CAOSS 2012: Workshop on Computational and Online Social Science

Social science research based on Twitter and other massive online datasets has taken off in recent years. A large collection of tweets is an exciting source of information with which to investigate basic psychological questions. Golder and Macy (2011) estimated diurnal and seasonal mood patterns across cultures using Twitter data. Even just a few years ago, Daniel Kahneman and his colleagues (see Krueger et al. 2009) needed to run costly and time-consuming lab experiments and telephone surveys to answer similar questions. But convenient datasets must be analyzed with care to avoid bias. Beyond the obvious question of representativeness, other factors may introduce bias. As a case study, we compare Golder and Macy (2011)’s estimates of diurnal mood patterns to patterns we estimate from a novel experience sampling (ES) dataset from paid study participants (n = 2041) collected through an iPhone application. The most notable discrepancy between the two datasets is that the Twitter dataset shows mood as a “U”-shape, with good mood in the morning deteriorating to a valley from noon until 5pm, and then increasing until midnight. In contrast, the ES dataset shows the opposite, an inverted “U”-shape, with the lowest mood at waking, increasing to a maximum plateau at noon, and then decreasing until midnight, a pattern much more in line with previous research. We investigate this discrepancy and discuss various possible explanations for the striking contrast. Using ES data on concurrent self-reports of activities and emotions we estimate a basic confounding variable, asking whether mood is independent of the likelihood of a user to use a social networking site. We also look for sub-populations of our dataset that resemble the Twitter users in diurnal patterns, and investigate the reliability of sentiment analysis on tweets. While datasets like Twitter are appealing, properly avoiding bias is a challenge. Meanwhile, classically designed studies can achieve much larger sample sizes than in the past because of the widespread availability of smartphones. Fortunately, this is not an either/or proposition: methods for big datasets are helpful in analyzing the ES dataset we discuss, and the ES dataset gives insight into sources of bias in the Twitter dataset.

Exchange networks model the behavior of a set of players who need to reach pairwise agreements for mutual benefit, as in the labor market, the housing market and the 'market' for social relationships. We describe internet-based experiments on bargaining in networks, that are the largest such experiments to date. Our results include some of the first insights into the dynamics of bargaining.

While past research clearly demonstrates how social influence leads to the emergence of self-fulfilling prophecies in cultural markets it remains an open question if, and to what degree, this process occurs in other market types. Using a web-based natural field experiment on the philanthropic fundraising platform DonorsChoose.Org we are able to causally identify the effects of social influence in crowdfunding markets. Specifically we find that self-reinforcing cycles do occur within crowdfunded marketplaces but that not all forms of social influence have an equal impact on the behavior of subsequent donors. Beyond demonstrating the role of social influence in non-cultural markets our work sheds light on the under-studied dynamics of the rapidly growing crowdfunding industry.

The explosion of data from eCommerce and social media provides an unprecedented opportunity to empirically test theories in Behavioural Economics. In this short talk, I’ll discuss a couple of theories that have been well-documented in lab and field experiments, namely Prospect Theory and Projection Bias, and present some of our recent results from eCommerce datasets: 1) Using a large eCommerce transactions dataset, we test predictions of Prospect theory: the propensity of consumers to be loss-averse with respect to a reference point. As opposed to standard economic theory explanations such as stockpiling, prospect theory predicts that consumers’ purchases of substitute products would be influenced by whether or not an item is on sale. 2) Projection bias is the propensity for consumers to make errors in predicting their future consumption, when influenced by current, temporary events such as weather and social trends. We test this effect using local weather data at the time of purchases of durable goods in our eCommerce dataset. Theoretical models from Behavioural Economics provide a principled framework for analyzing and interpreting human behavior from massive datasets. The empirical estimation of these effects has real-world economic prescriptions, for the pricing and promotions strategies of eCommerce retailers.

Social scientists are now debating the usefulness of Big Data for the future of their field(s). In our recent work, we have developed a proof of concept study of one way in which novel sources of data about human behavior might enrich social science research: we exploit social network data gathered from Twitter to improve our capacity to measure the political ideology of the Members of the 110th-112th sessions of the United States Congress. We demonstrate that we can extend the Ideal Points model of political ideology, which traditionally uses only voting records, by incorporating data from social networks into our analysis. This extension allows us to estimate the ideology of members of both Houses of the United States Congress on a common scale even though the voting records for the two Houses are strictly disjoint. We argue that multimodal modeling approaches, in which heterogenous data sets are analyzed in aggregate, provide one mechanism for the social sciences to leverage the kinds of large-scale, naturalistic data being generated by the Internet’s ubiquitous data generating processes.

Daily deals sites such as Groupon offer deeply discounted goods and services to tens of millions of customers through geographically targeted daily e-mail marketing campaigns. In our prior work we observed that a negative side effect for merchants using Groupons is that, on average, their Yelp ratings decline significantly. However, this previous work was essentially observational, rather than explanatory. In this work, we rigorously consider and evaluate various hypotheses about underlying consumer and merchant behavior in order to understand this phenomenon, which we dub the Groupon effect. We use statistical analysis and mathematical modeling, leveraging a dataset we collected spanning tens of thousands of daily deals and over 7 million Yelp reviews. In particular, we investigate hypotheses such as whether Groupon subscribers are more critical than their peers, or whether some fraction of Groupon merchants provide significantly worse service to customers using Groupons. We suggest an additional novel hypothesis: reviews from Groupon subscribers are lower on average because such reviews correspond to real, unbiased customers, while the body of reviews on Yelp contain some fraction of reviews from biased or even potentially fake sources. Although we focus on a specific question, our work provides broad insights into both consumer and merchant behavior within the daily deals marketplace.