Mike Thelwall

Mike Thelwall, Kayvan Kousha, Mahshid Abdoli et al.

National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics. Here we assess whether artificial intelligence (AI) could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the UK Research Excellence Framework 2021, matching a Scopus record 2014-18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, as estimated by the algorithms, but this substantially reduced the number of scores predicted.

Mike Thelwall, Kayvan Kousha, Mahshid Abdoli et al.

This document describes strategies for using Artificial Intelligence (AI) to predict some journal article scores in future research assessment exercises. Five strategies have been assessed.

Kayvan Kousha, Mike Thelwall

This literature review identifies indicators that associate with higher impact or higher quality research from article text (e.g., titles, abstracts, lengths, cited references and readability) or metadata (e.g., the number of authors, international or domestic collaborations, journal impact factors and authors' h-index). This includes studies that used machine learning techniques to predict citation counts or quality scores for journal articles or conference papers. The literature review also includes evidence about the strength of association between bibliometric indicators and quality score rankings from previous UK Research Assessment Exercises (RAEs) and REFs in different subjects and years and similar evidence from other countries (e.g., Australia and Italy). In support of this, the document also surveys studies that used public datasets of citations, social media indictors or open review texts (e.g., Dimensions, OpenCitations, Altmetric.com and Publons) to help predict the scholarly impact of articles. The results of this part of the literature review were used to inform the experiments using machine learning to predict REF journal article quality scores, as reported in the AI experiments report for this project. The literature review also covers technology to automate editorial processes, to provide quality control for papers and reviewers' suggestions, to match reviewers with articles, and to automatically categorise journal articles into fields. Bias and transparency in technology assisted assessment are also discussed.

Kayvan Kousha, Mike Thelwall

This study investigates how Large Language Models (LLMs) are influencing the language of academic papers by tracking 12 LLM-associated terms across six major scholarly databases (Scopus, Web of Science, PubMed, PubMed Central (PMC), Dimensions, and OpenAlex) from 2015 to 2024. Using over 2.4 million PMC open-access publications (2021-July 2025), we also analysed full texts to assess changes in the frequency and co-occurrence of these terms before and after ChatGPT's initial public release. Across databases, delve (+1,500%), underscore (+1,000%), and intricate (+700%) had the largest increases between 2022 and 2024. Growth in LLM-term usage was much higher in STEM fields than in social sciences and arts and humanities. In PMC full texts, the proportion of papers using underscore six or more times increased by over 10,000% from 2022 to 2025, followed by intricate (+5,400%) and meticulous (+2,800%). Nearly half of all 2024 PMC papers using any LLM term also included underscore, compared with only 3%-14% of papers before ChatGPT in 2022. Papers using one LLM term are now much more likely to include other terms. For example, in 2024, underscore strongly correlated with pivotal (0.449) and delve (0.311), compared with very weak associations in 2022 (0.032 and 0.018, respectively). These findings provide the first large-scale evidence based on full-text publications and multiple databases that some LLM-related terms are now being used much more frequently and together. The rapid uptake of LLMs to support scholarly publishing is a welcome development reducing the language barrier to academic publishing for non-English speakers.

Mike Thelwall, Kayvan Kousha

The use of Large Language Models (LLMs) like ChatGPT and DeepSeek for translation and language polishing is a welcome development, reducing the longstanding publishing barrier to non-English speakers. Assessing the uptake of this facility is useful to give insights into changing nature of scientific writing. Although the prevalence of LLM-associated terms has been tracked across science in abstracts and for full text biomedical research, their science-wide prevalence in full texts is unknown. In response, this article investigates an expanded set of 80 potentially LLM-associated terms during 2021-2025 in a science-wide full text collection from the publisher MDPI (1.25 million articles), partly focusing on the 73 journals that published at least 500 articles in 2021. The results demonstrate the increasing prevalence of LLM-associated terms science-wide in full texts to 2024, with some terms declining from 2024 to 2025 and others continuing to increase. LLMs seem to avoid some terms (e.g., thus, moreover) and a few terms have stronger associations with abstracts than full texts (e.g., enhanced) or the opposite (e.g., leveraged). The term family "underscore" had the biggest increase: up to 29-fold. There are substantial differences between journals in the apparent use of LLMs for writing, from lower uptake in the life sciences to higher uptake in social sciences, electronic engineering and environmental science. Fields in which there is currently low uptake may need improved or specialist support, such as for reliably translating complex formulae, before the full benefits of automatic translation can be realised.

Er-Te Zheng, Hui-Zhen Fu, Xiaorui Jiang et al.

Post-publication peer review (PPPR) has emerged as an important supplement to traditional peer review, with social media playing a growing role in publicising potential problems in published research. However, it remains unclear whether social media discussions of retracted articles primarily reflect good practices, such as exposing flaws and acknowledging retraction status, or bad practices, such as overlooking retractions and continuing to disseminate scientific misinformation. In this study, we collected Bluesky posts referencing scholarly articles from Altmetric and retrieved metadata for the referenced articles using OpenAlex. The final dataset included 284 retracted articles with 79 pre-retraction posts and 857 post-retraction posts, 59 retraction notices with 186 posts, and 609,461 non-retracted articles with 1,344,756 posts. We manually coded Bluesky posts discussing retracted articles to identify instances of good and bad practice. The results show that posts demonstrating good practice (89.9%) substantially outnumbered those demonstrating bad practice (10.1%). Posts reflecting good practice also had more user engagement. In the pre-retraction phase, good practice posts constituted a slight minority (43.0%), whereas in the post-retraction phase they were dominant (94.2%). Most negative posts in the pre-retraction phase (90.0%) had good practice while only 17.3% positive posts in the post-retraction phase showed bad practice. Thus, sentiment analysis can be helpful to filter posts that could flag potential flaws before retraction, but it may struggle to accurately identify the spread of misinformation after retraction. More broadly, this study highlights the potential of Bluesky to support responsible scientific communication, public scrutiny, and research integrity.

Mike Thelwall

Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises, appointments and promotion. It is therefore important to investigate whether Large Language Models (LLMs) can play a role in this process. This article assesses which ChatGPT inputs (full text without tables, figures and references; title and abstract; title only) produce better quality score estimates, and the extent to which scores are affected by ChatGPT models and system prompts. The results show that the optimal input is the article title and abstract, with average ChatGPT scores based on these (30 iterations on a dataset of 51 papers) correlating at 0.67 with human scores, the highest ever reported. ChatGPT 4o is slightly better than 3.5-turbo (0.66), and 4o-mini (0.66). The results suggest that article full texts might confuse LLM research quality evaluations, even though complex system instructions for the task are more effective than simple ones. Thus, whilst abstracts contain insufficient information for a thorough assessment of rigour, they may contain strong pointers about originality and significance. Finally, linear regression can be used to convert the model scores into the human scale scores, which is 31% more accurate than guessing.

Kayvan Kousha, Mike Thelwall

Large Language Models (LLMs) have the potential to be used to support research evaluation and have a moderate capability to estimate the research quality of a journal article from its title and abstract. This paper assesses whether there are language-related factors unrelated to the quality of the research that influence ChatGPT's scores. Using a dataset of 99,277 journal articles submitted to the UK-wide Research Excellence Framework (REF) 2021 assessments, we calculated several readability indicators from abstracts and correlated them with ChatGPT scores and departmental REF scores. From the results, linguistic complexity and length were more strongly associated with ChatGPT research quality scores than with REF expert scores in many subject areas. Although cause-and-effect was not tested, these results suggest that ChatGPT may be more likely than human experts to reward linguistic complexity, with a potential bias towards longer and less readable abstracts in many fields. The apparent preference of LLMs for complex language is an undesirable feature for practical applications of LLMs for research quality evaluation, unless solutions can be found.

Mike Thelwall

Large Language Models (LLMs) can be helpful for literature search and summarisation, but retracted articles can confuse them. This article asks three open weights (offline) LLMs whether 161 high profile retracted articles had been retracted, performing a similar check for a benchmark multidisciplinary set of 34,070 non-retracted articles. Based on titles and abstracts, in over 80% of cases the LLMs claimed that a retracted article had not been retracted (GPT OSS 120B: 82%; Gemma 3 27B: 84%; DeepSeek R1 72B: 88%). The reasons given for a correct retraction declaration were often wrong, even if detailed. This confirms that LLMs have little ability to distinguish between valid and retracted studies, unless they are allowed to, and do, check online. For the benchmark test, there were only 55 false retraction claims from 34,070 non-retracted full text articles, and 28 false claims when only the title and abstract were entered, suggesting that there is only a small chance that LLMs discount valid studies. When retractions are erroneously claimed, this does not seem to be due to mistakes in the article. Overall, the results give new reasons to be cautious about LLM claims about academic findings.

Mike Thelwall

Large Language Models (LLMs) like ChatGPT, DeepSeek and Gemini seem to be increasingly used for knowledge discovery, information retrieval, and knowledge summaries, including for academic topics. This can result in users being misled, such as due to hallucinations. These problems may be exacerbated for academic knowledge if LLMs base their answers on journal article abstracts when they lack full text access. To test whether the information content of abstracts can be misleading, full text articles were submitted to the GPT-OSS 120B, an LLM from OpenAI, asking it to assess separately the strength the claims for the main result in the abstract, discussion, and conclusion. Outside the social sciences and humanities, claims tended to be stronger in the abstract and conclusions than the discussion, suggesting that relying on the strength of claims in abstracts would be misleading. Thus, if LLMs ingest abstracts but not full texts, there is a risk that they will be overconfident about the findings and pass it on to users in response to relevant prompts. This is another reason to be cautious about using LLMs for academic-related knowledge discovery and summaries.

Ulf Sandström, Mike Thelwall

Purpose: Despite the importance of peer review for grant funding decisions, academics are often reluctant to conduct it. This can lead to long delays between submission and the final decision as well as the risk of substandard reviews from busy or non-specialist scholars. At least one funder now uses Large Language Models (LLMs) to reduce the reviewing burden but the accuracy of LLMs for scoring grant proposals needs to be assessed. Design/methodology/approach: This article compares scores from a range of medium sized open weights LLMs with peer review scores for a well-researched dataset, the Swedish Medical Council's post-doctoral fellowship applications from 1994. Findings: Whilst the LLM scores correlate moderately between each other (mean Spearman correlation: 0.34), they correlated weakly but positively and mostly statistically significantly with the average expert scores (mean Spearman correlation: 0.22). The highest rank correlation between expert scores and LLMs was 0.33 for Gemma 3 27b based on proposal titles and summaries without their main texts, which is about half (56%) of the correlation between reviewers. Research limitations: The small sample size, old funding call and heterogeneous evaluation criteria all undermine the robustness of the analysis. Practical implications: Despite the ability of LLMs to score grant proposals being quantitatively weaker than that of experts, at least in this special case, they may have role in application triage or tie-breaking. Originality/value: This is the first assessment of the value of LLM scores for funding proposals.

Mike Thelwall

Purpose: Assess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task. Design/methodology/approach: Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework (REF) 2021 to create a research evaluation ChatGPT. This was applied to 51 of my own articles and compared against my own quality judgements. Findings: ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria. Its overall scores have weak correlations with my self-evaluation scores of the same documents (averaging r=0.281 over 15 iterations, with 8 being statistically significantly different from 0). In contrast, the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509. Thus, averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores. The positive correlation may be due to ChatGPT being able to extract the author's significance, rigour, and originality claims from inside each paper. If my weakest articles are removed, then the correlation with average scores (r=0.200) falls below statistical significance, suggesting that ChatGPT struggles to make fine-grained evaluations. Research limitations: The data is self-evaluations of a convenience sample of articles from one academic in one field. Practical implications: Overall, ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks. Research evaluators, including journal editors, should therefore take steps to control its use. Originality/value: This is the first published attempt at post-publication expert review accuracy testing for ChatGPT.

Mike Thelwall

In some areas of computing, natural language processing and information science, progress is made by sharing datasets and challenging the community to design the best algorithm for an associated task. This article introduces a shared dataset of 1446 short texts, each of which describes a research quality score on the UK scale of 1* to 4*. This is a messy collection, with some texts not containing scores and others including invalid scores or strange formats. With this dataset there is also a description of what constitutes a valid score and a "gold standard" of the correct scores for these texts (including missing values). The challenge is to design a prompt for Large Language Models (LLMs) to extract the scores from these texts as accurately as possible. The format for the response should be a number and no other text so there are two aspects to the challenge: ensuring that the LLM returns only a number, and instructing it to deduce the correct number for the text. As part of this, the LLM prompt needs to explain when to return the missing value code, -1, instead of a number when the text does not clearly contain one. The article also provides an example of a simple prompt. The purpose of the challenge is twofold: to get an effective solution to this problem, and to increase understanding of prompt design and LLM capabilities for complex numerical tasks. The initial solution suggested has an accuracy of 72.6%, so the challenge is to beat this.

Mike Thelwall, Xiaorui Jiang, Peter A. Bath

Estimating the quality of published research is important for evaluations of departments, researchers, and job candidates. Citation-based indicators sometimes support these tasks, but do not work for new articles and have low or moderate accuracy. Previous research has shown that ChatGPT can estimate the quality of research articles, with its scores correlating positively with an expert scores proxy in all fields, and often more strongly than citation-based indicators, except for clinical medicine. ChatGPT scores may therefore replace citation-based indicators for some applications. This article investigates the clinical medicine anomaly with the largest dataset yet and a more detailed analysis. The results showed that ChatGPT 4o-mini scores for articles submitted to the UK's Research Excellence Framework (REF) 2021 Unit of Assessment (UoA) 1 Clinical Medicine correlated positively (r=0.134, n=9872) with departmental mean REF scores, against a theoretical maximum correlation of r=0.226. ChatGPT 4o and 3.5 turbo also gave positive correlations. At the departmental level, mean ChatGPT scores correlated more strongly with departmental mean REF scores (r=0.395, n=31). For the 100 journals with the most articles in UoA 1, their mean ChatGPT score correlated strongly with their REF score (r=0.495) but negatively with their citation rate (r=-0.148). Journal and departmental anomalies in these results point to ChatGPT being ineffective at assessing the quality of research in prestigious medical journals or research directly affecting human health, or both. Nevertheless, the results give evidence of ChatGPT's ability to assess research quality overall for Clinical Medicine, where it might replace citation-based indicators for new research.

Kayvan Kousha, Mike Thelwall

Academics and departments are sometimes judged by how their research has benefitted society. For example, the UK Research Excellence Framework (REF) assesses Impact Case Studies (ICS), which are five-page evidence-based claims of societal impacts. This study investigates whether ChatGPT can evaluate societal impact claims and therefore potentially support expert human assessors. For this, various parts of 6,220 public ICS from REF2021 were fed to ChatGPT 4o-mini along with the REF2021 evaluation guidelines, comparing the results with published departmental average ICS scores. The results suggest that the optimal strategy for high correlations with expert scores is to input the title and summary of an ICS but not the remaining text, and to modify the original REF guidelines to encourage a stricter evaluation. The scores generated by this approach correlated positively with departmental average scores in all 34 Units of Assessment (UoAs), with values between 0.18 (Economics and Econometrics) and 0.56 (Psychology, Psychiatry and Neuroscience). At the departmental level, the corresponding correlations were higher, reaching 0.71 for Sport and Exercise Sciences, Leisure and Tourism. Thus, ChatGPT-based ICS evaluations are simple and viable to support or cross-check expert judgments, although their value varies substantially between fields.

Mike Thelwall, Abdullah Yaghi

While previous studies have demonstrated that Large Language Models (LLMs) can predict peer review outcomes to some extent, this paper builds on that by introducing two new contexts and employing a more robust method - averaging multiple ChatGPT scores. The findings that averaging 30 ChatGPT predictions, based on reviewer guidelines and using only the submitted titles and abstracts, failed to predict peer review outcomes for F1000Research (Spearman's rho=0.00). However, it produced mostly weak positive correlations with the quality dimensions of SciPost Physics (rho=0.25 for validity, rho=0.25 for originality, rho=0.20 for significance, and rho = 0.08 for clarity) and a moderate positive correlation for papers from the International Conference on Learning Representations (ICLR) (rho=0.38). Including the full text of articles significantly increased the correlation for ICLR (rho=0.46) and slightly improved it for F1000Research (rho=0.09), while it had variable effects on the four quality dimension correlations for SciPost LaTeX files. The use of chain-of-thought system prompts slightly increased the correlation for F1000Research (rho=0.10), marginally reduced it for ICLR (rho=0.37), and further decreased it for SciPost Physics (rho=0.16 for validity, rho=0.18 for originality, rho=0.18 for significance, and rho=0.05 for clarity). Overall, the results suggest that in some contexts, ChatGPT can produce weak pre-publication quality assessments. However, the effectiveness of these assessments and the optimal strategies for employing them vary considerably across different platforms, journals, and conferences. Additionally, the most suitable inputs for ChatGPT appear to differ depending on the platform.

Mike Thelwall

Although both Google Gemini (1.5 Flash) and ChatGPT (4o and 4o-mini) give research quality evaluation scores that correlate positively with expert scores in nearly all fields, and more strongly that citations in most, it is not known whether this is true for smaller Large Language Models (LLMs). In response, this article assesses Google's Gemma-3-27b-it, a downloadable LLM (60Gb). The results for 104,187 articles show that Gemma-3-27b-it scores correlate positively with an expert research quality score proxy for all 34 Units of Assessment (broad fields) from the UK Research Excellence Framework 2021. The Gemma-3-27b-it correlations have 83.8% of the strength of ChatGPT 4o and 94.7% of the strength of ChatGPT 4o-mini correlations. Differently from the two larger LLMs, the Gemma-3-27b-it correlations do not increase substantially when the scores are averaged across five repetitions, its scores tend to be lower, and its reports are relatively uniform in style. Overall, the results show that research quality score estimation can be conducted by offline LLMs, so this capability is not an emergent property of the largest LLMs. Moreover, score improvement through repetition is not a universal feature of LLMs. In conclusion, although the largest LLMs still have the highest research evaluation score estimation capability, smaller ones can also be used for this task, and this can be helpful for cost saving or when secure offline processing is needed.

Mike Thelwall, Ehsan Mohammadi

Assessing published academic journal articles is a common task for evaluations of departments and individuals. Whilst it is sometimes supported by citation data, Large Language Models (LLMs) may give more useful indications of article quality. Evidence of this capability exists for two of the largest LLM families, ChatGPT and Gemini, and the medium sized LLM Gemma3 27b, but it is unclear whether smaller LLMs and reasoning models have similar abilities. This is important because larger models may be slow and impractical in some situations, and reasoning models may perform differently. Four relevant questions are addressed with Gemma3 variants, Llama4 Scout, Qwen3, Magistral Small and DeepSeek R1, on a dataset of 2,780 medical, health and life science papers in 6 fields, with two different gold standards, one novel. The results suggest that smaller (open weights) and reasoning LLMs have similar performance to ChatGPT 4o-mini and Gemini 2.0 Flash, but that 1b parameters may often, and 4b sometimes, be too few. Moreover, averaging scores from multiple identical queries seems to be a universally successful strategy, and few-shot prompts (four examples) tended to help but the evidence was equivocal. Reasoning models did not have a clear advantage. Overall, the results show, for the first time, that smaller LLMs >4b, including reasoning models, have a substantial capability to score journal articles for research quality, especially if score averaging is used.

Er-Te Zheng, Hui-Zhen Fu, Mike Thelwall et al.

Timely detection of problematic research is essential for safeguarding scientific integrity. To explore whether social media commentary can serve as an early indicator of potentially problematic articles, this study analysed 3,815 tweets referencing 604 retracted articles and 3,373 tweets referencing 668 comparable non-retracted articles. Tweets critical of the articles were identified through both human annotation and large language models (LLMs). Human annotation revealed that 8.3% of retracted articles were associated with at least one critical tweet prior to retraction, compared to only 1.5% of non-retracted articles, highlighting the potential of tweets as early warning signals of retraction. However, critical tweets identified by LLMs (GPT-4o mini, Gemini 2.0 Flash-Lite, and Claude 3.5 Haiku) only partially aligned with human annotation, suggesting that fully automated monitoring of post-publication discourse should be applied with caution. A human-AI collaborative approach may offer a more reliable and scalable alternative, with human expertise helping to filter out tweets critical of issues unrelated to the research integrity of the articles. Overall, this study provides insights into how social media signals, combined with generative AI technologies, may support efforts to strengthen research integrity.

Mike Thelwall

Researchers may be tempted to attract attention through poetic titles for their publications, but would this be mistaken in some fields? Whilst poetic titles are known to be common in medicine, it is not clear whether the practice is widespread elsewhere. This article investigates the prevalence of poetic expressions in journal article titles 1996-2019 in 3.3 million articles from all 27 Scopus broad fields. Expressions were identified by manually checking all phrases with at least 5 words that occurred at least 25 times, finding 149 stock phrases, idioms, sayings, literary allusions, film names and song titles or lyrics. The expressions found are most common in the social sciences and the humanities. They are also relatively common in medicine, but almost absent from engineering and the natural and formal sciences. The differences may reflect the less hierarchical and more varied nature of the social sciences and humanities, where interesting titles may attract an audience. In engineering, natural science and formal science fields, authors should take extra care with poetic expressions, in case their choice is judged inappropriate. This includes interdisciplinary research overlapping these areas. Conversely, reviewers of interdisciplinary research involving the social sciences should be more tolerant of poetic license.

Mike Thelwall, Saheeda Thelwall

Purpose: 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. Design/methodology/approach: A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings: 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. Research limitations/implications: 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. Practical implications: 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. Originality/value: This is the first qualitative analysis of general COVID-19-related retweeting.

Ehsan Mohammadi, Mike Thelwall

Reading academic publications is a key scholarly activity. Scholars accessing and recording academic publications online are producing new types of readership data. These include publisher, repository, and academic social network download statistics as well as online reference manager records. This chapter discusses the use of download and reference manager data for research evaluation and library collection development. The focus is on the validity and application of readership data as an impact indicator for academic publications across different disciplines. Mendeley is particularly promising in this regard, although all data sources are not subjected to rigorous quality control and can be manipulated.

Mike Thelwall

Computer systems need to be able to react to stress in order to perform optimally on some tasks. This article describes TensiStrength, a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation, particularly in the context of transportation. It is slightly more effective than a comparable sentiment analysis program, although their similar performances occur despite differences on almost half of the tweets gathered. The effectiveness of TensiStrength depends on the nature of the tweets classified, with tweets that are rich in stress-related terms being particularly problematic. Although generic machine learning methods can give better performance than TensiStrength overall, they exploit topic-related terms in a way that may be undesirable in practical applications and that may not work as well in more focused contexts. In conclusion, TensiStrength and generic machine learning approaches work well enough to be practical choices for intelligent applications that need to take advantage of stress information, and the decision about which to use depends on the nature of the texts analysed and the purpose of the task.

Patrick Kenekayoro, Kevan Buckley, Mike Thelwall

Using raw hyperlink counts for webometrics research has been shown to be unreliable and researchers have looked for alternatives. One alternative is classifying hyperlinks in a website based on the motivation behind the hyperlink creation. The method used for this type of classification involves manually visiting a webpage and then classifying individual links on the webpage. This is time consuming, making it infeasible for large scale studies. This paper speeds up the classification of hyperlinks in UK academic websites by using a machine learning technique, decision tree induction, to group web pages found in UK academic websites into one of eight categories and then infer the motivation for the creation of a hyperlink in a webpage based on the linking pattern of the category the webpage belongs to.