The effect of Participants Interactions on the Sustainability of Online Communities Social media has become an important online social venue where people can connect and communicate with each other. However, despite the increasing value of social media, researchers have noticed that the participants are not necessarily as active as it has been believed. It is also not uncommon that some online communities have not attracted enough participants and turned into xe2x80x9ccyber ghost towns.xe2x80x9d In this paper, we concentrate on investigating the effect of participantsu0027 interactions on the sustainability of online communities. Social network analysis is adopted as the underlying analytical method and used to estimate diverse social network measures as indicators of participantsu0027 interactions for sustainability analysis. Three types of social network indicators are examined. Moreover, Reddit, a leading social news and media aggregation website, is adopted as our data source for empirical evaluation. Some interesting and promising results are identified and discussed.

Tweeting the United Nations Climate Change Conference in Paris (COP21): An analysis of a social network and factors determining the network influence Abstract   To understand the Twitter network of an environmental and political event and to extend the network theory of social capital, we first performed a network analysis of the English tweets during the first 10 days of the United Nationsxe2x80x99 Conference of the Parties in Paris in 2015. Accounts for nonprofit and government agencies were more likely to be influential in the Twitter network and be retweeted, whereas individual accounts were more likely to retweet others. Based on a quota sample of 133 Twitter accounts and using both manual and machine coding, we further found that the number of followers (but not the size of following) and the commongoal frame (i.e., mitigationadaptation) positively predicted an accountu0027s influence in the Twitter network, whereas the conflict frame negatively predicted an accountu0027s network influence.