1. User Joining Behavior in Online Forums
Xiaolin Shi, Jun Zhu, Rui Cai, Lei Zhang
Univ. of Michigan, Tsinghua Univ., Microsoft Research Asia

2. Motivations A process of information diffusion and epidemics
Building social computing systems: providing valuable insights to improve user experience
Difference between online forums and many other social media: relative randomness and lesser commitment of structural relationships.
From the point of view of social networks, user grouping behavior is a process of information diffusion.
From the point of application, this study would help with providing valuable insights to improve user experiences in building social computer systems.
Moreover, online forums are a special type of social networks because of their relatively random and looser structural relationships.

3. Community posts rating
Here is an example. Digg is a platform for people sharing online news, images, etc. it is a typical news forum.
It has communities of great diversity.
Users post in articles or comments in communities according to the topics.
Here are the ratings of the posts. Ratings are scored by users, based on how much they like the articles.

4. thread
reply
This is another example, which is a thread in Apple forum. i.e. one user starts a topic, and others leave comments.

5. Definitions Communities: explicitly pre-defined
Relationships: temporary, require little effort
User-community relationship or user joining community: posting
User-user relationship: reply
Now we look at some definitions in this study.
From the definitions, we see that relationships in online forums are very weak. Unlike many other social networks, people do not have to pay much effort to build the reply relationship with other people, or pay much commitment to the community or affiliation they belong to.
However, we will see later that, despite of such weak relationship, they still present strong regularities.

6. Illustration of users joining communities
Time t:
post
Community A
Alice
reply
Community B
Bob

7. Illustration of users joining communities
Time t+1:
post
Community A
Alice
In this procedure, there are many possible reasons for Bob to join community B. for example, one of them is that Bob is influenced by his prior reply neighbor, Alice, to join community B. If this is true, then we say that there is information diffusion from Alice to Bob.
In this work, we are investigating the possible reasons causing users joining communities, and the patterns of user joining behavior.
reply
Community B
Bob

8. Three central questions8
Factors in online forums that influence people’s behavior in joining communities
Relationships between these factors
Differences of user grouping behavior in forums of different types (such as news forums versus technology forums)
We will see how to answer these three questions in our study one by one. 8

9. Description of datasets
Forum
Digg
Apple
Google Earth
Honda
Type
News
Technology
Time
User
212,635
349,066
231,976
45,718
Community 50
331 54 63
Edge
1,185,167
451,338
345,038
122,946

10. The first question
What are the factors in online forums that influence people’s behavior in joining communities?

11. Feature factors
Examine the relationship between the features at time t and a user joining a community at time t+1
Features associated with users: number of reply neighbors in the community
Features associated with communities
Community size: popularity of information
Average rating of top posts: authority or interestingness of information
Features at t
Join at t+1?
Features at t
We extract several features from the forum data, and examine…

12. Diffusion curves 1: reply relationship
(a) Digg (b) Apple
The probability of a user joining a community at time t as a function of the number of reply neighbors who are active in that community at time t-1
Observations:
Exhibiting law of diminishing returns – curves increase fast at the beginning, but more and more slowly towards the end.
“S-shaped” behavior at k = 0, 1, 2
(c) Google Earth (d) Honda
These figures show the relationship between a user join a community and the number of users that user have reply relationship and are active in that community at the previous time snapshot.
In social networks, we call them diffusion curves.

13. Reply relationship vs. stronger relationships
LiveJournal
Digg
As we have said before, reply relationship is a type of very weak relationship in social networks. However, we find that this weak relationship exhibit similar patterns in its diffusion curves as those of strong relationships. Here is a diffusion curve of real friendship in livejournal.
The weak relationship of reply exhibit similar patterns in its diffusion curves as those of strong relationships, such as real friendship and co-authorship in [Backstrom, 2006]

14. Diffusion curves 2: community sizes
(a) Digg (b) Apple
The probability of a user joining a community at time t as a function of the normalized community size at time t-1.
The growth of the joining probability is sub-linear or linear with respect to the normalized community size.
(c) Google Earth (d) Honda
The diffusion curves of community sizes are quite different from those of reply friends.

15. Diffusion curves 3: average ratings of top posts
The probability of a user joining a community at time t as a function of the average rating of the top 10% high rating posts in the community at time t-1. The difference may be due to the different interfaces of the systems and people’s purposes in joining two types of forums.
(a) Digg (b) Google Earth
Only Digg and Google Earth have rating systems.
Digg: critical mass
Google earth: doesn’t constantly increase
This may be due to the different interface of these two forums and the purposes of users joining communities.
In GE, people are mainly seeking answers to their particular questions that may be only related to the topics in limited communities, so although the scores of the posts in the communities matter, they do not have much difference after a threshold. However, the purpose people have for joining communities in Digg are more diverse. And the front page of Digg enables user to read interesting topics without being aware of the communities they are in.

16. The second question
What are the relationships between these factors: which ones are more effective in predicting the user joining behavior, and which ones carry supplementary information?

17. Bipartite Markov Random Fields
A bipartite MRF model with N communities and M users at time t is an instance of the connections between users and communities at time t. The dashed edges are observed evidence.
In order to answer this question, we build BMR models to study the prediction performances of these features.
In this figure, the white nodes on the left are communities, and the black ones on the right are users. Based on the features we observed either associated with communities or users, we want to predict the existence of the edges between users and communities.

18. Evaluation results
Evaluate the performance of prediction by measuring the areas under ROC curves
poor  excellent
0.5
1.0
BiMRF Models
Digg
Google Earth
Apple
Honda
# of neighbors of reply (I)
0.718
0.520
0.522
0.640
Community Size (II)
0.700
0.860
0.912
0.833
Avg. rating of top posts (III)
0.639
0.760 NA
(I) + (II)
0.800
0.862
0.913
0.853
(I) + (III)
0.774
0.765
(II) + (III)
0.708
0.882
(I) + (II) + (III)
0.804
0.883
It is a value ranging from 0.5 to 1.
By comparing the evaluation results of these features, we find that the feature associated with user is…, associated with communities are … difference between news forums and technology forums.
After combining them together… they carry supplementary information.
At this point, we are not only able to answer the second questions, which is…, we are also able to answer the third question, that is… In fact, we have observed more differences of user joining behavior in different types of forums.

19. Conclusions
Users’ joining behavior in online forums has strong regularities – in contrast to the little effort and commitment they have:
Reply relationship has similar diffusion curves as other strong social ties
Impact by features associated with communities
Relationships between the features
Different effects of feature associated with users versus features associated with communities
Carry supplementary information
Differences of user joining behavior in news forums and technology forums.

20. Thank you!
Questions?