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There is increasing evidence that indicates how personalising persuasive strategies may increase the effectiveness of persuasive technologies and behaviour change interventions. This has led to a wide range of studies exploring susceptibility to persuasion which highlight the role of individual differences. Measuring susceptibility to persuasion, while accounting for individual differences can be challenging, particularly where persuasive strategies may be considered similar due to their underlying components. In this paper, we present a study exploring susceptibility to Cialdini's principles of persuasion with a focus on how we can distinguish susceptibility measures between the most recently identified Unity principle and Social proof. This study was conducted using an online survey incorporating susceptibility measures to all seven Cialdini principles and a measure of the actual effectiveness of seven corresponding persuasive strategies. Our results indicate that while we are able to distinguish susceptibility measures between Unity and Social proof, together with Commitment, Scarcity and Reciprocity, we were unable to obtain these with susceptibility measures for Liking and Authority.

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The increasing adoption and widespread use of social media provides significant opportunities for social scientists to discover novel insights of varying aspects of human behaviour. In response to increasing interest and research in this area, a wide variety of theoretical, methodological frameworks, guidelines and software tools have emerged. However, tools for collecting and analysing social media data are often inaccessible or unsuitable for social scientists. This is often due to interdisciplinary challenges that conflict with social scientists' research aims, objectives and methodological approaches towards collecting and analysing social media. To address this, we are developing an extensible open source platform to support social scientists' research in this area. This platform provides the means to collect and annotate social media data which can then be used to construct a knowledge graph. The knowledge graph provides social scientists with the means to consider their analysis within a broader context that may yield further insights.