Tribe (internet)

The term is related to "tribe", which traditionally refers to people closely associated in both geography and genealogy.

The tribes are divided into clans, with their own customs and cultural values that differentiate them from activities that occur in 'real life' contexts.

[citation needed] The term "tribe" originated around the time of the Greek city-states and the early formation of the Roman Empire.

The Latin term "tribus" has since been transformed to mean "A group of persons forming a community and claiming descent from a common ancestor" [3] As years passed by, the range of meanings have grown greater, for example, "Any of various systems of social organization comprising several local villages, bands, districts, lineages, or other groups and sharing a common ancestry, language, culture, and name" (Morris, 1980, p. 1369).

Morris (1980) also notes that a tribe is a "group of persons with a common occupation, interest, or habit," and "a large family.

However, the existence of social media as we know it today is due to the post-industrial society that has seen the rapid growth of personal computers, mobile phones and the Internet.

People now can collaborate, communicate, celebrate, commemorate, give their advice and share their ideas around these virtual clans that have once again redefined the social behaviour.

Not only do Twitter tribes have mutual interests,[11] but they also share potentially subconscious language features as found in the 2013 study by researchers from Royal Holloway University of London and Princeton.

Dr. John Bryden from the School of Biological Sciences at Royal Holloway states that it is possible to anticipate which community somebody is likely to belong to, with up to 80 percent accuracy.

This approach can enrich new communities detection based on word analysis in order to automatically classify people inside social networks.

The methods of identification of tribes relied heavily on algorithms and techniques from statistical physics, computational biology and network science.

Specifically, these classifiers work by collecting the Twitter feeds of all the users from the tribes that Tribefinder is training on.

To test this theory, around 250,000 users from the social networking and microblogging site Twitter were monitored in order to analyse whether the groups identified had the same language features or not.

These algorithms had to determine the word frequency inside messages between people and make a link to the groups they usually visited.

Moreover, the largest group found in the study was composed of African Americans who were using the words "nigga", "poppin", and "chillin".

These campfires tend to enable one or more of the following three tribal activities:[1] However, some brands are building their own tribes around platforms outside of these.

[22] Cooperation developed naturally over time, as it helped companies to streamline their research costs and to better answer to users' requirements.

Informal communication predominates and specialists in certain domains exchange their experience with other people within the groupware environment.

[23] Groupware can be split into three categories: communication, collaboration and coordination, depending on the level of cooperation and technology involved in the process.

One of the major drawbacks of social networks is privacy, as people tend to trust others more rapidly and send more open messages about themselves.

[34] The main advantage of Twitter is that people can gain followers quickly and share ideas and links very fast.

As a result, it can be used as a marketing tool to make someone's product visible, on condition that a big tribe of followers is created.

On the other hand, new tribes are self-sustaining and can survive without a leader, they are not necessarily dialogue based and they are long lasting.

A social network diagram displaying tribes clustered by friendship ties among a set of Facebook users
Communication between and within tribes of Twitter users clustered based on word usage. Tribes tend to communicate more within than between themselves.
The proportion of users whose topological community association is correctly predicted by the study.
An illustration of the method for predicting which community a user is embedded in.
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