Project: PTT is the most popular Bulletin Board System (BBS) in Taiwan and the largest BBS in the Chinese-speaking community. There are many kinds of boards in PTT. One of them is ShuangHe, which the people, including me, who live in this location in Taipei provide relevant information about this place. I am planning to use social network analysis to measure the size, density, clique, and centralization of the board with UCINet or Pajek, the social network analysis software. I will collect the data manually, including members of registration and posts during six or twelve months, and then cluster and analyze them to find out the development of the board, such as evolution of topics and members. Comments: Good. At least you describe an approach to conduct the study. Seminar: I have chosen the four papers, and am planning to read them. These papers mention the concept of links to analyze the social network. I am planning to use the concept to analyze the PTT, the largest BBS in Taiwan. Now I am still thinking about the dimensions I can analyze of evlution of the topic and relationship. Papers: 1. Online Supportive Interactions: Using a Network Approach to Examine Communication Patterns within a Psychosis Social Support Group in Taiwan. Hui-Jung Chang, 2009, Journal of the American Society for Information Science and Technology, 60(7):1540-1518. Abstract: A network approach was used to determine the overall supportive communication patterns constructed within the PTT psychosis support group in Taiwan, the largest bulletin board system in the Chinese-speaking world. The full sequences of supportive interactions were observed over a -year period from February 2004 to July 2006. The results indicated that the most exchanged support types were information and network links. All types of supportive communication networks were relatively sparse, yet small groups of cliques with different provision of support types formed within the psychosis group. Most of the online supportive interactions exchanged at dyadic and triadic levels. The overall supportive network was highly centralized. The overall findings with implications for future studies were discussed. 2.Expressing Social Relationships on the Blog through Links and Comments. Noor Ali-Hasan and Lada A. Adamic, 2007, In Proceedings of the 1st Annual Meeting of the North American Chapter of the Association for Computational Linguistics. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.4956 Abstract: Blogs, regularly updated online journals, allow people to quickly and easily create and share online content. Most bloggers write about their everyday lives and generally have a small audience of regular readers. Readers interact with bloggers by contributing comments in response to specific blog posts. Moreover, readers of blogs are often bloggers themselves and acknowledge their favorite blogs by adding them to their blogrolls or linking to them in their posts. This paper presents a study of bloggers’ online and real life relationships in three blog communities: Kuwait Blogs, Dallas/Fort Worth Blogs, and United Arab Emirates Blogs. Through a comparative analysis of the social network structures created by blogrolls and blog comments, we find different characteristics for different kinds of links. Our online survey of the three communities reveals that few of the blogging interactions reflect close offline relationships, and moreover that many online relationships were formed through blogging. 3. Visualization of the Nordic academic web: Link analysis using social network tools. Jose Luis Ortega and Isidro F. Aguillo, 2007, Information Processing and Management. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.99.9758 Abstract: The aim of this paper is to study the link relationships in the Nordic academic web space – comprised of 23 Finnish, 11 Danish and 28 Swedish academic web domains with the European one. Through social networks analysis we intend to detect sub-networks within the Nordic network, the position and role of the different university web domains and to understand the structural topology of this web space. Co-link analysis, with asymmetrical matrices and cosine measure, is used to identify thematic clusters. Results show that the Nordic network is a cohesive network, set up by three well-defined subnetworks and it rests on the Finnish and Swedish sub-networks. We conclude that the Danish network has less visibility than other Nordic countries. The Swedish one is the principal Nordic sub-network and the Finland network is a slightly isolated from Europe, with the exception of the University of Helsinki. 4. On the Evoluation of User Interaction in Facebook. Bimal Viswanath, Alan Mislove, Meeyoung Cha, and Krishna P. Gummadi, 2009, WOSN '09 Proceedings of the 2nd ACM workshop on Online social networks http://www.mpi-sws.org/~gummadi/papers/wosn23-viswanath.pdf Abstract: Online social networks have become extremely popular; numerous sites allow users to interact and share content using social links. Users of these networks often establish hundreds to even thousands of social links with other users. Recently, researchers have suggested examining the activity network - a network that is based on the actual interaction between users, rather than mere friendship - to distinguish between strong and weak links. While initial studies have led to insights on how an activity network is structurally different from the social network itself, a natural and important aspect of the activity network has been disregarded: the fact that over time social links can grow stronger or weaker. In this paper, we study the evolution of activity between users in the Facebook social network to capture this notion. We find that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, we also find that even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.