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In his inauguration speech of 1961, John F. Kennedy, the first Catholic and youngest-ever holder of the office of US President, famously exhorted citizens to ‘Ask not what your country can do for you; ask what you can do for your country.’ At the time, few would have interpreted this as a call for open innovation or even citizen crowdsourcing: neither the language nor the architecture then existed for either. But the sentiment he expressed marked the beginning of a campaign of citizen engagement in developing ideas for government. It was, in effect, the first national exhortation for the crowdsourcing of ideas, and Kennedy’s words have subsequently been adapted by Jeff Howe for the modern crowdsourcing context.

Citizen crowdsourcing is now well-established. This chapter sets out to assess how successful it has been as a mechanism for finessing original and meaningful ideas that advance social goals. We look briefly at leading examples of crowdsourcing for social good. We also look at the underlying factors that support it, including the knowledge and input solicited from the crowd; the crowd’s willingness to participate; and the mechanisms through which the crowd can engage. We trace the idea and practice of crowdsourcing back to Socrates in ancient Athens. We look at prosocial behaviour, exploring selected annals of public intellectuals, including Emerson. We examine citizen science as a forerunner of crowdsourcing, then move into the business strategy of open innovation and, finally, we arrive at crowdsourcing for social good in various guises. In conclusion, we explore what has been learned from initiatives that can now be considered current best practice in this area.

The purpose of this paper is to determine to what extent a person’s psychophysiological well-being is affected by misinformation and whether their level of information discernment has any positive or negative effect on the outcome.

Participants (n = 48) were randomly and blindly allocated to one of two groups: control group participants were told a person they were working with was a student; experimental group participants were additionally led to believe that this other participant had extreme religious views. This was both stigmatising and misinforming, as this other person was an actor. Participants completed a pre-screening booklet and a series of tasks. Participants’ cardiovascular responses were measured during the procedure.

Participants with high levels of information discernment, i.e. those who are curious, use multiple sources to verify information, are sceptical about search engine information, are cognisant of the importance of authority and are aware that knowledge changes and is contradictory at times exhibited an adaptive stress response, i.e. healthy psychophysiological outcomes and responded with positive emotions before and after a stressful task.

The findings indicate the potential harmful effects of misinformation and discuss how information literacy or Metaliteracy interventions may address this issue.

The first study to combine the hitherto unrelated theoretical areas of information discernment (a sub-set of information literacy), affective states (positive affect negative affect survey) and stress (challenge and threat cardiovascular measures).

This study applies social identity theory (SIT) to explore the perceptual differences among various stakeholder groups regarding the relevance of social and environmental accounting (SEA), SEA education and mandatory disclosure of SEA.

The study adopts a mixed method applying a qualitative and quantitative approach. In total, 325 structured questionnaires were analyzed quantitatively, using ANOVA and group comparison methods. Responses from 18 interviews were analyzed qualitatively to provide complementary evidence for the quantitative study.

There were significant differences between various stakeholder groups regarding the relevance of SEA practice and SEA education. Regulators were mostly affected by considerations about the external perception of work quality, followed by financiers. Practitioners and shareholders were influenced by the ability of SEA in its current state to affect actual work quality. This possibly indicates that academic qualifications have marginal effects on predicting considerations about SEA compared to social identity.

This is the first application of SIT to SEA research and contributes to the effort to improve SEA within emerging economies, highlighting that a one-size-fits-all approach may be ineffective.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.