We examine the relationship between leader grandiose narcissism, composed of admiration and rivalry, and corporate fundraising success in a sample of 2377 organizational leaders. To examine a large sample of leaders, we applied a machine-learning algorithm to predict leaders' personality scores based on leaders' Twitter profiles. We found that admiration was positively related to - while rivalry was negatively related to corporate fundraising success (in '000s). Analyses also showed that leader gender does not moderate this relationship, unlike initially expected. We discuss and compare our findings to previous work on narcissism and crowdfunding.
Purpose
The purpose of this paper is to present the evolution in notions from bibliometrics to altmetrics and confront them taking into consideration specific criteria. The objective of this paper is to present the evolution of research, regarding the above fields, the study of metrics and indicators used, and the strength and weaknesses resulting from the current literature. Furthermore, the authors present the manipulation techniques for both fields as their main weakness, as well as further key points, analyzing the alternative options of bibliometrics and altmetrics.
Design/methodology/approach
First, the authors present the evolution of the literature, concerning the specific field and metrics used, following with a brief description of basic indicators related to the field of bibliometrics (journal impact factor (JIF), eigenfactor, article influence score and h-index) discussing their advantages and disadvantages. In the second part, the authors describe altmetrics and present the differences with bibliometrics.
Findings
Both bibliometrics and altmetrics remain weak indicators as fraught with disadvantages with manipulation being the greatest of all. Nevertheless, the combination of the two is proposed in order to export safer conclusions on assessing the impact. Regarding the manipulation there is yet not a clean technique to eliminate manipulation. In specific, regarding bibliometrics, the manipulation of indicators refers only to the human factor intervention. The theoretical implication of this study constitutes of collecting the relevant literature regarding scientific indicators.
Research limitations/implications
We must consider the study of new indicators, which combine metrics and methodologies used in both bibliometrics and altmetrics. The theoretical implication of this study constitutes of collecting the relevant literature regarding scientific indicators. Therefore, researchers are encouraged to test the proposed propositions further.
Practical implications
The practical contribution, on the other side, provides scholars with the knowledge of how making their work more accessible, increasing their impact.
Originality/value
The authors add to the originality by providing a framework of the relevant literature for bibliometrics and altmetrics for future researchers. The authors describe altmetrics and present the differences with bibliometrics. The authors conclude the research with the implications of the conducted analysis and the potential directions for future research. Regarding manipulation, the authors provide with the techniques so researchers are aware of the methods in order to protect their academic profile.
Due to their confidence and dominance, narcissistic leaders oftentimes can be perceived favorably by followers, in particular during times of uncertainty. In this study, we propose and examine the relationship between narcissistic leaders and followers who are prone to experience uncertainty intensely and frequently in general, namely highly anxious followers. We do so by applying machine learning algorithms to account for personality traits in a large sample of leaders and followers on Twitter. We find that highly anxious followers are more likely to interact with narcissistic leaders in general, and male narcissistic leaders in particular. Finally, we also examined these interactions in the context of highly popular leaders and found that as leaders become more popular, they begin to attract less anxious followers, regardless of leader gender. We interpret and discuss these findings in relation to previous work and outline limitations and future research recommendations based on our approach.
Users in Online Social Networks (OSNs,) leave traces that reflect their personality characteristics. The study of these traces is important for several fields, such as social science, psychology, marketing, and others. Despite a marked increase in research on personality prediction based on online behavior, the focus has been heavily on individual personality traits, and by doing so, largely neglects relational facets of personality. This study aims to address this gap by providing a prediction model for holistic personality profiling in OSNs that includes socio-relational traits (attachment orientations) in combination with standard personality traits. Specifically, we first designed a feature engineering methodology that extracts a wide range of features (accounting for behavior, language, and emotions) from the OSN accounts of users. Subsequently, we designed a machine learning model that predicts trait scores of users based on the extracted features. The proposed model architecture is inspired by characteristics embedded in psychology; i.e, it utilizes interrelations among personality facets and leads to increased accuracy in comparison with other state-of-the-art approaches. To demonstrate the usefulness of this approach, we applied our model on two datasets, namely regular OSN users and opinion leaders on social media, and contrast both samples’ psychological profiles. Our findings demonstrate that the two groups can be clearly separated by focusing on both Big Five personality traits and attachment orientations. The presented research provides a promising avenue for future research on OSN user characterization and classification.
Fake news spreading is strongly connected with the human involvement as individuals tend to fall, adopt and circulate misinformation stories. Until recently, the role of human characteristics in fake news diffusion, in order to deeply understand and fight misinformation patterns, has not been explored to the full extent. This paper suggests a human-centric approach on detecting fake news spreading behavior by building an explainable fake-news-spreader classifier based on psychological and behavioral cues of individuals. Our model achieves promising classification results while offering explanations of human motives and features behind fake news spreading behavior. Moreover, to the best of our knowledge, this is the first study that aims at providing a fully explainable setup that evaluates fake news spreading based on users credibility applied to public discussions aiming to a comprehensive way to combat fake news through human involvement.
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