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Online “community” is not exactly same as the real- life community. Real- life community means a particular group of people who are all alike in some way. While it goes online, “the term community relates to group with a communal preference in music, movies or books” (Dijck, 2009: 44) which can also be called “taste community”. Like people who all take “extremely lying down” photos or believe in vampires. Those “communities” are definitely too informal to become a community existing in society.

But in UGC online system, those “communities” are popular and increasing, especially after the ranking tactics are introduced in. Ranking tactics, in this case, includes a series of strategies which can promote popular favorites. Such strategies, in a video sharing website, are tagging, commenting, sharing, rating etc.

YouTube Recommendation Page. From applicationtalk.com.

Tagging, the most important ranking tactic, stimulates the online “communities” to form. After a video is generated, the user will tag it with various short descriptions to get more attention. “Shared free-form tags… (are) generating annotations for objects with a minimum amount of effort” ( Choudhury, etc, 2008: 747). If any of those tags are similar to existent tags by other users, the system will automatically combine them together. And the communication between those two like-minded users is provided. Thus more similar tags will bring more users together to form a “community”.

After the “communities” are formed, ranking tactics like tagging also help to get more users involved. When a user search some tag, the systems will automatically provide a series of the similarly-tagged videos based on the popularity (rating) of each. And users will then either share them or comment on them to join the community. Also, when a user selects a tag or a person, some tasks “that apply for personalization in a collaboratively tagged database” will be shown. And some “common tasks (will also show) like: suggesting tags when interesting content has been found, retrieving relevant content by using tags as queries, getting help from experts on a certain topic, making new friends, and using your friends to discover relevant content” (Clements, etc, 2010: 21: 2). And obviously each of them would lead the user to join a “community” which suits him/ her. Besides, “the profile created by a user’s annotations can be used effectively to adapt the ranking to personal preferences” (Clements, etc, 2010: 21: 2). For example On YouTube, after users have created their own profiles, the site will automatically generate a personalized “recommended videos” for them. Those recommendations may not be the most popular videos for majority but may become their favorites. Thus the users will be more likely to get involved in the online “communities”.

Other ranking features like sharing and subscribing also contribute on integrating same-interested users. Because the ranking of a video strongly depends on the two features. The video providers will normally encourage viewers to share it or subscribe their personal channels. If users share videos of YouTube on Facebook, then their friends might be also interested in them. And the subscription includes the subscribers into the “community” created by founders already.

A taxonomy of the tasks in a social content system that apply for personalization. Level 1 shows the three tasks that apply to users that just enter the system (T1-T3). Level 2 indicates the tasks that arise after the user has selected either an item, tag, or another user (T4-T12). (Clements, etc, 2010: 21: 2)

Reference:

Jose van Dijck, ‘Users Like You? Theorizing Agency in User-Generated Content’, Media, Culture and Society 31 (2009): 41-58.

S. Choudhury, J.G. Breslin, and A. Passant (2008), ‘Enrichment and Ranking of the YouTube Tag Space and Integration with the Linked Data Cloud’, in A. Bernstein et al. (Eds.): ISWC 2009, LNCS 5823, pp. 747–762, Berlin: Springer-Verlag Berlin Heidelberg 2009.

Maarten Clements, Argen P. de Vries, Marce J. T. Reinders (2010), ‘The Task-Dependent Effect of Tags and Ratings on Social Media Access’ in M. Clements et al, ACM Transactions on Information Systems, Vol. 28, No. 4, Article 21 (2010 Nov.)

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