducted an online experiment to study how susceptible traders in these markets are to peer influence from trading behavior. We created bots that executed over one hundred thousand trades costing less than a penny each in 217 cryptocurrencies over the course of six months. We find that individual "buy" actions led to short-term increases in subsequent buy-side activity hundreds of times the size of our interventions. From a design perspective, we note that the design choices of the exchange we study may have promoted this and other peer influence effects, which highlights the potential social and economic impact of HCI in the design of digital institutions.Comment: CHI 201

Semantic Systems and Visual Tools to Support Environmental Communication

Given the intense attention that environmental topics such as climate change attract in news and social media coverage, scientists and communication professionals want to know how different stakeholders perceive observable threats and policy options, how specific media channels react to new insights, and how journalists present scientific knowledge to the public. This paper investigates the potential of semantic technologies to address these questions. After summarizing methods to extract and disambiguate context information, we present visualization techniques to explore the lexical, geospatial, and relational context of topics and entities referenced in these repositories. The examples stem from the Media Watch on Climate Change, the Climate Resilience Toolkit and the NOAA Media Watch—three applications that aggregate environmental resources from a wide range of online sources. These systems not only show the value of providing comprehensive information to the public, but also have helped to develop a novel communication success metric that goes beyond bipolar assessments of sentiment

Modeling consumer behaviour in the presence of network effects

Consumer choice models are a key component in fields such as Revenue Management and Transport Logistics, where the demands for certain products or services are assumed to follow a particular form, and sellers or market-makers use that information to adjust their strategies accordingly, choosing for example which products to display (assortment problem) or their prices (pricing problem). In the last couple of decades, online markets have taken a lot of relevance, providing a setting where consumers can compare easily different products, before deciding to buy them. More information is now available, and the purchasing decisions not only depend on the quality, prices and availability of the products, but also on what previous consumers think about them (phenomenon commonly known as Network Effects). Hence, in order to create a suitable model for this kind of market, it is relevant to understand how the collective decisions affect the market evolution. In this thesis we consider a particular subset of those online markets, cultural markets, where the products are for example songs, video games or ebooks. This kind of market has the special feature that its products have unlimited supply (since they are just a digital copy), and therefore we can exploit this in our models, to justify assumptions of the asymptotic behaviour of the market. We study some variations of the traditional Multinomial Logit (MNL) model, characterising the behaviour of consumers, where their purchasing decisions are affected by the quality and prices (initially fixed) of the available products, as well as their visibilities in the market interface and the consumption patterns of previous users. We focus particularly on the parameters associated to the network effects, where depending on the strength of the network effects, it is possible to explain: herd behaviours, where an alternative overpowers the rest; as well as more well-distributed settings, where all the alternatives receive enough attention giving a notion of fairness, since higher quality products get a larger market share. Finally, using the model where market shares are distributed according to the quality of the products, we study pricing strategies, where sellers can either collaborate or compete. We analyse the effect of both type of strategies into the choice model

The effects of online reviews and promotional messages on product performance : review helpfulness and the power of language

This dissertation contains two essays. For Essay One, previous studies on review helpfulness focus on what makes a review helpful and how to predict review helpfulness. In so doing, researchers hope to identify the most helpful reviews for consumers and improve the recommendation system. However, little is known about the effect of these helpful reviews on product performance. Thus, this paper investigates how helpful reviews or the helpfulness votes influence product sales. Since product sales are only available at the group (product) level, estimating the effect of helpfulness votes presents a challenging multilevel problem. This research considers both the disaggregating (individual) and the aggregating (group) approaches and compares four competing models in their parameter estimates and model fitness. The results suggest that the average number of votes performs the worst while the mean-adjusted model slightly improves predictive power. Among them, the total number of helpfulness votes renders the best predictive performance. For Essay Two, crowdfunding has become a trendy way to raise funding nowadays. Budding entrepreneurs try to make a convincing pitch to attract potential backers\u27 interest. Existing studies have found that linguistic styles such as the narrative tone, the use of emotional or informational arguments, concreteness, precision, and interactivity are signals of underlying project quality. Nevertheless, this body of research lacks proof of the effect of micro-level linguistic elements on the success of crowdfunding. In this essay, we conduct two studies to investigate the effect of word-level and topic-level linguistic characteristics on crowdfunding outcomes. In Study One, we adopt a multimethod approach which includes N-gram natural language processing model, penalized logistic regression (PLR), and linguistic analysis to analyze the narratives of projects on Kickstarter. We find that speaking the same language and careful choice of words is critical to the success of crowdfunding. Further, the psychological meanings of the words and phrases associated with the success and failure of crowdfunding Our findings will provide a valuable insight for entrepreneurs to prepare their pitches. In Study Two, we switch our focus from the choice of word to the choice of topic. We use topic entropy to measure the theme complexity for each project pitch and examine how it would affect the probability of crowdfunding success. We find a significant prediction power of the topic entropy, with the lower (higher) the value, the more probability the success (failure) of the project. Among successful projects, certain words and topics that have more positive or negative impacts vary depending on the movie genre. This essay is one of the first in marketing research to use advanced text analysis to evaluate the effect of micro-level linguistic features on message persuasiveness. In addition, this work has further proved the power of language in effective marketing communications

Modeling the successes and failures of content-based platforms

Online platforms, such as Quora, Reddit, and Stack Exchange, provide substantial value to society through their original content. Content from these platforms informs many spheres of life—software development, finance, and academic research, among many others. Motivated by their content's powerful applications, we refer to these platforms as content-based platforms and study their successes and failures. The most common avenue of studying online platforms' successes and failures is to examine user growth. However, growth can be misleading. While many platforms initially attract a massive user base, a large fraction later exhibit post-growth failures. For example, despite their enormous growth, content-based platforms like Stack Exchange and Reddit have struggled with retaining users and generating high-quality content. Motivated by these post-growth failures, we ask: when are content-based platforms sustainable? This thesis aims to develop explanatory models that can shed light on the long-term successes and failures of content-based platforms. To this end, we conduct a series of large-scale empirical studies by developing explanatory and causal models. In the first study, we analyze the community question answering websites in Stack Exchange through the economic lens of a "market". We discover a curious phenomenon: in many Stack Exchange sites, platform success measures, such as the percentage of the answered questions, decline with an increase in the number of users. In the second study, we identify the causal factors that contribute to this decline. Specifically, we show that impression signals such as contributing user's reputation, aggregate vote thus far, and position of content significantly affect the votes on content in Stack Exchange sites. These unintended effects are known as voter biases, which in turn affect the future participation of users. In the third study, we develop a methodology for reasoning about alternative voting norms, specifically how they impact user retention. We show that if the Stack Exchange community members had voted based upon content-based criteria, such as length, readability, objectivity, and polarity, the platform would have attained higher user retention. In the fourth study, we examine the effect of user roles on the health of content-based platforms. We reveal that the composition of Stack Exchange communities (based on user roles) varies across topical categories. Further, these communities exhibit statistically significant differences in health metrics. Altogether, this thesis offers some fresh insights into understanding the successes and failures of content-based platforms