Benford's Law applies to word frequency rank in English, German, French, Spanish, and Italian.

Benford's Law states that, in many real-world data sets, the frequency of numbers' first digits is predicted by the formula log(1 + (1/d)). Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to the the frequency rank of words in English, German, French, Spanish, and Italian. We calculated the frequency rank of words in the Google Ngram Viewer corpora. Then, using the first significant digit of the frequency rank, we found the FSD distribution adhered to the expected Benford's Law distribution. Over a series of additional corpora from sources ranging from news to books to social media and across the languages studied, we consistently found adherence to Benford's Law. Furthermore, at the user-level on social media, we found Benford's Law holds for the vast majority of users' collected posts and significant deviations from Benford's Law tends to be a mark of spam bots.

Don't Go Chasing Narcissists: A Relational-Based and Multiverse Perspective on Leader Narcissism and Follower Engagement Using a Machine Learning Approach.

Although research interest in leader narcissism has been on the rise over the past few years, prior literature has predominantly discussed leader narcissism from a leader-centric perspective. In this article, we provide a relational-based perspective of leader narcissism by examining the interaction between follower personality traits and leader narcissism on follower engagement in an online context. We combine a machine learning (ML) approach and multiverse analysis to predict the personality traits of a large sample of leaders and engaged followers across 18 created multiverses and analyze hypothesized interactions using multilevel regressions, also accounting for leader gender moderation effects. We find that the interaction between leader narcissism and follower agreeableness and follower neuroticism positively predicts follower engagement, whereas the interaction between leader narcissism and follower openness negatively predicts follower engagement. In addition, we find that leader gender plays an important moderating role. Limitations and implications are discussed.

Digital spaces and their perils.

A pair of books confront the dark sides of virtual communities.

Social media and shared realityOff the Edge: Flat Earthers, Conspiracy Culture, and Why People Will Believe Anything Kelly Weill Algonquin Books, 2022. 256 pp.

Exacerbated by engagement algorithms, flat Earth theories and other fringe beliefs thrive.

Benford's Law Applies to Online Social Networks.

Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.

Modeling a description logic vocabulary for cancer research.

The National Cancer Institute has developed the NCI Thesaurus, a biomedical vocabulary for cancer research, covering terminology across a wide range of cancer research domains. A major design goal of the NCI Thesaurus is to facilitate translational research. We describe: the features of Ontylog, a description logic used to build NCI Thesaurus; our methodology for enhancing the terminology through collaboration between ontologists and domain experts, and for addressing certain real world challenges arising in modeling the Thesaurus; and finally, we describe the conversion of NCI Thesaurus from Ontylog into Web Ontology Language Lite. Ontylog has proven well suited for constructing big biomedical vocabularies. We have capitalized on the Ontylog constructs Kind and Role in the collaboration process described in this paper to facilitate communication between ontologists and domain experts. The artifacts and processes developed by NCI for collaboration may be useful in other biomedical terminology development efforts.