Small states, great power? Gaining influence through intrinsic, derivative, and collective power

In recent years, scholars have devoted increased attention to the agency of small states in International Relations. However, the conventional wisdom remains that while not completely powerful, small states are unlikely to achieve much of significance when faced by great power opposition. This argument, however, implicitly rests on resource-based and compulsory understandings of power. This article explores the implicit connections between the concept of "small state" and diverse concepts of power, asking how we should understand these states' attempts to gain influence and achieve their international political objectives. By connecting the study of small states with additional understandings of power, the article elaborates the broader avenues for influence that are open to many states but are particularly relevant for small states. The article argues that small states' power can be best understood as originating in three categories: “derivative,” collective, and particular-intrinsic. Derivative power, coined by Michael Handel, relies upon the relationship with a great power. Collective power involves building coalitions of supportive states, often through institutions. Particular-intrinsic power relies on the assets of the small state trying to do the influencing. Small states specialize in the bases and means of these types of power, which may have unconventional compulsory, institutional, structural, and productive aspects.

In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.