Syndicated from http://www.hpl.hp.com/research/ – LABS HP.

Social Computing Lab

1. Artificial Inflation: The Real Story of Trends in Sina Weibo - There has been a tremendous rise in the growth of online social networks all over the world in recent years. This has facilitated users to generate a large amount of real-time content at an incessant rate, all competing with each other to attract enough attention and become trends. While Western online social networks such as Twitter have been well studied, characteristics of the popular Chinese microblogging network Sina Weibo has not been. In this paper, we analyze in detail the temporal aspect of trends and trend-setters in Sina Weibo, constrasting it with earlier observations on Twitter. One of our key findings is that a large percentage of trends in Sina Weibo are due to the continuous retweets of a small amount of fraudulent accounts. These fake accounts are set up to artificially inflate certain posts causing them to shoot up into Sina Weibo's trending list.
2. The Pulse of News on Social Media: Forecasting Popularity - News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.
3. Long Trend Dynamics in Social Media - A main characteristic of social media is that its diverse content, copiously generated by both standard outlets and general users, constantly competes for the scarce attention of large audiences. Out of this flood of information some topics manage to get enough attention to become the most popular ones and thus to be prominently displayed as trends. Equally important, some of these trends persist long enough so as to shape part of the social agenda. How this happens is the focus of this paper. By introducing a stochastic dynamical model that takes into account the user's repeated involvement with given topics, we can predict the distribution of trend durations as well as the thresholds in popularity that lead to their emergence within social media. Detailed measurements of datasets from Twitter confirm the validity of the model and its predictions.
4. Collective Attention and the Dynamics of Group Deals - We present a study of the group purchasing behavior of daily deals in Groupon and LivingSocial and introduce a predictive dynamic model of collective attention for group buying behavior. In our model, the aggregate number of purchases at a given time comprises two types of processes: random discovery and social propagation. We find that these processes are very clearly separated by an inflection point. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group deals as a function of time. We find that Groupon deals are easier to predict accurately earlier in the deal lifecycle than LivingSocial deals due to the final number of deal purchases saturating quicker. One possible explanation for this is that the incentive to socially propagate a deal is based on an individual threshold in LivingSocial whereas it is based on a collective threshold, which for the most part is reached very early on in Groupon. Furthermore, the personal benefit of propagating a deal is also greater in LivingSocial.
5. Swayed by Friends or by the Crowd? - We conducted three empirical studies of the effects of friend recommendations and general ratings on how online users make choices. These two components of social influence were investigated through user studies on Mechanical Turk. We find that for a user deciding between two choices an additional rating star has a much larger effect than an additional friend's recommendation on the probability of selecting an item. Equally important, negative opinions from friends are more influential than positive opinions, and people exhibit more random behavior in their choices when the decision involves less cost and risk. Our results can be generalized across different demographics, implying that individuals trade off recommendations from friends and ratings in a similar fashion.
6. Understanding Social Influence in Recommender Systems - To investigate whether online recommendations can sway peoples' own opinions, we designed and ran an experiment to test how often people's choices are reversed by others' preferences when facing different levels of confirmation and conformity pressures. In our experiment participants were first asked to provide their preferences between pairs of items. They were then asked to make second choices about the same pairs with knowledge of others' preferences. To measure the pressure to confirm people's own opinions, we manipulated the time before the participants needed to make their second decisions. And to determine the effects of social pressure we manipulated the ratio of opposing opinions that the participants saw when making the second decision. Additionally, we tested whether other factors (i.e. age, gender and decision time) affect the tendency to revert. Our results show that others people's opinions significantly sway people's own choices. The influence is stronger when people are required to make their second decision sometime later (22.4%) than immediately (14.1%). Moreover, people are most likely to reverse their choices when facing a moderate number of opposing opinions. Finally, the time people spend making the first decision significantly predicts whether they will reverse their decisions later on, while demographics such as age and gender do not.
7. What Trends in Chinese Social Media - There has been a tremendous rise in the growth of online social networks all over the world in recent times. While some networks like Twitter and Facebook have been well documented, the popular Chinese microblogging social network Sina Weibo has not been studied. In this work, we examine the key topics that trend on Sina Weibo and contrast them with our observations on Twitter. We find that there is a vast difference in the content shared in China, when compared to a global social network such as Twitter. In China, the trends are created almost entirely due to retweets of media content such as jokes, images and videos, whereas on Twitter, the trends tend to have more to do with current global events and news stories.
8. Rankr: A Mobile System for Crowdsourcing Opinions - Evaluating large sets of items, be they business ideas, priorities or agile feature requests, is a difficult task. But while no one person has time to evaluate all the items, many people can contribute by each evaluating a few. Moreover, given the mobility of people, it is useful to allow them to evaluate items from their mobile devices. We present the design, implementation and evaluation of a new mobile service, Rankr, which provides a lightweight and efficient way to crowdsource the relative ranking of ideas, photos, music, or priorities through a series of pairwise comparisons. Through a usability test, we discover that users are willing to sacrifice fidelity in order to have two items displayed at the same time on their mobile devices. From an algorithm standpoint, given the votes that others have already cast, Rankr automatically determines the next most useful pair of candidates a user can evaluate to maximize the information gained while minimizing the number of votes required. Unlike typical rank voting methods, voters do not need to compare and manually rank all of the candidates.
9. The Sunk Cost Fallacy in Reverse Auctions - We empirically study buyer behavior in an online outsourcing website where sealed bid auctions are held with bids arriving over time. We focus on when buyers terminate their requests and how they behave when choosing the winning bid. We find that buyers tend to choose any bid prior to the last one with the approximately the same frequency, whereas they are more likely to choose the last bid. We provide a simple probabilistic model that captures this behavior. The key characteristic of this model is that buyers are more likely to stop when the most recent bid is the best so far. This feature is related to the sunk cost fallacy: once a buyer has waited for some time, she has an escalating tendency to continue waiting until a bid that is better than all prior bids arrives. A buyer is unwilling to recall early bids, because that would make her perceive the time since the arrival of early bids as wasted, even though the time cost has already been incurred at the time of the decision.
10. Trends in Social Media : Persistence and Decay - Social media generates a prodigious wealth of real-time content at an incessant rate. From all the content that people create and share, only a few topics manage to attract enough attention to rise to the top and become temporal trends which are displayed to users. The question of what factors cause the formation and persistence of trends is an important one that has not been answered yet. In this paper, we conduct an intensive study of trending topics on Twitter and provide a theoretical basis for the formation, persistence and decay of trends. We also demonstrate empirically how factors such as user activity and number of followers do not contribute strongly to trend creation and its propagation. In fact, we find that the resonance of the content with the users of the social network plays a major role in causing trends.