| Summary: | 碩士 === 國立臺灣大學 === 圖書資訊學研究所 === 106 === Virtual communities have become an important venue for individuals to seek information and find social support. Previous studies indicate that different types of virtual communities have distinct interaction patterns among members, it is therefore inappropriate to treat all virtual communities as the same because of their unique network structures. Specifically, three types of virtual communities have been distinguished: “question and answer”, “discussion” and ” social support”. To examine whether different virtual communities do indeed have different network structure and topologies, three bulletin boards: ”C++”, “MuscleBeach” and “Dog”, were selected from PPT, the largest electronic bulletin board in Taiwan; each represents the aforementioned types of virtual communities.
A new approach to network construction that takes into account the full complexity of the interactive nature of the threaded dialogues within the bulletin boards was adopted. Significant differences were found between the proposed and the traditional network construction method on serveral social network measures. It was also found that the three virtual communities did demonstrate very different interaction patterns and network structure. Furthermore, motivated by social exchange theory, we further explore the network typologies in these virtual communities using Exponential Random Graph Models (ERGMs), which allows us to perform statistical test on the differences in interaction patterns.
The results show that: (1) Contrary to previous research result, the “question and answer community” demonstrates considerable network cohesivenss and resembles the “small world” typology, mainly due to the more elaborate network construction approach we propose. “Direct reciprocity” and “indirect reciprocity” in this type of community were shown to be statistically more prevalent. (2) The “discussion community” is cohesive and without clearly demarcated network boundaries, hence we propose “chance encounter” typology to portray this kind of community. “Indirect reciprocity” and “preferential attachment” were found to occur frequently in it. (3) The ”social support community” demonstrates very clear network clustering, though considerable denser intearctions were found than in the case of the small world structure. It demonstrates the “structurally cohesive” typology where “indirect reciprocity” and “preferential attachment” were more frequently found.
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