In an information-rich world, people's time and attention must be divided among rapidly changing information sources and the diverse tasks demanded of them. How people decide which of the many sources, such as scientific articles or patents, to read and use in their own work affects dissemination of scholarly knowledge and adoption of innovation. We analyze the choices people make about what information to propagate on the citation networks of Physical Review journals, US patents and legal opinions. We observe regularities in behavior consistent with human bounded rationality: rather than evaluate all available choices, people rely on simply cognitive heuristics to decide what information to attend to. We demonstrate that these heuristics bias choices, so that people preferentially propagate information that is easier to discover, often because it is newer or more popular. However, we do not find evidence that popular sources help to amplify the spread of information beyond making it more salient. Our paper provides novel evidence of the critical role that bounded rationality plays in the decisions to allocate attention in social communication.
Bounded rationality is recognized as an important factor shaping human decisions and behavior @cite_3 , including consumer behavior @cite_43 @cite_1 @cite_36 . Computer scientists have begun to use attention, a concept related to bounded rationality, to explain social behavior online @cite_20 , including peer production of content in social media @cite_29 @cite_47 . Eye tracking studies @cite_9 @cite_12 and controlled online experiments @cite_14 demonstrated the importance of cognitive heuristics and biases in online behavior. Specifically, people pay more attention to content near the top of the page than to lower content, a cognitive bias known as position bias @cite_19 . Other studies examined how news articles, Web pages, and YouTube videos, compete for the limited collective attention @cite_39 @cite_26 @cite_13 @cite_51 .Such studies often reproduced the observed macroscopic phenomena, such as the extreme inequality of item popularity, but failed to note the link to individual's cognitive heuristics and biases.
@cite_14 @cite_26 @cite_36 @cite_29 @cite_9 @cite_1 @cite_3 @cite_39 @cite_43 @cite_19 @cite_51 @cite_47 @cite_13 @cite_12 @cite_20
2025574664 2964002805 2007590415 2165757809 1615730030 2147775745 2118303196 2952051872 2078939860 2072606289 2404406225 2014790997
With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation. Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire country’s Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics. Online peer production systems have enabled people to coactively create, share, classify, and rate content on an unprecedented scale. This paper describes strong macroscopic regularities in how people contribute to peer production systems, and shows how these regularities arise from simple dynamical rules. First, it is demonstrated that the probability a person stops contributing varies inversely with the number of contributions he has made. This rule leads to a power law distribution for the number of contributions per person in which a small number of very active users make most of the contributions. The rule also implies that the power law exponent is proportional to the effort required to contribute, as justified by the data. Second, the level of activity per topic is shown to follow a lognormal distribution generated by a stochastic reinforcement mechanism. A small number of very popular topics thus accumulate the vast majority of contributions. These trends are demonstrated to hold across hundreds of millions of contributions to four disparate peer production systems of differing scope, interface style, and purpose. While limited attention has been analyzed in a variety of economic and psychological settings, its impact on financial markets is not well understood. In this paper, we examine individual NYSE specialist portfolios and test whether liquidity provision is affected as specialists allocate their attention across stocks. Our results indicate that specialists allocate effort toward their most active stocks during periods of increased activity, resulting in less frequent price improvement and increased transaction costs for their remaining assigned stocks. Thus, the allocation of effort due to limited attention has a significant impact on liquidity provision in securities markets. In our modern society, people are daily confronted with an increasing amount of information of any kind. As a consequence, the attention capacities and processing abilities of individuals often saturate. People, therefore, have to select which elements of their environment they are focusing on, and which are ignored. Moreover, recent work shows that individuals are naturally attracted by what other people are interested in. This imitative behaviour gives rise to various herding phenomena, such as the spread of ideas or the outbreak of commercial trends, turning the understanding of collective attention an important issue of our society. In this article, we propose an individual-based model of collective attention. In a situation where a group of people is facing a steady flow of novel information, the model naturally reproduces the log-normal distribution of the attention each news item receives, in agreement with empirical observations. Furthermore, the model predicts that the popularity of a news item strongly depends on the number of concurrent news appearing at approximately the same moment. We confirmed this prediction by means of empirical data extracted from the website digg.com. This result can be interpreted from the point of view of competition between the news for the limited attention capacity of individuals. The proposed model, therefore, provides new elements to better understand the dynamics of collective attention in an information-rich world. Limited consumer attention limits product market competition: prices are stochastically lower the more attention is paid. Ads compete to be the lowest price in a sector but compete for attention with ads from other sectors: equilibrium ad shares follow a CES form. When a sector gets more proÞtable, its advertising expands: others lose ad market share. The "information hump" shows highest ad levels for intermediate attention levels. The Information Age takes off when the number of viable sectors grows, but total ad volume reaches an upper limit. Overall, advertising is excessive, though the allocation across sectors is optimal. While the statisticians are trying to knock a few tenths off the statistical error, says Mr. Payne, errors of tens of percents occur because of bad question wording. Mr. Payne's shrewd critique of the problems of asking questions reveals much about the nature of language and words, and a good deal about the public who must answer the poller's questions. For public opinion pollers, census takers, advertising copywriters, and survey makers of all kinds this book will be a tool for the achievement of more reliable results.Originally published in 1951.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These paperback editions preserve the original texts of these important books while presenting them in durable paperback editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905. Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hastag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hastag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hastag popularity, which is mostly driven by exogenous factors. It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior. The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas. Microblogging environments such as Twitter present a modality for interacting with information characterized by exposure to information “streams”. In this work, we examine what information in that stream is attended to, and how that attention corresponds to other aspects of microblog consumption and participation. To do this, we measured eye gaze, memory for content, interest ratings, and intended behavior of active Twitter users as they read their tweet steams. Our analyses focus on three sets of alignments: first, whether attention corresponds to other measures of user cognition such as memory (e.g., do people even remember what they attend to?); second, whether attention corresponds to behavior (e.g., are users likely to retweet content that is given the most attention); and third, whether attention corresponds to other attributes of the content and its presentation (e.g., do links attract attention?). We show a positive but imperfect alignment between user attention and other measures of user cognition like memory and interest, and between attention and behaviors like retweeting. To the third alignment, we show that the relationship between attention and attributes of tweets, such as whether it contains a link or is from a friend versus an organization, are complicated and in some cases counterintuitive. We discuss findings in relation to large scale phenomena like information diffusion and also suggest design directions to help maximize user attention in microblog environments. If the Web and the Net can be viewed as spaces in which we will increasingly live our lives, the economic laws we will live under have to be natural to this new space.