- Pattern Languages
- Liberating Voices (English)
- Liberating Voices (other languages)
- Liberating Voices (Arabic)
- Liberating Voices (Chinese)
- Liberating Voices (French)
- Liberating Voices (German)
- Liberating Voices (Greek)
- Liberating Voices (Hebrew)
- Liberating Voices (Italian)
- Liberating Voices (Korean)
- Liberating Voices (Portuguese)
- Liberating Voices (Russian)
- Liberating Voices (Serbian)
- Liberating Voices (Spanish)
- Liberating Voices (Swahili)
- Civic Ignorance (English)
- Digital Resources
Catalyzing Collective Action in Social Cyberspaces
Pattern number within this pattern set:36
As online communities and groups grow they often sucumb to their noise becoming all but unuseable to their participants. Tools to filter the noise are critical but often rely on crude evaluations of content. An alternative approach considers the social histories of participants as an indicator of value, allowing the signal to rise above the background noise.
The emergence of online civil society requires that tools support the development of public discussion spaces that resist the corrosive influence of extreme participants who often blot out all moderate discussion. In current systems, tiny minorities often have more power than majorities. The goal is not to silence minorities but to allow productive contributors to be identified and the content highlighted so that disruption is minimal.
The challeneges of identifying "value" are clear. One technique is to mask issues of content and focus, instead, on the behavioral patterns likely to indicate commitment and tenure in the group.
Behavior histories are in many ways superior to "reputation systems" that rely on the active contribution of user opinions and impressions of other users. Opions are often not expressed when they are moderate, leaving only the most extreme participant's views visible. In contrast, behavioral histories in discussion spaces highlight the ways people have behaved and others have behaved towards them. The result is a more objective and less invasive method for developing data that can illuminate content that is likely to be of high value with out in any way limiting the speech or contributions of marginal members.