Citation contagion: a citation analysis of selected predatory marketing journals

An open automation system for predatory journal detection

The growing number of online open-access journals promotes academic exchanges, but the prevalence of predatory journals is undermining the scholarly reporting process. Data collection, feature extraction, and model prediction are common steps in tools designed to distinguish between legitimate and predatory academic journals and publisher websites. The authors include them in their proposed academic journal predatory checking (AJPC) system based on machine learning methods. The AJPC data collection process extracts 833 blacklists and 1213 whitelists information from websites to be used for identifying words and phrases that might indicate the presence of predatory journals. Feature extraction is used to identify words and terms that help detect predatory websites, and the system's prediction stage uses eight classification algorithms to distinguish between potentially predatory and legitimate journals. We found that enhancing the classification efficiency of the bag of words model and TF-IDF algorithm with diff scores (a measure of differences in specific word frequencies between journals) can assist in identifying predatory journal feature words. Results from performance tests suggest that our system works as well as or better than those currently being used to identify suspect publishers and publications. The open system only provides reference results rather than absolute opinions and accepts user inquiries and feedback to update the system and optimize performance.

Are the works that cite Beall's List accounting journals comparable to those that cite Scopus journals of similar citation impact?

Purpose

Th study evaluates the apparent quality of the 10 Beall's List accounting journals with the highest citation rates by investigating whether the works that cite those journals are comparable to those that cite 11 Scopus journals of similar citation impact.

Design/methodology/approach

The study investigates the characteristics of the works that cited the Beall's List and Scopus journals, 2015–2020, comparing the two groups of citing works by publication type (article, book, etc.), extent of self-citation, inclusion in Beall's List and Scopus, Open Access (OA) status, publisher type, citation impact, and country/region of interest.

Findings

The Beall's List accounting journals tend to be cited in less reputable outlets; they are especially likely to be cited in Beall's List journals and especially unlikely to be cited in Scopus journals. However, other evidence suggests that these journals occupy a distinctive niche. The works that cite Beall's List journals are especially likely to be OA, to be published by universities and other nonprofits, and to focus on lower-income countries. They also have relatively low journal and publisher self-citation rates.

Originality/value

Beall's List accounting journals may be especially useful to scholars who rely on OA journals, who see their local universities as natural publishing partners, and who investigate topics of concern to developing countries. An increase in the number of non-predatory journals that cater to these authors' needs might help resolve the apparent problem of unmet demand for journal space.

Citation patterns between impact-factor and questionable journals

One of the most fundamental issues in academia today is understanding the differences between legitimate and questionable publishing. While decision-makers and managers consider journals indexed in popular citation indexes such as Web of Science or Scopus as legitimate, they use two lists of questionable journals (Beall’s and Cabell’s), one of which has not been updated for a few years, to identify the so-called predatory journals. The main aim of our study is to reveal the contribution of the journals accepted as legitimate by the authorities to the visibility of questionable journals. For this purpose, 65 questionable journals from social sciences and 2338 Web-of-Science-indexed journals that cited these questionable journals were examined in-depth in terms of index coverages, subject categories, impact factors and self-citation patterns. We have analysed 3234 unique cited papers from questionable journals and 5964 unique citing papers (6750 citations of cited papers) from Web of Science journals. We found that 13% of the questionable papers were cited by WoS journals and 37% of the citations were from impact-factor journals. The findings show that neither the impact factor of citing journals nor the size of cited journals is a good predictor of the number of citations to the questionable journals.