Visualizing the Affective Structure of a Text Document

Brokering Private Reputation Information In Online Transaction Communities

Hugo Liu, Judith Donath, Pattie Maes MIT Media Laboratory 20 Ames St., Bldg E15 Cambridge, MA 02139 USA {hugo, donath}@media.mit.edu

ABSTRACT is so well-received that we are proud to give it in public, Online communities represent an exciting new forum for even in front of audiences. As a culture, we celebrate occa- user-to-user transactions, overcoming many of the physical sions of public praise – benedictions, eulogies, celebrations, and social boundaries associated with traditional communi- applause. It is even entrenched in language. In English, ties. To foster trust in transacting with virtual strangers, you can give someone all your love, but it is ungrammatical online transaction communities have adopted user reputa- to give them your hate, or resentment, or anger, or disgust. tion systems, such as eBay’s Feedback Forum, and friend- Whereas we are wanton to praise, we must always think ster.com’s testimonials. Because these reputation systems twice before expressing a negative opinion of someone be- are public, giving positive feedback to enhance someone’s cause it carries with it many serious ramifications. A nega- reputation becomes a form of social capital for the giver, tive statement toward someone can be perceived as an act while social pressures often suppress publicly voicing neg- of hostility and aggression. It can draw negative statements ative feedback. However there are high-cost domains in about us in retaliation. In our English vernacular, every which the dissemination of candid reputation information is synonym of criticize, (e.g. belittle, nag, rebuke) carries with particularly important, such as online dating or equities it negative connotation about the giver. Public criticism is trading. a social taboo too, and when given, is often closely scruti- In this paper, we explore how private reputation systems nized for hints of slander or libel or defamation. Many may be better suited to the dissemination of negative or Western cultures say it ok to talk down about a person’s candid reputation information in an online transaction com- idea or behavior or action, but not ok to talk down about munity. In particular, we are inspired by the activity of the person directly. In Japan, the acceptable target of criti- gossip: the exchange of sensitive information about a third cism is even further restricted. The general acceptability of party between two trusted parties. We adapt the idea to an public praise, and close social scrutiny of public criticism online transaction community, where a system called Gos- are artifacts of our human culture and evolutionary her- sipMonger brokers a trusted exchange of private reputation itage, and our social designs should reflect these realities. information between interested but not necessarily ac- It is quite paradoxical then, that rumors about someone – quainted parties. often negative or sensitive in nature – can spread through a We implement and use a social agent simulation of an on- social community like wildfire, even outpacing the dissemi- line transaction community to more formally explore how a nation of public information. Perhaps it is precisely the system like GossipMonger affects information dissemina- taboo of negative information that makes it such a social tion and decision-making. commodity. Why do rumors spread so quickly and efficiently? The exchange of negative or sensitive information Keywords about a third party between two friendly parties, or gossip, Gossip, reputation systems, social networks, ebay, game as it is commonly known, strengthens the ties and trust be- theory, agent simulations. tween the two friendly parties. In fact, cultivating social re- THE NATURE OF GOSSIP lationships may be the raison d’être of gossip. Ronald “If you don’t have anything nice to say, don’t say anything Burt’s echo hypothesis (2001) suggests that selective dis- at all!” In nearly all of the world’s cultures past and closure of information in informal conversations often only present, an offer of praise to a compatriot curries favor, is serves to reinforce predispositions and enhance shared feel- socially decorous, and often draws reciprocal praise. Praise ings in a dyad. Evolutionary sociologist Robin Dunbar ar- gues that language and social gossip coevolved as a substi- tute for physical grooming that allowed social cohesion to Copyright is held by the authors. Draft of MIT Media Laboratory Technical Report. be maintained within larger-sized clans (1998). While the Please do not cite. individual motivation to gossip may be to cultivate dyadic social relationships, an important side effect of gossip is that it facilitates tabooed information of a negative or sensitive nature to be spread efficiently through a social net- transaction communities by creating a private reputation work. system with many similar features. To establish privacy, Understanding the social dynamics of praise and criticism the identity of the gossiper is kept semi-anonymous, and in traditional communities help us to better understand and gossip is only shared with trusted and deserving parties. design for online communities. The public nature of the Gossip is not accessible by the target of gossip so that there existing reputation systems for online transaction commu- is no retaliation. In traditional communities, the main moti- nities (e.g. eBay and online dating sites) encourage praise vation of gossip is improved social relationships; however, and discourage criticism. We are exploring how private this reward model does not work with online communities, reputation systems can be designed to facilitate the ex- where users are more interested in maximizing the informa- change of negative or candid reputation information, with tion they collect to make informed decisions, than making the goal of allowing users to make more informed decisions friends with and giving away information to other users, based on balanced evidence. We adapt the concept of gos- who are often competing for the same transactions. If a sip to an online community where gossipers are not person- user’s goal is to maximize his/her own information, per- ally acquainted. In our proposal, a system called Gossip- haps users can be motivated to give gossip if doing so en- Monger brokers a private and trusted exchange of gossip. ables them to hear gossip. The approach taken in this paper This paper is organized as follows. First, we compare pub- is to look at gossip as a cooperative activity, in which giv- lic with private reputation systems. Second, we discuss a ing gossip is motivated by reciprocation. design for a system-brokered gossip reputation system In the following section, we sketch a design for a sys- called GossipMonger. Third, we describe a social agent tem-brokered gossip reputation system called GossipMon- simulation and some theoretical experiments we performed ger. to test our proposed gossip model. We conclude with more SYSTEM-BROKERED GOSSIP discussion on system-brokered gossip and a summary of One scheme for cooperative gossip is to establish direct our findings. reciprocity between two users. User X has gossip on User PUBLIC VS. PRIVATE REPUTATION SYSTEMS A and needs information on User B. User Y has gossip on In this paper, we focus on online communities in which User B and needs information on User A. However, requir- users participate in transactions, such as eBay, friendster.- ing this exact precondition severely limits the possible com, or dating sites. In these communities, users have a bandwidth of information available to Users X and Y, be- particular interest in being able to accurately assess the rep- cause many more Users may have gossip on Users A and utation of other users, as this factors into the decision to B. Instead of direct reciprocity, we design our gossip sys- transact or not to transact. The more prominent of the user tem for indirect reciprocity. Recent work in game theory reputation systems adopted by these communities, such as (cf. Nowak & Sigmund, 1998) has yielded mathematical eBay’s Feedback Forum, and friendster.com’s testimonials, models which demonstrate that cooperation can exist even are ingeniously designed to have a very public nature. Rep- if recipients have no chance to return the help to their utation feedback is visible to the target of the feedback, as helper. Instead, recipients return the help to someone else. well as to the entire community. This resembles the dynamics of anonymous markets, which As expected, these public reputation systems encourage are known for their efficiency. positive feedback while suppressing negative feedback. In our GossipMonger design, the system brokers the coop- Resnick and Zeckhauser’s empirical analysis of eBay’s eration within a gossip community, pairing information Feedback Forum (2002) found that feedback was almost al- needed with information available. The system tries to ways positive and that a large portion of negative feedback qualify and quantify the contribution of each user to this was likely suppressed. They also found a strong correlation cooperative community. Quantity measures the ratio of between buyer and seller feedback, providing evidence for gossip given versus gossip received and assures that users reciprocation, which encourages positive feedback, and re- are only getting out what they put in. The quality of a taliation, which discourages negative feedback. We say user’s gossiping is compiled from feedback given by the that these public reputations are ingenious because they are consumers of that user’s gossip. To assure that gossip designed to foster trust in transactions (Resnick et al., quality is reported fairly, how a user rates a gossiper will 2000). They may be in the best interest of promoting trust also affect their direct trust relationship. If User X favors in the community, but are not necessarily in the best inter- the gossip of User Y, then User Y’s reputation will be im- est of individual users trying to make informed decisions. proved and User Y’s gossip will be presented above the Promoting praise and discouraging criticism denies users a gossip of others to User X in future searches. If User X re- balanced picture of reputation that would help them make jects the gossip of User Y, then User Y’s reputation will be more informed decisions. hurt, but User X forfeits the opportunity to get gossip from In traditional communities, privacy assurances and the so- User Y in the future. cial rewards of gossiping facilitates the spread of negative Users who give more candid and thorough gossip are likely reputation information. We would like to create an infra- to have higher reputations. However, their candidness and structure for negative reputation information flow in online specificity might make them more vulnerable to retaliation and identification if for example the target of the gossip ties are represented by a vector of five characteristics, each hacks into the gossip community and discovers this gossip. of which is valued from 0 to 10. A seller also has project- In our design, users can specify to only trust dealings with ed-qualities, which is also a five-item vector. We can think other users of similarly high reputation, thus making better of the projected-qualities as either a willful or unwillful dis- confidentiality assurances. What we might expect to form tortion of the actual-qualities, depending on whether or not is a core user base of a high reputation that is difficult for deception is involved. We experiment with both types of casual hackers to infiltrate, because building up a suffi- distortion. The projected-qualities are what the seller mar- ciently high reputation has a high initiation cost. kets himself/herself as publicly. On the buyer’s side, each Although the system does not require the content of gossip buyer has a five-item vector of desired-qualities that repre- to be negative, we hypothesize that it is primarily negative, sents the ideal seller being sought out. sensitive, and unique information that will thrive and Combining all available information known to the buyer, spread best in this private reputation system. Information the buyer maintains, at any given point in time, interpreta- that is candid and publicly scarce will naturally be more tion-of-qualities for each seller, a five-item vector reflect- valuable commodities. Negative information tends to be ing the buyer’s most current understanding of a seller. more scarce because there is a greater risk taken in sharing Without extra information, interpretation-of-qualities will it, so it is perceived as being more valuable. Negative in- just be the seller’s projected-qualities. However, if gossip formation more so than positive information, can help a or public reputation information is available, the added in- user to avert the high cost of a bad transaction. Opportuni- formation will be used to revise the interpretation-of-quality cost is not as much a responsibility of the gossip system ties. because users will generally advertise their virtues to attract Representing Gossip transaction business. When an agent creates gossip about a seller, the gossip is a We hypothesize that system-brokered gossip will generate five-item vector. Ideal-gossip is equal to the seller’s actu- a higher bandwidth of information than traditional gossip, al-qualities. To make gossip more plausible, we model two because users can access gossip from more people than in a types of distortion: insightfulness, and reporting bias. In- traditional social network, and also because there may be sightfulness is an attribute of the agent, taking values from less echo or redundancy in gossip. Previously we said that 0.0 (not insightful) to 1.0 (very insightful). The less in- in traditional gossip, people selectively disclose based on sightful the agent is, the more distortion is added to the ide- what they think the recipient wants to hear. In system-bro- al-gossip. We model this distortion with Gaussian noise, a kered gossip, we speculate that users will want to create frequently used tool in modeling theory for adding varia- gossip to maximize marketability, not necessarily to rein- tion or noise to a system. The second type of distortion is force the predispositions of any user. reporting bias. To account for the fact that gossip is often reported with a slant, or agenda, or emphasis, a reporting In the next section, we describe a social agent simulation bias is either a five-item vector added to the ideal-gossip that we implemented to investigate the dynamics of system- (slant, e.g. [+4,+4, 0, 0, 0]), or multiplied with the ideal- brokered gossip. gossip (emphasis, e.g. [0%, 0%, 50%, 100%, 100%]). SIMULATING GOSSIP IN AN ONLINE TRANSACTION COMMUNITY Transaction Decision Process Real online transaction communities are fraught with enor- The simulation operates on rounds. Within a round, each mous complexity. It is a game of decision-making in an agent plays the role of perspective buyer for one turn. Ex- environment of imperfect information, competing partici- cept the agent whose turn it is to play the role of perspec- pants, and deception. Participants in this game must model tive buyer, every other agent plays the role of seller. The the behaviors of other participants from spotty information, goal of the perspective buyer agent is to buy from the seller and manage the risk of their decisions. whose actual-qualities are closest to the buyer’s own desired-qualities. Of course, the buyer does not know what We do not assume to be able to model the complexity of re- each seller’s actual-qualities are until there is an actual ality into our simulation, but rather, we tried to create a transaction, because this is a game of imperfect and unreli- simple formal model whose system dynamics resemble as- able knowledge! Q(t) is a measure of how good a match pects of online transaction communities. the buyer considers the seller to be, at current time t, based Simulation Design on currently known information. As shown in Eq. (1), Q is Agent Qualities calculated as the numerical deviation of the buyer’s current In eBay, transaction decisions takes into account at least interpretation-of-qualities (I) from the buyer’s desired-qual- these two factors: the value of the goods or services being ities (D), divided through by the buyer’s decision confi- offered, and the characteristics of the seller. In online dat- dence (C). Note that a smaller Q(t) maps to better buy- ing, the goods being offered and the characteristics of the er-seller compatibility. seller are one and the same. In our simulation we assume that all sellers are offering the same goods and so only the characteristics of the seller matter. A seller’s actual-quali- 5 probability or made inversely proportional to the top candi-  Di  Ii (t) date seller’s Q(t). i 1 (1) Q(t)  Creating Gossip and Giving Feedback on Gossip Quality C(t) Consider the case where there exists an infrastructure for If we set the buyer to have ideal decision confidence, so system-brokered gossip. If a transaction is made, the buyer that C(t) = 1, then we are saying that the agent trusts his/her may create gossip on the seller. For simplicity, we do not current interpretation-of-qualities the same, regardless of presently consider gossip about the buyer. Depending on the quality and quantity of information going into that deci- the configuration of the simulation, the gossip may be ide- sion. A more sophisticated model considers how the quali- al, or it may take into account insightfulness and reporting- ty of information (i.e. gossip from bad sources, gossip from bias (as aforementioned). good sources, from public reputation systems) and the Having encountered the seller’s actual-qualities, the buyer quantity of information (i.e. how much gossip, how much agent now reviews any gossip he/she received about the public reputation information) affect the agent’s confidence seller, and gives feedback on the gossip quality. Feedback in the interpretation-of-qualities. has five values, with the following meanings: During an agent’s turn, the agent calculates the Q(t) for ev- 1. Very low quality. Don’t hear from this gossiper again. ery qualifying seller, factoring in all the buyer’s current 2. Low quality. Hear less from this gossiper. knowledge into the valuations. Because in this game we 3. Indifferent. are interested in learning about how various types of infor- 4. Good quality. Hear more from this gossiper. mation can affect transaction decisions, a buyer can only transact with a seller once. This means that qualifying sell- 5. Great quality. Definitely hear from this gossiper again. ers are all agents, minus the buyer, minus the sellers the Feedback 1 and 2 will lower the gossip-reputation of the buyer has previously transacted with. The output of this gossiper, and deduct 1 or 2 points from the gossiper’s par- process is a list of qualifying sellers rank-ordered by the ticipation score. Recall that the gossiper earns a participa- Q(t) scores (ascending). tion point every time the gossip is consumed. Gossip-reputation is a score from 0 to 1.0 which tracks whether or not At this point, an agent may do research on the top n candi- an agent’s gossiping is well-received or ill-received by the dates, by discovering either public reputation information gossip community. Feedback 4 and 5 will raise the gos- on each candidate, or gossip, or neither, or both, depending siper’s gossip-reputation and increase the gossiper’s partici- on what information is available. After research is done, the pation score. Why would agents be motivated to give accu- agent is given one chance to update the I(t) interpreta- rate feedback? Because feedback improves the type of gos- tion-of-qualities for all the sellers, incorporating the new sip that will be shown to the agent in the future. At the ex- research. Modeling how the three sources of information – tremes, feedback 1 will suppress future gossip from that gossip, public reputation information, and the seller’s pro- gossiper, and feedback 5 will definitely show future gossip jected-qualities – come together into a single interpretation- from that gossiper, whenever available. of-qualities I(t) is perhaps the single most complex decision, requiring much more elaborate models of an agent’s Simulation Experiments qualities, and of gossip, and perhaps better cognitive mod- Experiment one: effects of gossip on transaction quality els of decision-making. Admittedly, we resort to a simple In this basic experiment, we generate a homogenously ran- and naïve formula for combining multiple sources of infor- dom population of agents and see how the presence or ab- mation. Eq. (2) shows that the interpretation-of-qualities is sence of system-brokered gossip affects the average quality given as the weighted sum of the three sources of informa- of transactions in the community. tion, where a, b, and c are preset constants. Eq. (3) shows The population of 100 agents is generated using Gaussian that the interpretation given by just gossip information is noise distributions. Each agent’s actual-qualities and de- the weighted sum of each gossip G heard about the seller, sired-qualities are generated using a Gaussian function weighted by the gossip-reputation R of the gossiper giving whose mean is 5, sigma is 2.5, min-value is 0, and max-val- the gossip G. ue is 10. From here on, we will abbreviate this as: gauss(5, aI (t)  bI (t)  cI (t) 2.5, [0,10]). Each agent has an insightfulness variable and a I(t)  from projected fromgossip from publicreputation (2) a  b  c truthfulness variable whose values are gauss(0.5, 0.25,

gossipers [0,1]). The truthfulness variable is used to generate a pro- R(gossiper ,t) G (seller) ject-qualities for each agent only once. The less the truth-  n gossipern n1 I fromgossip (t)  gossipers (3) fulness, the greater the Gaussian distortion of the actu-  R(gossipern ,t) al-qualities. The insightfulness variable is used to add n1 noise to gossip creation. Reporting bias is not used. After I(t) is updated for each seller, the Q(t)’s are recalcu- At this point, we are conservative and do not want to pre- lated and sellers are once-again rank-ordered. At this point, sume that the user trusts the gossip system enough for gos- the buyer will decide whether or not to transact with the top sip information to improve decision confidence, so we as- candidate seller. This decision can be based on a fixed sume ideal decision confidence, i.e., C(t) = 1. When an agent creates a rank-order of qualifying sellers, this. There are no public reputation systems, personals pro- the agent will do no research if gossip is off, and will do files are often deceptive, and there is no way for competi- gossip research on the top three candidates. The decision tors to share gained knowledge. The same population run to transact for both the gossip-on and gossip-off scenarios in the scenario with gossip-on yields a much better curve. is fixed at 20% probability, so that transactions occur at the Whereas the transaction quality starts out the same way as same rate in both cases. When obtaining gossip, the agent in the gossip-off scenario, it takes about 7 rounds for gossip will get all available gossip on the top three candidates, to idealize buyer’s decisions. This corresponds to the form- (setting no threshold criteria for the reputation of the gos- ing of a critical mass of gossip. Given that the Pr(transac- siper) or will run out of gossip participation credits trying tion) = 20%, and that the Pr(gossip|transaction) = 100%, to do so. the expected number of transactions after 7 rounds is 140. If a transaction is completed, agents will create one gossip Considering that gauss(5, 2.5, [0, 10]) dictates the likely with 100% probability. And if applicable, will give feed- histogram of which agents were transacted with, 140 trans- back on gossip with 100% probability. Feedback is decid- actions marks the milestone that at least one gossip is prob- ed based on the deviation of the gossip from the actu- ably available for each possible seller. We suggest that this al-qualities observed. milestone is responsible for the minimization of fluctuations in the curve starting at round 8. Considering that the The population is closed, with no emigration or immigra- system is performing quite well at transaction qualities of tion, and the simulation is run for 100 rounds. At the end under 5, the thing that could easily cause a fluctuation is a of each round, the average quality of transactions from that couple of very bad transactions in that round. We argue round is calculated. We chose the least implementa- that having one gossip on each seller averts these bad trans- tion-specific metric for transaction quality possible. actions, and thus, averts fluctuations in the curve. A single transaction’s quality is the actual seller’s rank in Let us illustrate this with an example. Suppose that 25% of the rank-order of the actual compatibility of each potential the population of sellers are rogues, and suppose that hear- seller with the buyer. The deviations between the actu- ing just one gossip about a rogue is enough to knock that al-qualities of each potential seller and the desired-qualities seller out of the top ranks. Suppose the buyer always specified by the buyer are compiled and rank-ordered (as- chooses the top rank to transact with. If the buyer does no cending). The top of the list represents the potential seller research, he/she has a ¼ chance of transacting with a rogue who has the best actual compatibility with the buyer. The and incurring very high cost. If the buyer researches the actual seller is located in this list, and his/her rank in this top 1 candidate, the chance of a rogue transaction falls to list is the measure of transaction quality. For instance, if 1/16. Researching the top 2 candidates reduces the rogue the rank of the actual seller was 2, then that seller was the threat to 1/64, and so on… the rogue threat decreases ex- second most ideal decision that the buyer could have possi- ponentially as the number of sellers researched increases in bly made. Figure 1 summarizes the results of the simula- integer steps! tion experiment. Note that a transaction quality of 1 represents ideal transaction quality, while higher scores repre- If we assume that n (as in the top n candidates to be re- sent increasingly less ideal transaction decisions. searched) is a small fraction of the total candidate seller population, then gossip’s main steady-state benefit is to help agents avoid the high costs of bad transactions. As the rounds progress, the transaction quality of the gossip-on curve gets steadily worse, and fluctuations begin to return more dramatically. Whereas initially the saturation of gossip helped agents to avoid rogue transactions, over the rounds, there became fewer and fewer clear-cut rogue transactions that could be avoided. And those early rogue sellers who were “averted” earlier by moving them down the list, have worked their ways back up the list. And this time, there cannot be avoided any longer. The steady-state benefits of gossip are diminishing, ushering in the return of Fig. 1. Experiment one simulation results. fluctuations (albeit, more moderate ones). Without a gossip reputation system, agents only have the Experiment two: using gossip to identify and ostracize projected-qualities of potential sellers to go on. In our rogues Gaussian population, some sellers project themselves hon- In this experiment, we examine the notion of rogues and estly while equally many do not. Decisions based on unre- how gossip can help buyers to identify and avert transac- liable projections cause the without-gossip curve to hover tions with these rogues. For the purposes of this experi- with zero slope, at an average transaction quality of 12, and ment, we define a rogue as an agent whose actual-qualities to fluctuate dramatically. This is as expected. The reality are undesirable to the norm. The rogue is deceptive and of most online dating sites, for example, are not far from projects projected-qualities that seemingly blend in with the norm. Unaware buyers with transact with rogue sellers will mation. In this paper, we have proposed a design for a pri- face a high cost. There are many good examples of rogues vate reputation system, based on the idea of gossip. Like in online transaction communities, like a fraudulent mer- gossip, an environment of privacy and trust is created for chant on eBay, or a predator on a dating site. information exchange. Gossipers are kept anonymous, yet We implement rogues as having actual-qualities of the system remembers and tracks familiar gossipers and gauss(1.5, 0.5 [0, 3]) and projected-qualities of gauss(7.5, helps to implicitly manage the trust relationship with famil- 1, [5, 10]). Non-rogue “normal” people have actual-quali- iar gossipers. Trust can be controlled by the user, who may ties of gauss(7.5, 1, [5, 10]). They project personalities specify that he/she only wants to make his/her gossip view- with Gaussian noise based on a Gaussian truthfulness val- able to other users of sufficiently high reputation or those ue, just as in experiment one. with established rapport. The motivation for gossiping is different from the traditional motivation of social relation- All other agent qualities are the same as in experiment one. ship grooming, because users do not want to befriend We generate a population of 400 agents, 20% of which are strangers who are in competition with them. The motiva- rogues. The simulation is run with the gossip system run- tion has to be acquiring information, because it is to a ning, and also without. After each round, we tabulate the user’s advantage to command a broad range of information percentage of rogues that just sold in a transaction, and to support decision-making. With this in mind, we de- compare this to the percentage of normal people who just signed a system-brokered gossip system that establishes a sold in a transaction. The results are shown in Figure 2. market for gossip. Participation level and reputation for gossip quality are things that users can strive for. The indirect reciprocity model rewards people who create helpful gossip with the privilege to obtain the gossip of others. This paper implements a system-brokered gossip system design in an agent simulation of an online transaction community. The multi-criteria decision-making aspects of the problem are highly complex, so we tried to stay away from complicated cognitive models of decision; but rather, we design a simpler formal model for the simulation that we hope to see many parallels in. The game of decision-making in an environment requires the interpretation of imper- Fig. 1. Experiment two simulation results. fect information, coping with competing participants, and These results show that without gossip, these rogues con- coping with deception, and the behaviors of other partici- tinue to sell as often as normal people. But with gossip, pants. The game theory behind the various decision pro- buyers stop transacting with rogues after just 6 rounds. cesses is also quite complicated. What effectively happens is that gossip exposes rogues, Despite these challenges, we were able to do some theoreti- causing them to be ostracized from the community. This is cal experiments illustrating the system dynamic with and because the power of negative reputation lies in the princi- without gossiping activity. It is clear from our results that ple of exclusion (cf. (Yamagichi & Matsuda, 2002)). gossiping’s main steady-state benefit is not to lower oppor- FURTHER DISCUSSION tunity cost, but rather, it is to help agents avoid the high In traditional communities, gossip is often the perfect quick costs of bad transactions. Looking at a population with a conduit for information that is not publicly known, either rogue minority, we also show that system-brokered gossip because it is negative, of a sensitive nature, or discloses se- can quickly and efficiently ostracize rogue agents. crets. It spreads because the act of telling gossip to one of Our contributory summary. We have motivated the need our friends is a social grooming behavior, helping to for a private reputation system that can conduit negative, strength a dyadic tie between the giver and recipient. sensitive, and secretive information quickly and in a trusted When we gossip, we feel freer to pass certain information and indirectly cooperative way through an online transac- along that is otherwise taboo to spread publicly. We are tion community. GossipMonger is a design we propose, willing to gossip because there is some level of privacy and which realizes a private reputation system as a system-bro- trust that we assume will shield us from retaliation if the kered gossip system that is semi-anonymous, trustworthy, target of the gossip should ever trace the gossip back to its and a valuable source of information. We created an agent origins. Publicly spread information filters out information simulation of our proposed approach and used game theory that is not socially decorous to spread, so it is skewed posi- to interpret the results of our simulation experiments. We tive, and less informative. concluded from the simulation behavior that gossiping’s In the online transaction communities like eBay and online main benefit is to help agents avoid the high costs of bad dating sites that are of interest to us, there exists public rep- transactions and to enable the quick identification and os- utation systems to spread skewed positive information, but tracization of rogue agents. Gossiping by itself does less to there are no conduits for negative, sensitive, secretive infor- address the opportunity cost problem where great transactions are missed. Social collaborative filtering may be better suited to solving the opportunity cost problem because it is widely used as a predictive recommender system (Shardanand & Maes, 1995). 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