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Mendeley reader counts have been proposed as early indicators for the impact of academic publications. The purpose of this paper is to assess whether there are enough Mendeley readers for research evaluation purposes during the month when an article is first published.

Average Mendeley reader counts were compared to the average Scopus citation counts for 104,520 articles from ten disciplines during the second half of 2016.

Articles attracted, on average, between 0.1 and 0.8 Mendeley readers per article in the month in which they first appeared in Scopus. This is about ten times more than the average Scopus citation count.

Other disciplines may use Mendeley more or less than the ten investigated here. The results are dependent on Scopus’s indexing practices, and Mendeley reader counts can be manipulated and have national and seniority biases.

Mendeley reader counts during the month of publication are more powerful than Scopus citations for comparing the average impacts of groups of documents but are not high enough to differentiate between the impacts of typical individual articles.

This is the first multi-disciplinary and systematic analysis of Mendeley reader counts from the publication month of an article.

The purpose of this paper is to assess the educational value of prestigious and productive Spanish scholarly publishers based on mentions of their books in online scholarly syllabi.

Syllabus mentions of 15,117 books from 27 publishers were searched for, manually checked and compared with Microsoft Academic (MA) citations.

Most books published by Ariel, Síntesis, Tecnos and Cátedra have been mentioned in at least one online syllabus, indicating that their books have consistently high educational value. In contrast, few books published by the most productive publishers were mentioned in online syllabi. Prestigious publishers have both the highest educational impact based on syllabus mentions and the highest research impact based on MA citations.

The results might be different for other publishers. The online syllabus mentions found may be a small fraction of the syllabus mentions of the sampled books.

Authors of Spanish-language social sciences and humanities books should consider general prestige when selecting a publisher if they want educational uptake for their work.

This is the first study assessing book publishers based on syllabus mentions.

The purpose of this paper is to investigate whether machine learning induces gender biases in the sense of results that are more accurate for male authors or for female authors. It also investigates whether training separate male and female variants could improve the accuracy of machine learning for sentiment analysis.

This paper uses ratings-balanced sets of reviews of restaurants and hotels (3 sets) to train algorithms with and without gender selection.

Accuracy is higher on female-authored reviews than on male-authored reviews for all data sets, so applications of sentiment analysis using mixed gender data sets will over represent the opinions of women. Training on same gender data improves performance less than having additional data from both genders.

End users of sentiment analysis should be aware that its small gender biases can affect the conclusions drawn from it and apply correction factors when necessary. Users of systems that incorporate sentiment analysis should be aware that performance will vary by author gender. Developers do not need to create gender-specific algorithms unless they have more training data than their system can cope with.

This is the first demonstration of gender bias in machine learning sentiment analysis.

Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19.

A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10–29, 2020.

The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news.

Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed.

Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues.

This is the first qualitative analysis of general COVID-19-related retweeting.

Toward achieving a better guest experience, the current study aims to use the word frequency comparison technique to evaluate the types of attributes and services that are used most frequently in guests’ five- and one-star reviews on TripAdvisor. The working-paper also aims to investigate the differences between reviews written by men and women.

A combined sentiment and text analysis was applied to 329,849 UK hotel reviews from UK TripAdvisor to identify factors that influence customer satisfaction, including those with gender differences.

The present findings reveal important differences between the male- and female-produced terms. The results show that female travelers pay more attention to the hotel’s core products and their comfort compared to male travelers. In terms of food and beverage, men’s comments tended to focus on pubs, beer and certain types of food. In contrast, women’s comments were more likely to be related to healthy eating, such as homemade, vegan and vegetarian foods, as well as fruits and healthy breakfasts. Women also pay more attention to the soft skills of staff such as friendliness, helpfulness and welcoming messages.

While core attributes of a hotel stay remain crucial for all guests, disparities exist between the language men and women use to describe them. For core products, women pay more attention to the room’s cleanliness, comfort and features such as bed, pillow, blanket, towel, toiletries and decoration, whereas men pay more attention to the layout, size and type of room. Hotels may use gender as a segmentation variable and use these findings in their marketing campaigns.

This is one of the first studies offering insights into the differences between the male and female reactions to and preferences for hotel services at a national level. Following a novel method, this study has listed and ranked attributes and differentiated them based on gender.

Scholars often aim to conduct high quality research and their success is judged primarily by peer reviewers. Research quality is difficult for either group to identify, however and misunderstandings can reduce the efficiency of the scientific enterprise. In response, we use a novel term association strategy to seek quantitative evidence of aspects of research that are associated with high or low quality.

We extracted the words and 2–5-word phrases most strongly associated with different quality scores in each of 34 Units of Assessment (UoAs) in the Research Excellence Framework (REF) 2021. We extracted the terms from 122,331 journal articles 2014–2020 with individual REF2021 quality scores.

The terms associating with high- or low-quality scores vary between fields but relate to writing styles, methods and topics. We show that the first-person writing style strongly associates with higher quality research in many areas because it is the norm for a set of large prestigious journals. We found methods and topics that associate with both high- and low-quality scores. Worryingly, terms associated with educational and qualitative research attract lower quality scores in multiple areas. REF experts may rarely give high scores to qualitative or educational research because the authors tend to be less competent, because it is harder to do world leading research with these themes, or because they do not value them.

This is the first investigation of journal article terms associating with research quality.

There have been many attempts to study the content of the Web, either through human or automatic agents. Describes five different previously used Web survey methodologies, each justifiable in its own right, but presents a simple experiment that demonstrates concrete differences between them. The concept of crawling the Web also bears further inspection, including the scope of the pages to crawl, the method used to access and index each page, and the algorithm for the identification of duplicate pages. The issues involved here will be well‐known to many computer scientists but, with the increasing use of crawlers and search engines in other disciplines, they now require a public discussion in the wider research community. Concludes that any scientific attempt to crawl the Web must make available the parameters under which it is operating so that researchers can, in principle, replicate experiments or be aware of and take into account differences between methodologies. Also introduces a new hybrid random page selection methodology.

The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.

This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females.

Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis.

Only one lexical sentiment analysis algorithm was used.

Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results.

This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.

The purpose of this paper is to analyse popular YouTube science video channels for evidence of attractiveness to a female audience.

The influence of presenter gender and commenter sentiment towards males and females is investigated for 50 YouTube science channels with a combined view-count approaching ten billion. This is cross-referenced with commenter gender as a proxy for audience gender.

The ratio of male to female commenters varies between 1 and 39 to 1, but the low proportions of females seem to be due to the topic or presentation style rather than the gender of the presenter or the attitudes of the commenters. Although male commenters were more hostile to other males than to females, a few posted inappropriate sexual references that may alienate females.

Comments reflect a tiny and biased sample of YouTube science channel viewers and so their analysis provides weak evidence.

Sexist behaviour in YouTube commenting needs to be combatted but the data suggest that gender balance in online science presenters should not be the primary concern of channel owners.

This is the largest scale analysis of gender in YouTube science communication.

Despite the social, educational and therapeutic benefits of book clubs, little is known about which books participants are likely to have read. In response, the purpose of this paper is to investigate the public bookshelves of those that have joined a group within the Goodreads social network site.

Books listed as read by members of 50 large English-language Goodreads groups – with a genre focus or other theme – were compiled by author and title.

Recent and youth-oriented fiction dominate the 50 books most read by book club members, whilst almost half are works of literature frequently taught at the secondary and postsecondary level (literary classics). Whilst J.K. Rowling is almost ubiquitous (at least 63 per cent as frequently listed as other authors in any group, including groups for other genres), most authors, including Shakespeare (15 per cent), Goulding (6 per cent) and Hemmingway (9 per cent), are little read by some groups. Nor are individual recent literary prize winners or works in languages other than English frequently read.

Although these results are derived from a single popular website, knowing more about what book club members are likely to have read should help participants, organisers and moderators. For example, recent literary prize winners might be a good choice, given that few members may have read them.

This is the first large scale study of book group members’ reading patterns. Whilst typical reading is likely to vary by group theme and average age, there seems to be a mainly female canon of about 14 authors and 19 books that Goodreads book club members are likely to have read.