page_title: Tribe (internet)
section_title: Results and discussion
hierarchical_section_title: Tribe (internet) / An in-depth look into the research / Results and discussion
caption_attribution_description: English: Proportion of users whose community is correctly predicted. The proportion of users whose topological community association is correctly predicted by analysing a random sample of words, as a function of the number of words sampled. Results are presented for both the modularity maximisation partition (users from only English-speaking communities are shown as red pluses, users from any community as blue circles), and the Map Equation partition (English-speaking communities are shown as black crosses, all communities are as blue squares). For each data point, 5,000 users were tested. Standard error of each point is &lt;1%
context_page_description: The term tribe or digital tribe is used as a slang term for an unofficial community or organization of people who share a common interest, and usually who are loosely affiliated with each other through social media or other Internet mechanisms. The term is related to "tribe", which traditionally refers to people closely associated in both geography and genealogy. Nowadays, it looks more like a virtual community or a personal network and it is often called global digital tribe. Most anthropologists agree that a tribe is a society that practices its own customs and culture, and that these define the tribe. The tribes are divided into clans, with their own customs and cultural values that differentiate them from activities that occur in 'real life' contexts. People feel more inclined to share and defend their ideas on social networks than they would dare to say to someone face to face. For example, it would be ridiculous to 'poke' someone in real life.
context_section_description: The problem of detecting the community features is one of the main issues in the study of networking systems. Social networks naturally tend to divide themselves into communities or modules. However, some world networks are too big so they must be simplified before information can be extracted. As a result, an effective way of dealing with this drawback for smaller communities is by using modularity algorithms in order to partition users into even smaller groups. For larger ones, a more efficient algorithm called 'map equation' decomposes a network into modules by optimally compressing a description of information flows on the network. Each community was therefore characterised according to the words they used the most, based on a ranking algorithm. To determine the significance of word usage differences, word endings and word lengths were also measured and showed that the pattern found was the correct one. Moreover, these studies also helped in predicting community membership of users, by comparing their own word frequencies with community word usage. This helped in forecasting which community a certain user is going to access based on the words that they are using.
caption: The proportion of users whose topological community association is correctly predicted by the study.
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