What Is the Added Value of Negative Links in Online Social Networks?

We investigate the“negative link”feature of social networks that allows users to tag other users as foes or as distrusted in addition to the usual friend and trusted links. To answer the question whether negative links have an added value for an online social network, we investigate the machine learning problem of predicting the negative links of such a network using only the positive links as a basis, with the idea that if this problem can be solved with high accuracy, then the “negative link ” feature is redundant. In doing so, we also present a general methodology for assessing the added value of any new link type in online social networks. Our evaluation is performed on two social networks that allow negative links: The technology news website Slashdot and the product review site Epinions. In experiments with these two datasets, we come to the conclusion that a combination of centrality-based and proximity-based link prediction functions can be used to predict the negative edges in the networks we analyse. We explain this result by an application of the models of preferential attachment and balance theory to our learning problem, and show that the “negative link ” feature has a small but measurable added value for these social networks

The path to success: A study of user behaviour and success criteria in online communities

Maintaining online communities is vital in order to increase and retain their economic and social value. That is why community managers look to gauge the success of their communities by measuring a variety of user behaviour, such as member activity, turnover and interaction. However, such communities vary widely in their purpose, implementation and user demographics, and although many success indicators have been proposed in the literature, we will show that there is no one- ts-all approach to community success: Different success criteria depend on different user behaviour. To demonstrate this, we put together a set of user behaviour features, including many that have been used in the literature as indicators of success, and then we define and predict community success in three different types of online communities: Questions & Answers (Q&A), Healthcare and Emotional Support (Life & Health), and Encyclopaedic Knowledge Creation. The results show that it is feasible to relate community success to specific user behaviour with an accuracy of 0.67–0.93 F1 score and 0.77–1.0 AUC.This research has been conducted with the financial support of Science Foundation Ireland (Grant Number SFI/12/RC/2289) and with data provided by Stack Exchange, Boards.ie and Wikipedia.non-peer-reviewe

The path to success: A study of user behaviour and success criteria in online communities

Maintaining online communities is vital in order to increase and retain their economic and social value. That is why community managers look to gauge the success of their communities by measuring a variety of user behaviour, such as member activity, turnover and interaction. However, such communities vary widely in their purpose, implementation and user demographics, and although many success indicators have been proposed in the literature, we will show that there is no one- ts-all approach to community success: Different success criteria depend on different user behaviour. To demonstrate this, we put together a set of user behaviour features, including many that have been used in the literature as indicators of success, and then we define and predict community success in three different types of online communities: Questions & Answers (Q&A), Healthcare and Emotional Support (Life & Health), and Encyclopaedic Knowledge Creation. The results show that it is feasible to relate community success to specific user behaviour with an accuracy of 0.67–0.93 F1 score and 0.77–1.0 AUC.This research has been conducted with the financial support of Science Foundation Ireland (Grant Number SFI/12/RC/2289) and with data provided by Stack Exchange, Boards.ie and Wikipedia