The researchers analyzed the CouchSurfing network to understand reciprocity and reputation. They found that while most users have few connections, a small group is very active in hosting and surfing. Direct reciprocity occurred in 12-18% of stays, while 1/3 of active users engaged in generalized reciprocity. Vouching formed a tight web of trust, with 6.8% of users vouched and 1.8% able to vouch. Friendship degree and experience ratings best predicted whether connections were vouched. Global reputation scores were poor predictors of specific vouching behavior. The researchers question whether vouches are given too freely without truly knowing someone in person.
The experimental study of economic exchange behavior revealed many discrepancies between normative theory of strategic rationality (game theory) and actual behavior. In many games games where defection and competition is expected by game theory, subjects robustly display cooperative behavior. In the ultimatum game, for instance, a ‘proposer’ makes an offer to a ‘responder’ that can either accept or refuse the offer; if the responder refuses, both players get nothing. The rational outcome is a minimal offer by the first player and an unconditional acceptance of the offer by the second. In fact, proposers make ‘fair’ offers, about 50% of the amount, responders tend to accept these offers and reject most of the ‘unfair’ offers (less than 20%;Oosterbeek et al., 2004). Cooperative and prosocial behavior is also observed in similar games, e.g. the trust game and the prisoner’s dilemma (Camerer, 2003). Neuroeconomics, the study of the neural mechanisms of decision-making (Glimcher, 2003), also showed that subjects seems to entertain prosocial preferences. Brain scans of people playing the ultimatum game indicates that unfair offers trigger, in the responders’ brain, a ‘moral disgust’: the anterior insula, an area involved in disgust and other negative emotional responses, is more active when unfair offers are proposed (Sanfey et al., 2003). In the prisoner’s dilemma and the trust game, similar activations have been found: cooperation and punishment of unfair players elicit positive affective emotions, while unfairness elicit negative one (de Quervain et al., 2004; Rilling et al., 2002).
The received view of these behavioral and neural data is that human beings are endowed with genuinely altruistic cognitive mechanisms, a view now labelled “Strong Reciprocity” (SR). According to SR, an innate propensity for altruistic punishment and altruistic rewarding makes us averse to inequity (Fehr & Rockenbach, 2004). In this talk, I argue that this moral optimism is far-fetched. Yes, the ‘cold logic’ model of rationality is not an accurate description of our decision-making mechanisms, but the SR model, I shall argue, relies on unwarranted assumptions. I present another model–the ‘hot logic’ approach–according to which human agents are selfish agents adapted to trade, exchange and partner selection in biological markets (Noë et al., 2001). Cognitive mechanisms of decision-making aims primarily at maximizing positive outcomes and minimizing negative ones. This initial hedonism is gradually modulated by social norms, by which agents learn how to maximise their utility given the norms. The ‘hot logic’ approach provide a simpler explanation of cooperation and fairness: subjects make ‘fair’ offers in the ultimatum game because they know their offer would be rejected otherwise. Responders affective reaction to ‘unfair offers’ is in fact a reaction to the loss of an expected monetary gain: they anticipated that the proposer would comply with social norms. This claim is supported by other imaging studies showing that loss of money can be aversive, and that actual and counterfactual utility recruit the same neural resources (Delgado et al., 2006; Montague et al., 2006). This approach explains why subjects make lower offers in the dictator game (an ultimatum game in which the responder make an offer and the responder's role is entirely passive) than in the ultimatum, why, when using a computer displaying eyespots, almost twice as many participants transfer money in the dictator (Haley & Fessler, 2005), and why attractive people are offered more in the ultimatum (Solnick & Schweitzer, 1999). In every case, agents seek to maximize a complex hedonic utility function, where the reward and the losses can be monetary, emotional or social (reputation, acceptance, etc.). SR is thus seen as cooperative habits that are not repaid (Burnham & Johnson, 2005).
The experimental study of economic exchange behavior revealed many discrepancies between normative theory of strategic rationality (game theory) and actual behavior. In many games games where defection and competition is expected by game theory, subjects robustly display cooperative behavior. In the ultimatum game, for instance, a ‘proposer’ makes an offer to a ‘responder’ that can either accept or refuse the offer; if the responder refuses, both players get nothing. The rational outcome is a minimal offer by the first player and an unconditional acceptance of the offer by the second. In fact, proposers make ‘fair’ offers, about 50% of the amount, responders tend to accept these offers and reject most of the ‘unfair’ offers (less than 20%;Oosterbeek et al., 2004). Cooperative and prosocial behavior is also observed in similar games, e.g. the trust game and the prisoner’s dilemma (Camerer, 2003). Neuroeconomics, the study of the neural mechanisms of decision-making (Glimcher, 2003), also showed that subjects seems to entertain prosocial preferences. Brain scans of people playing the ultimatum game indicates that unfair offers trigger, in the responders’ brain, a ‘moral disgust’: the anterior insula, an area involved in disgust and other negative emotional responses, is more active when unfair offers are proposed (Sanfey et al., 2003). In the prisoner’s dilemma and the trust game, similar activations have been found: cooperation and punishment of unfair players elicit positive affective emotions, while unfairness elicit negative one (de Quervain et al., 2004; Rilling et al., 2002).
The received view of these behavioral and neural data is that human beings are endowed with genuinely altruistic cognitive mechanisms, a view now labelled “Strong Reciprocity” (SR). According to SR, an innate propensity for altruistic punishment and altruistic rewarding makes us averse to inequity (Fehr & Rockenbach, 2004). In this talk, I argue that this moral optimism is far-fetched. Yes, the ‘cold logic’ model of rationality is not an accurate description of our decision-making mechanisms, but the SR model, I shall argue, relies on unwarranted assumptions. I present another model–the ‘hot logic’ approach–according to which human agents are selfish agents adapted to trade, exchange and partner selection in biological markets (Noë et al., 2001). Cognitive mechanisms of decision-making aims primarily at maximizing positive outcomes and minimizing negative ones. This initial hedonism is gradually modulated by social norms, by which agents learn how to maximise their utility given the norms. The ‘hot logic’ approach provide a simpler explanation of cooperation and fairness: subjects make ‘fair’ offers in the ultimatum game because they know their offer would be rejected otherwise. Responders affective reaction to ‘unfair offers’ is in fact a reaction to the loss of an expected monetary gain: they anticipated that the proposer would comply with social norms. This claim is supported by other imaging studies showing that loss of money can be aversive, and that actual and counterfactual utility recruit the same neural resources (Delgado et al., 2006; Montague et al., 2006). This approach explains why subjects make lower offers in the dictator game (an ultimatum game in which the responder make an offer and the responder's role is entirely passive) than in the ultimatum, why, when using a computer displaying eyespots, almost twice as many participants transfer money in the dictator (Haley & Fessler, 2005), and why attractive people are offered more in the ultimatum (Solnick & Schweitzer, 1999). In every case, agents seek to maximize a complex hedonic utility function, where the reward and the losses can be monetary, emotional or social (reputation, acceptance, etc.). SR is thus seen as cooperative habits that are not repaid (Burnham & Johnson, 2005).
Quantifying the Invisible Audience in Social NetworksMichael Bernstein
Presented at CHI 2013
When you share content in an online social network, who is listening? Users have scarce information about who actually sees their content, making their audience seem invisible and difficult to estimate. However, understanding this invisible audience can impact both science and design, since perceived audiences influence content production and self-presentation online. In this paper, we combine survey and large-scale log data to examine how well users’ perceptions of their audience match their actual audience on Facebook. We find that social media users consistently underestimate their audience size for their posts, guessing that their audience is just 27% of its true size. Qualitative coding of survey responses reveals folk theories that attempt to reverse-engineer audience size using feedback and friend count, though none of these approaches are particularly accurate. We analyze audience
logs for 222,000 Facebook users’ posts over the course of one month and find that publicly visible signals — friend count, likes, and comments — vary widely and do not strongly indicate the audience of a single post. Despite the variation, users typically reach 61% of their friends each month. Together, our results begin to reveal the invisible undercurrents of audience attention and behavior in online social networks.
Week 4 slides from the class "Social Web 2.0" I taught at the University of Washington's Masters in Communication program in 2007. Most of the content is still very relevant today. Topics: Social networks, privacy.
Strengthening The Link Between Affinity and BehaviorHiebing
How can you determine your alumni’s affinity for your university? Our process takes a comprehensive look at your alumni’s feelings, perceptions, behaviors and actions, leading to an Affinity Score that can be trended over time or compared between colleges and schools. Check out Dave Florin’s presentation to members of the Council of Alumni Association Executives on how we helped the University of Wisconsin—Madison by viewing the SlideShare below.
Quantifying the Invisible Audience in Social NetworksMichael Bernstein
Presented at CHI 2013
When you share content in an online social network, who is listening? Users have scarce information about who actually sees their content, making their audience seem invisible and difficult to estimate. However, understanding this invisible audience can impact both science and design, since perceived audiences influence content production and self-presentation online. In this paper, we combine survey and large-scale log data to examine how well users’ perceptions of their audience match their actual audience on Facebook. We find that social media users consistently underestimate their audience size for their posts, guessing that their audience is just 27% of its true size. Qualitative coding of survey responses reveals folk theories that attempt to reverse-engineer audience size using feedback and friend count, though none of these approaches are particularly accurate. We analyze audience
logs for 222,000 Facebook users’ posts over the course of one month and find that publicly visible signals — friend count, likes, and comments — vary widely and do not strongly indicate the audience of a single post. Despite the variation, users typically reach 61% of their friends each month. Together, our results begin to reveal the invisible undercurrents of audience attention and behavior in online social networks.
Week 4 slides from the class "Social Web 2.0" I taught at the University of Washington's Masters in Communication program in 2007. Most of the content is still very relevant today. Topics: Social networks, privacy.
Strengthening The Link Between Affinity and BehaviorHiebing
How can you determine your alumni’s affinity for your university? Our process takes a comprehensive look at your alumni’s feelings, perceptions, behaviors and actions, leading to an Affinity Score that can be trended over time or compared between colleges and schools. Check out Dave Florin’s presentation to members of the Council of Alumni Association Executives on how we helped the University of Wisconsin—Madison by viewing the SlideShare below.
Talk about what happens when people are asked to rate other people. Are the ratings biased? Can trust and friendship be quantified? And how does all this work in the context of CouchSurfing.org, one of the largest hospitality networks?
Amanda Lenhart to the International Communications Association Annual Meeting. This presentation dives into the demographics of teen and adult social network users and looks at how youth use of social networks compares to use by adults, both in frequency, but also in purpose and behavior. 5/23/09
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Length: 30 minutes
Session Overview
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Surfing a Web of Trust: Reputation and Reciprocity on CouchSurfing.com
1. Surfing a web of trust: Reputation and Reciprocity on CouchSurfing.com Debra Lauterbach, Hung Truong, Tanuj Shah, Lada Adamic University of Michigan School of Information
The study I will tell you about today is largely descriptive, where we focused on finding and interpreting the phenomena occurring in this unique online community, and in particular in the reputation system in use on the site So today in my talk I will cover… ..wrap up with some discussion of how the best practices from CS could be applied to other online communities, & our ideas for future work
So, what is CouchSurfing? I’ll give a short introduction as many of you may not have heard of it before - it’s a popular but a niche website. And to give you an idea of how it works, I’ll give you an example. So here we are, most of us visitors in Vancouver. Instead of staying in a hotel or wherever you are staying, you could have used CouchSurfing to find a local person to stay with. You would have done a search for Vancouver, and you would have found that there are over 100 people in Vancouver to choose from.
Here are just a few of those CouchSurfing members here in Vancouver with available couches. There is no monetary exchange involved, the whole idea and mission behind the site is to meet new people and share cultural experiences. So, who would you choose, and would you do it?
For many people, their answer depends on how much they feel they can trust the other person. You want to know when you’re staying with someone that you will be safe in their house, they will not hurt you - all the basic human survival instincts. Things like the personality and interests of the other person may matter to you too as to how much you may have in common and enjoy meeting each other, but that’s all secondary. Now, it’s already known that trust is a big factor in enabling successful online transactions. Research on eBay, for instance, has shown the importance of their reputation systems for creating trust between the buyer and the seller. So you can only imagine that on sites like CouchSurfing that exist to facilitate offline transactions that trust is even more important. Much research has been done on trust and reputation systems, especially on eBay and other marketplaces, P2P networks, etc. But little research on sites like CS where people meet in person. This is kind of the new frontier of social computing - bringing the experiences into the real world, so studying how this process is faciliated on a real life community was our main motivation.
They analyzed a set of private trust ratings, not the public reputation system that we’ll be focusing on - higher trust most correlated with origin and context of relationship, and duration reciprocity was the other salient factor to the site’s success, and the other thing that a reputation system must help enable. Otherwise, not enough hosts to sustain activity
In what FORM it exists Examined reputation system from several angles
Anonymized
This is where it gets interesting…CS contains all these details on friendships, to help users judge others based on their friendships. They are also unique in that their social norms really discourage having many weak friendships like you might see on other sites, so you typically only add friends if you are actually friends in real life.
So we did a thorough analysis of the data, beginning with looking at the whole network. And quickly, what we can see is that activity is distributed the way you see on many other sites.
Direct: we think this shows that personal connections are being made because of surfing experiences
Generalized: means that I may host you, then you may not host me but may host someone else. What you get when this happens is a strongly connected component in the network, where you can hop from couch to couch between users who have surfed or hosted together to every other user in that component. In this case, this component makes up 1/3 of active users who are those who have surfed or hosted at least once
We looked at where these experiences were taking place, and you can see that there is a lot of global activity. The size of the circle is the number of experiences within that country, and the lines are experiences between people from different countries. A network as broad, global, and active- must be supported by an underlying network of trust. so, turn attn to trust system
The part of the reputation system we studied was the vouching network.
How is the vouching system being used? While as I said before only 6.8% of users have been vouches, this is a bit misleading as it is skewed by the low activity on the site Vouches given out (by those who can)
From these results, we wondered… Pressure since vouching is public
CS friends make up many of the vouched edges
Wanted to quantify the factors that contribute in one person vouching for another. We took all the connections that were vouched, along with any connections that had the potential to be vouched but were not , and built a balanced sample of 100,000 of these edges. We then ran a logistic regression model using 10 fold cross validation in order to discern which variables would most accurately predict a vouched edge
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