Notes from a research paper on micro blogging, which aid in my own research into how learning through public and private shared hubs/activity streams could be used effectively with learner management systems environments, where students would be able to choose who they learn with, how their work is share and reviewed between students, teachers and course creators.
In the future comments may appear with each extract below;
Apply retention rates to aid continued learning
In a survey of bloggers, Nardi et al.  describe different motivations for “why we blog”. Their findings indicate that blogs are used as a tool to share daily experiences, opinions and commentary. Based on their interviews, they also describe how bloggers form communities online that may support different social groups in real world. Lento et al.  examined the importance of social relationship in determining if users would remain active in a blogging tool called Wallop. A user’s retention and interest in blogging could be predicted by the comments received and continued relationship with other active members of the community. Users who are invited by people with whom they share pre-exiting social relationships tend to stay longer and active in the network. Moreover, certain communities were found to have a greater retention rate due to existence of such relationships. Mutual awareness in a social network has been found effective in discovering communities
Catergorisation of use
Based on this rough categorization, we can see that user intention can be roughly categorized into these 3 types: information sharing, information seeking, and friendship-wise relationship.
Organising shared learning hubs
A community in a network can be vaguely defined as a group of nodes more densely connected to each other than to nodes outside the group. Often communities are topical or based on shared interests.
Contributing based on common/Shared interests on gaming
We also noticed that users in this community also share with each other their personal feeling and daily life experiences in addition to comments on “gaming”. Based on our study of the communities in Twitter dataset, we observed that this is a representative community in Twitter network: people in one community have certain common interests and they also share with each other about their personal feeling and daily experience.
Studying intentions at a community level, we observe users participate in communities which share similar interests. Individuals may have different intentions for joining these communities. While some act as information providers, others are merely looking for new and interesting information. Next, we analyze aggregate trends across users spread over many communities, we can identify certain distinct themes. Often there are recurring patterns in word usages. Such patterns may be observed over a day or a week. For example Figure 11 shows the trends for the terms “friends” and “school” in the entire corpus. While school is of interest during weekdays, friends take over on the weekends.
Most posts on Twitter talk about daily routine or what people are currently doing. This is the largest and most common user of Twitter
In Twitter, since there is no direct way for people to comment or reply to their friend’s posts, early adopters started using the @ symbol followed by a username for replies. About one eighth of all posts in the collection contain a conversation and this form of communication was used by almost 21% of users in the collection.
About 13% of all the posts in the collection contain some URL in them. Due to the small character limit a URL shortening service like TinyURL9 is frequently used to make this feature feasible.
Many users report latest news or comment about current events on Twitter. Some automated users or agents post updates like weather reports and new stories from RSS feeds. This is an interesting application of Twitter that has evolved due to easy access to the developer API.
An information source is also a hub and has a large number of followers. This user may post updates on regular intervals or infrequently.
9 http://www.tinyurl.com Despite infrequent updates, certain users have a large number of followers due to the valuable nature of their updates. Some of the information sources were also found to be automated tools posting news and other useful information on Twitter.
Most relationships fall into this broad category. There are many sub-categories of friendships on Twitter. For example a user may have friends, family and co-workers on their friend or follower lists. Sometimes unfamiliar users may also add someone as a friend.
An information seeker is a person who might post rarely, but follows other users regularly.
Issues with Twitter (2007)
Multiple user intentions have led to some users feeling overwhelmed by microblogging services . Based on our analysis of user intentions, we believe that the ability to categorize friends into groups (e.g. family, co-workers) would greatly benefit the adoption of microblogging platforms. In addition features that could help facilitate conversations and sharing news would be beneficial.