Post 4: Social differentiation and Status Hierarchies

Review

In lecture 7-9, we learnt some methods of social network analysis, and many of them are related to graph & matrix theory. I have learnt some graph theories in high school, so I was interested in these measurements.

A social network can be represented as a graph, in which each node represents a person in the network. For ease of computation, this graph can be also represented as a matrix. Some characteristic measures in graph include nodal degree, density, geodesics, cut-point, bridge, clique, centrality and prestige etc. These different values show different characteristics of a social network, and some show the extent of importance of each node in the graph. For example, the degree centrality in a graph measures the links a node connects to others (divided into in-degree and out-degree in directional graphs), and closeness centrality measures the average geodesic distance from one node to the others.

The different measures are inspiring to me, which can conclude a complicated social network diagram into a few matrices and numbers!

When eigenrumor was being discussed, I was especially interested in the differences in ranks of blogs, and hence want to know more about social differentiation.

Topic: Social differentiation and Status Hierarchies

Phenomenon

As discussed in class, there are always some “core” people in a group, who always contribute a lot to discussion and also receive a lot of attention. In contrast, there are a lot more people discuss less and rarely noticed by others. In Hong Kong, the latter are called CD-ROM (because they are so-called “read-only”); in mainland, they are called people who read but never reply (看帖不回帖), which is usually a pejorative term.

Actually, in many social networks I have been, I am an “outsider”. But in a few social networks, I have achieved the “core” position, from my point of view. One of them is an online game which is not well known, so the players’ community is relatively small. Since I was once very active in the discussion board, my in-game-name was known by a lot of people even though I had never played with them. Below are some examples taken from Baidu Tieba, shows that there are some people recognized me and asked me questions even unrelated to the theme of the post.

Formation

Since I was once a well-known person in a community, I can tell how to become such a person.

The key is to first give out more significant and useful information. From my experience and observation, people tend to respond to those who they know well, rather than a stranger. Therefore, it is common that a post from a new user just received few replies. Hence, in a social community, as long as you give out enough significant and useful information, you will be noticed by others gradually, and then get back more response.

If we use in-degree and out-degree to measure a person’s participation in an (online) community, in-degree represents messages/replies one receives from others, and out-degree represents messages/information on gives out to others. When one increases his out-degree to a significant amount, his in-degree will increase and increase gradually, and subsequently he may be the one in the center. For the others, since they devote less effort in the participation, their in-degree and out-degree will keep at a low level, and maybe they will keep in a coterie which just includes a few people and only discuss with themselves, or will be isolated by the community.

Therefore, I think in-degree and out-degree are related, and they will be about the same eventually, as they show the activeness of a person towards this community. Take our blog circle as an example! I collected 2 sociograms below, one of which was taken from lecture notes in week 7, and the other was just grabbed from Guan Hao’s work [1]. In the first sociogram, we can see there are people who gave out a lot of comments but received less (e.g. Ling Luo), but people who receive a lot of comments usually gave out a lot (e.g. Su Jing). Now let’s look at these people in the second diagram. Since the diagram is already hard to read, I just post the data here: Ling Luo in-17 out-20, Su Jing in-25 out-24, and some new centers: Silu Li in-25 out-32, youniting in-10 out-21. We can see the “old centers” are already with almost same in-degree and out-degree, but “new centers” are with out-degree more than in-degree. It is due to time lag for others to know about them. I believe that when time passes, the in-degree and out-degree will become similar!

Impact

When a person is over well-known, his fans will wrongly believe that every word he says is correct. Nowadays, after a famous people post something on twitter or weibo, most of the comments are positive, and we can seldom see a comment doubting him. For example, below is a weibo post from Kai-Fu Lee(李開復)[2], and most of the comments are positive. Only one doubted him, “then why you argued with others?” Such a celebrity effect leads people tend to agree with their “idols”. We need to think independently, not according to the celebrity; especially he is not the authority in that area!

Reference:

[1] http://blogosphere.id3.cc/sociogram
[2] http://weibo.com/1197161814/z6PKso3qs