Karma system: Difference between revisions
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Some of the first applications were used on the early online discussion forum site, [[The Well]]. This was a computer-assisted means of measuring quality of posts. In each rating interval, a random sample of users were selected to do basic quality assessments of individual posts. Over time, the poster's reputation in the community was established. | Some of the first applications were used on the early online discussion forum site, [[The Well]]. This was a computer-assisted means of measuring quality of posts. In each rating interval, a random sample of users were selected to do basic quality assessments of individual posts. Over time, the poster's reputation in the community was established. | ||
==Discussion forums== | ==Discussion forums== | ||
As with the Well, formal ratings of users, statistically controlled to avoid bias, is one approach. While the raters were public, their ratings were not. | As with the Well, formal ratings of users, statistically controlled to avoid bias, is one approach. While the raters were public, their ratings were not. Regresssion-based statistical approaches have been used to assess the community's appreciation of posts. <ref>{{citation | ||
| url = http://faculty.cs.tamu.edu/caverlee/pubs/hsu09socialcom.pdf | |||
| title = Ranking Comments on the Social Web | |||
| author = Chiao-Fang Hsu, Elham Khabiri, and James Caverlee | |||
| publisher = Department of Computer Science and Engineering, Texas A&M University }}</ref> | |||
With growing reputation, the user often gets either quantitatively more privilege, or qualitatively new privileges. Drops in reputation can reduce privilege. One common problem is that a minority of new users may flood the site with posts that variously might be spam, or simply are written without a good understanding of the culture. Typical karma systems create a dynamic quota, for new users, of volume or number of posts, a quota that moves to removing limits as reputation builds. It should be simple for users to request waivers from administrators, or, as here, Constables, who can use human judgment. | With growing reputation, the user often gets either quantitatively more privilege, or qualitatively new privileges. Drops in reputation can reduce privilege. One common problem is that a minority of new users may flood the site with posts that variously might be spam, or simply are written without a good understanding of the culture. Typical karma systems create a dynamic quota, for new users, of volume or number of posts, a quota that moves to removing limits as reputation builds. It should be simple for users to request waivers from administrators, or, as here, Constables, who can use human judgment. | ||
==References== | ==References== | ||
{{reflist}} | {{reflist|2}} |
Revision as of 11:44, 29 July 2010
A Karma system, also known as a web reputation system is, at its most general, an automated or semiautomated method of characterizing online user behavior in a way useful to the particular venue. The term "reputation system" is broader than World Wide Web context alone, and draws from the distributed trust model used by Pretty Good Privacy to assess the reliability of cryptographic keys, and, indirectly, the reputation of their user. Closely related mechanisms may be used to detect spammers in electronic mail systems.
"There are three types of lies - lies, damn lies, and facts found on the Web." — Dr. Tim Finin, paraphrasing the well known quotation by Benjamin Disraeli on Statistics[1]
Farmer and Glass have described some general terms for characterizing karma systems. First, a system may be public, with its findings immediately available to all or most users, or private, available only to administrators who control others' access. In all cases, the karma score is context-specific and should not be generalized to things beyond its capability, within the site or to other venues. They mention, as one example of overuse of a reputation, the FICO score for creditworthiness widely used in the United States, which has been controversially applied to such things as risk in granting insurance. More relevant to web use are attempts to apply eBay seller scores to other contexts, but
The eBay Feedback score reflects only the transaction worthiness of a specific account, and it does so only for particular products bought or sold on eBay. The user behind that identity may in fact steal candy from babies, cheat at online poker, and fail to pay his credit card bills.[2]
Some of the first applications were used on the early online discussion forum site, The Well. This was a computer-assisted means of measuring quality of posts. In each rating interval, a random sample of users were selected to do basic quality assessments of individual posts. Over time, the poster's reputation in the community was established.
Discussion forums
As with the Well, formal ratings of users, statistically controlled to avoid bias, is one approach. While the raters were public, their ratings were not. Regresssion-based statistical approaches have been used to assess the community's appreciation of posts. [3]
With growing reputation, the user often gets either quantitatively more privilege, or qualitatively new privileges. Drops in reputation can reduce privilege. One common problem is that a minority of new users may flood the site with posts that variously might be spam, or simply are written without a good understanding of the culture. Typical karma systems create a dynamic quota, for new users, of volume or number of posts, a quota that moves to removing limits as reputation builds. It should be simple for users to request waivers from administrators, or, as here, Constables, who can use human judgment.
References
- ↑ Models of Trust for the Web (MTW'06), 15th International World Wide Web Conference (WWW2006), May 22-26, 2006}
- ↑ Randy Farmer and Bryce Glass, On Karma: Top-line Lessons on User Reputation Design, Building Web Reputation Systems: The Blog
- ↑ Chiao-Fang Hsu, Elham Khabiri, and James Caverlee, Ranking Comments on the Social Web, Department of Computer Science and Engineering, Texas A&M University