This is the first study that presents a full picture of the field by using a combination of two methodologies, bibliometric and social network analysis (SNA). Thus, this work maps the knowledge of ...previous research and suggest new avenues for future research for the relationship between board characteristics and corporate social responsibility (CSR) and CSR disclosure (CSRD). We analysed 242 articles published on Web of Science database (WoS) journals for the period 1992-2019. The results show that board characteristics have a significant impact on CSR literature in terms of citations and high-quality journals. Moreover, the trend of the papers published in the field is increasing in the last five years. Our work clusters the literature according to keywords and draws the primary authors' networks. This study also draws potential future avenues for research in the field in terms of research gaps (governance mechanisms, variables, countries, etc.). Furthermore, our results suggest some potential areas of interest for future political reforms of board of directors' guidelines.
Nowadays, with the increasing complexity of decision-making environment, more and more large-scale group decision making (LGDM) problems are faced. Due to the existence of social network ...relationships among experts, social network analysis (SNA) is proved to be an effective analysis method for LGDM problems. Meanwhile, it is crucial for LGDM issues to determine the weights of decision groups and to lessen the large-scale DMs’ dimension, which will affect the result of decision making directly. This study proposes a clustering- and maximum consensus-based resolution framework with linguistic distribution (LD) for social network large-scale group decision making (SNLGDM) problems. In the consensus framework, independent sub-groups can be obtained by the division of large-scale DMs according to trust relationship using the proposed SNA-based trust network clustering model, and the LD assessments are used to represent the preference relation of sub-groups. Following this, by considering three dependable sources: consistency, similarity, and in-centrality degree, this paper devises a maximum consensus-based method, which can generate the sub-groups’ comprehensive weight by maximizing the level of consensus between sub-groups and the collective matrix. Meanwhile, the final ranking of alternatives can be obtained based on collective preference relation. Conclusively, the availability and advantage of this research are verified through numerical example, coefficient analysis and comparative analysis.
Vaccine hesitancy is a growing concern in public health, with increasing numbers of individuals expressing skepticism or outright refusal to receive vaccines. This factor was significantly ...highlighted during the COVID-19 pandemic, with large populations refusing to take the vaccine and prolonging the pandemic. This paper presents and compares two transformer-based approaches i.e. XLNET and BERT to classify vaccine misinformation on Twitter using the standard COVID-19 ANTi-Vax dataset. Subsequently, an analysis of vaccine discourse on Reddit is carried out following a user association mapping algorithm. The resultant graph was subsequently analyzed. The XLNET model outperformed BERT by showing a high accuracy of 0.9484, with an F1 score of 0.9353. The methodology can be used in multiple other scenarios to address concerns with regard to the usage of social media by analyzing network interactions.
Migration and integration research has been institutionalized over the last few decades. However, an increasing number of voices has been calling for more reflexivity, criticizing the nation-state- ...and ethnicity-centred epistemology that often informs this discipline. Consistently with this line of reasoning, I argue that migration and integration research originates in a historically institutionalized nation-state migration apparatus and is thus entangled with a particular normalization discourse. Therefore, this field of study contributes to reproducing the categories of this particular migration apparatus. This entanglement poses some serious dilemmas for this research tradition, dilemmas that ask for further consideration and possible solutions. My main proposition is to 'de-migranticize' migration and integration research. I outline possible ways of doing so and discuss the consequences of such a strategy for the future of migration and integration studies.
In China, the social risks associated with housing demolition increasingly challenge the success of urban redevelopment projects. In practice, these risks are interacting and are associated with ...various stakeholders. Previous studies have largely focused on risk identification and evaluation without giving sufficient consideration to stakeholders and their linkages with risks. Therefore, we used social network analysis to investigate social risks related to housing demolition, from a stakeholder perspective. Stakeholder-associated risks and their interrelations were investigated based on a literature analysis and interviews with key stakeholders. Using a network analysis we identified critical risks and their corresponding stakeholders. Social security schemes, efficient financial management, multi-dimensional impact assessments, policy analyses and adherence to laws, and public participation were proposed to mitigate risks. The effectiveness of these solutions was quantified based on a network simulation. This study contributes to the body of knowledge on social risk management via linking social risks with stakeholders.
•This study developed a model for managing social risks during the housing demolition stage of urban redevelopment projects.•This study investigated the linkages between social risks and stakeholders.•Critical risks and interactions were identified based on a risk network analysis.•Five strategies were proposed to mitigate social risks and their effectiveness was quantified via network simulation.
Link prediction methods anticipate the likelihood of a future connection between two nodes in a given network. The methods are essential in social networks to infer social interactions or to suggest ...possible friends to the users. Rapid social network growth trigger link prediction analysis to be more challenging especially with the significant advancement in complex social network modeling. Researchers implement numerous applications related to link prediction analysis in different network contexts such as dynamic network, weighted network, heterogeneous network and cross network. However, link prediction applications namely, recommendation system, anomaly detection, influence analysis and community detection become more strenuous due to network diversity, complex and dynamic network contexts. In the past decade, several reviews on link prediction were published to discuss the algorithms, state-of-the-art, applications, challenges and future directions of link prediction research. However, the discussion was limited to physical domains and had less focus on social network perspectives. To reduce the gap of the existing reviews, this paper aims to provide a comprehensive review and discuss link prediction applications in different social network contexts and analyses, focusing on social networks. In this paper, we also present conventional link prediction measures based on previous researches. Furthermore, we introduce various link prediction approaches and address how researchers combined link prediction as a base method to perform other applications in social networks such as recommender systems, community detection, anomaly detection and influence analysis. Finally, we conclude the review with a discussion on recent researches and highlight several future research directions of link prediction in social networks.
This study compares the interaction patterns of a novice and an experienced instructor using Social Network Analysis (SNA) and content analysis and explores how students' interactions, degrees of ...satisfaction, and cognitive presence differ according to the different interaction patterns of the two instructors. Results showed some differences in the interaction characteristics between the sections. First, the experienced instructor was the most powerful actor in the course, while some students in the novice instructor's section showed higher outdegree centrality than the instructor. In addition, the novice instructor's section was a more active network than the experienced instructor's section in which the instructor showed the highest outdegree and indegree and also seemed to have more reciprocal relations. In terms of satisfaction and cognitive presence levels, the students in the experienced instructor's section in which the instructor focused more on triggering events or exploration activities, reported higher satisfaction than the students in the novice instructor's section. However, there was no significant difference in students' cognitive presence levels. A key finding of research suggests that instructors need to balance their participation, stimulate students' curiosity, and encourage brainstorming-rather than directly offering solutions-to improve students' satisfaction in asynchronous discussion-based online learning. This research also indicates that well-designed discussion topics may contribute more to developing students' cognitive presence than the instructor's interaction patterns. Finally, this research highlights the effectiveness of SNA and content analysis to explore instructors' and students' interactions on discussion boards.
The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods ...for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutations. We show that, contrary to accepted wisdom, node-label permutations do not automatically account for the non-independences assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same assumption also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same
p
-values as equivalent parametric regression models, but that in the presence of non-independence, parametric regression models can also produce accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we suggest that standard parametric models could be used in the place of permutation-based methods. Moving away from permutation-based methods could have several benefits, including reducing over-reliance on
p-
values, generating more reliable effect size estimates, and facilitating the adoption of causal inference methods and alternative types of statistical analysis.
Objectives
The project aims to: (1) investigate structural and functional changes in an Australian drug trafficking network across time to determine ways in which such networks form and evolve. To ...meet this aim, the project will answer the following research questions: (1) What social structural changes occur in drug trafficking networks across time? (2) How are these structural changes related to roles/tasks performed by network members? (3) What social processes can account for change over time in drug trafficking networks?
Method
The relational data on the network was divided into four two years periods. Actors were allocated to specific roles. We applied a stochastic actor-oriented model to explain the dynamics of the network across time. Using RSiena, we estimated a number of models with the key objectives of investigating: (1) the effect of roles only; (2) the endogenous effect of degree-based popularity (Matthew effect); (3) the endogenous effect of balancing connectivity with exposure (preference for indirect rather than direct connections); (4) how degree-based popularity is moderated by tendencies towards reach and exposure.
Results
Preferential attachment is completely moderated by a preference for having indirect ties, meaning that centralization is a result of actors preferring indirect connections to many others and not because of a preference for connecting to popular actors. Locally, actors seek cohesive relationships through triadic closure.
Conclusions
Actors do not seek to create an efficient network that is highly centralized at the expense of security. Rather, actors strive to optimize security through triadic closure, building trust, and protecting themselves and actors in close proximity through the use of brokers that offer access to the rest of the network.
PDF
