Automated content analysis of cultural Diversity Perspectives in Annual Reports (DivPAR): Development, validation, and future research agenda

Objective: In this article, we present a digital tool (Diversity Perspectives in Annual Reports [DivPAR]) for automated content analysis of annual reports, designed to identify the presence of three cultural diversity perspectives-the Moral, Market, and Innovation perspectives-based on earlier work by Ely and Thomas (2001). Method: In Study 1, we describe the development and validation of the instrument, through an iterative procedure in which manual annotation of independent subsamples (n = 24, 25) by human coders was compared to the computer coding in subsequent rounds, until sufficient agreement was reached. In Study 2, we illustrate the type of data that the script generates, by analyzing the prevalence of the three perspectives in annual reports of 55 Dutch organizations over a period of 2 decades (1999-2018; n = 937). Results: Our findings confirm that DivPAR is sufficiently reliable for use in future research. In Study 2, we show that among Dutch organizations, the moral perspective is most prevalent, but the market and innovation perspectives are increasing in popularity. Conclusion: DivPAR can be used to analyze the prevalence and longitudinal development of diversity perspectives in organizational communication. It enables scholars to draw comparisons across different sectors, regions, or countries, to study how diversity perspectives correlate with societal developments, and to uncover the (lack of) relationships between diversity communication and diversity outcomes. Directions for future research are discussed at the end of the article. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding the link between audience engagement metrics and the perceived quality of online news using machine learning

This article aims to explain the perceived quality of online news articles. Discovering which elements of a news story influence readers’ perceptions could drive online popularity, which is the paramount factor of digital news readership. This work explores an approach to use tree-based machine learning algorithms to address this problem based on selected characteristics, which measure engagement, drawn from prior research mostly developed by communication scientists. A proposed extended model is used to examine the association between the engagement features and perceived quality concerning all the articles depending mainly on their genre. To demonstrate the capacity of using predictive analytics to facilitate journalistic news writing the proposed methodology is applied on a novel data set with 200K articles in total constructed from a blog site. The results of phase A, indicate interesting correlations between the features and the perceived quality of the articles. In stage B, the paper seeks to extract a set of rules that can be used as guidelines for authors in the writing of their next articles, indicating the probability of popularity that their articles may gain if these rules are taken into consideration.

Reliable or not? An automated classification of webpages about early childhood vaccination using supervised machine learning

OBJECTIVE

To investigate the applicability of supervised machine learning (SML) to classify health-related webpages as 'reliable' or 'unreliable' in an automated way.

METHODS

We collected the textual content of 468 different Dutch webpages about early childhood vaccination. Webpages were manually coded as 'reliable' or 'unreliable' based on their alignment with evidence-based vaccination guidelines. Four SML models were trained on part of the data, whereas the remaining data was used for model testing.

RESULTS

All models appeared to be successful in the automated identification of unreliable (F1 scores: 0.54-0.86) and reliable information (F1 scores: 0.82-0.91). Typical words for unreliable information are 'dr', 'immune system', and 'vaccine damage', whereas 'measles', 'child', and 'immunization rate', were frequent in reliable information. Our best performing model was also successful in terms of out-of-sample prediction, tested on a dataset about HPV vaccination.

CONCLUSION

Automated classification of online content in terms of reliability, using basic classifiers, performs well and is particularly useful to identify reliable information.

PRACTICE IMPLICATIONS

The classifiers can be used as a starting point to develop more complex classifiers, but also warning tools which can help people evaluate the content they encounter online.

3bij3 – Developing a framework for researching recommender systems and their effects

Today’s online news environment is increasingly characterized by personalized news selections, relying on algorithmic solutions for extracting relevant articles and composing an individual’s news diet. Yet, the impact of such recommendation algorithms on how we consume and perceive news is still understudied. We therefore developed one of the first software solutions to conduct studies on effects of news recommender systems in a realistic setting. The web app of our framework (called 3bij3) displays real-time news articles selected by different mechanisms. 3bij3 can be used to conduct large-scale field experiments, in which participants’ use of the site can be tracked over extended periods of time. Compared to previous work, 3bij3 gives researchers control over the recommendation system under study and creates a realistic environment for the participants. It integrates web scraping, different methods to compare and classify news articles, different recommender systems, a web interface for participants, gamification elements, and a user survey to enrich the behavioural measures obtained.

More or less diverse: An assessment of the effect of attention to media salient company types on media agenda diversity in Dutch newspaper coverage between 2007 and 2013

This study on news coverage of highly visible company types in a Dutch daily quality newspaper (NRC Handelsblad; N = 14,363), during the economic crisis (2007-2013), shows that attention to banks (and to a lesser extent also to the automobile and components industry) had a structural negative influence on media agenda diversity. The majority of the other salient company types had a significant positive impact on diversity. These results suggest that banks attracted attention at the expense of more varied, diverse coverage during the crisis. Our findings extend knowledge of agenda-building dynamics in relation to organizational news by considering characteristics of the broader media agenda. We discuss our findings in light of causes and consequences of media coverage of salient businesses.

The SV40 pseudovirus: its potential for general transduction in animal cells

The rates of reassociation of DNA from a monkey cell line (Vero), from SV40 pseudovirus (consisting of Vero DNA and virus protein coat), and from mature SV40 were measured. The results show that the host DNA in the pseudovirus contains repeated and unique sequences in the same proportions as normal host DNA; hence, no one portion of the host genome is preferentially incorporated in the pseudovirus. It was also shown that the Vero DNA in the pseudoviruses enters the nuclei of mouse embryo cells without loss of its physical integrity.