The Application of “Long Tail” Effect in Social Media

The Application of “Long Tail” Effect in Social Media

LUO Nan 1155081715

The “long tail” theory was first proposed by Anderson (2004, 2006), which indicates that the total values of products in the tail were worth as much as the sales value of those in hits. In Sonia’s (2014) study of application of long tail effect to online news market, she analyzed three forces of long tail economy which included more producers and products, democratization of distribution, as well as connecting supply and demand through filters. Internet just provide a perfect platform where numerous products can exist, also, websites play well in the role of distributor and filters. Thus, based on Sonia’s study we can know that long tail theory was much more applicable when applying to online economy, for Internet provides a much wider market and cut cost at every part of the process of selling a product. Amazon and other online businesses are successful examples of applying the long tail theory to online economy.

While Internet provides the possibility of a large market for products in the “long tail”, the market also need an efficient approach to help people select emphases from numerous products, and this is the necessity for applying the theory nowadays. Especially on social media platform which sells information on, we are surrounded by all kinds of information, and beyond the hot topics set by public media, how can other information reach their target publics? To solve the problem, we can see that many social media have applied the long tail theory and got great success.

Taking Sina Weibo as an example, it mainly takes four approaches to maximize values of niche content. First, they classified information to niche market according to content, and create the hash tag (“#”) for them. When users are seeking for information, they can choose a tag and collect all the information with hash tag. With this method, information becomes more concentrated, and the seeking-information process becomes more efficient. 

 Weibo uses hash tag

Besides, the hash tag is not consolidated and contents are always updating, so some very niche content can be spread to their target users. Second, Weibo also has the function of recommendation. When your friends make comments, or click the like button of a tweet, it may appear to your home page as recommendation, and this is also useful to reinforce the social interaction by enhancing connection between users. However, the limitation is that only relatively hot information would be recommended. If the system recommended more less hot topic randomly, the long tail theory may play the role better. The third approach is promoting information reading model. Even though Weibo started from the short blog, it gradually encourages people to write long blogs with pictures, and it also make the picture bigger and the layout more suitable for reading. This method will attract people who are truly interested in the content to read it in detail and further to reduce the volume of tweets to enhance the possibility of long tail effect. The last one is that Weibo added some third party application into itself to diffluence information, such as “微招聘”“微公益”“投票”and “点评”, etc. People who are caring about some information related to these topics can click into the sub-column to read important news and information from other users. However, it is also a limited function because it doesn’t solve the problem of diffluence information and recommend them effectively.

sub-columns of Weibo

Additionally, there is another method adopted by Facebook that it can recommend information according to how users use Facebook. If the user log in Facebook frequently, for example three or four times a day, the information flow on users’ homepage will include more new things, but if he/she use Facebook several days a time, it will only show some important information in those days. Besides, new technologies of digital time, such as big data technology, provide the possibility for social media to analyze what people like through their selection and to recommend information matching their preference.

In all, there are four kinds of obvious adoptions and successful tries of long tail theory in online social media: niche content, recommendation, improving reading model, and third party application. And they all try to make information especially those in tail reach their own audiences.

Reference:

1、J. Sonia Huang, & Wei-Ching Wang. Application of the Long Tail Economy to the Online News Market. Journal of Media Economics, 27:3, 158-176.

2、黄孝章. 新媒体发展中的长尾效应探析. 北京印刷学院学报, 2008年2月.

3、赵云泽. 长尾效应下自媒体营销方式探析. 新南记者,  2016年第九期.