Online Information Review最新文献

{"title":"Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter","authors":"Lingshu Hu","doi":"10.1108/oir-10-2022-0545","DOIUrl":"https://doi.org/10.1108/oir-10-2022-0545","url":null,"abstract":"PurposeThis study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.Design/methodology/approachThis study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.FindingsThrough the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.Practical implicationsThis study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.Social implicationsThis study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.Originality/valueThis study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.","PeriodicalId":503252,"journal":{"name":"Online Information Review","volume":"27 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139523159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predatory journals in dermatology: a bibliometric review","authors":"Amrollah Shamsi, Ting Wang, Narayanaswamy Vasantha Raju, A. Ghamgosar, Golbarg Mahdizadeh Davani, Mohammad Javad Mansourzadeh","doi":"10.1108/oir-04-2023-0161","DOIUrl":"https://doi.org/10.1108/oir-04-2023-0161","url":null,"abstract":"PurposeBy distorting the peer review process, predatory journals lure researchers and collect article processing charges (APCs) to earn income, thereby threatening clinical decisions. This study aims to identifying the characteristics of predatory publishing in the dermatology literature.Design/methodology/approachThe authors used Kscien's list to detect dermatology-related predatory journals. Bibliometric parameters were analyzed at the level of journals, publishers, documents and authors.FindingsSixty-one potential predatory dermatology publishers published 4,164 articles in 57 journals from 2000 to 2020, with most publishers claiming to be located in the United States. Most journals were 1–5 years old. Six journals were indexed in PubMed, two in Scopus and 43 in Google Scholar (GS). The average APC was 1,049 USD. Skin, patient, cutaneous, psoriasis, dermatitis and acne were the most frequently used keywords in the article's title. A total of 1,146 articles in GS received 4,725 citations. More than half of the journals had <10 citations. Also, 318 articles in Web of Science were contaminated by the most cited articles and 4.49% of the articles had reported their funding source. The average number of authors per article was 3.7. India, the United States and Japan had the most articles from 119 involved countries. Asia, Europe and North America had the most contributed authors; 5.2% of articles were written through international collaboration. A majority of authors were from high- and low-middle-income countries. Women contributed 43.57% and 39.66% as the first and corresponding authors, respectively.Research limitations/implicationsThe study had limitations, including heavy reliance on Kscien's list, potential for human error in manual data extraction and nonseparation of types of articles. Journals that only published dermatology articles were reviewed, so those occasionally publishing dermatology articles were missed. Predatory journals covering multiple subjects (Petrisor, 2016) may have resulted in overlooking some dermatology papers. This study did not claim to have covered all articles in predatory dermatology journals (PDJs) but evaluated many of them. The authors accept the claim that Kscien's list may have made a mistake in including journals.Originality/valueThe wide dispersion of authors involved in PDJs highlights the need to increase awareness among these authors.","PeriodicalId":503252,"journal":{"name":"Online Information Review","volume":"39 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139379963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TAI: a lightweight network for content-based fake news detection","authors":"Na Ye, Dingguo Yu, Xiaoyu Ma, Yijie Zhou, Yanqin Yan","doi":"10.1108/oir-11-2022-0629","DOIUrl":"https://doi.org/10.1108/oir-11-2022-0629","url":null,"abstract":"PurposeFake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.Design/methodology/approachThe authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.FindingsThe authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.Originality/valueThe authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.","PeriodicalId":503252,"journal":{"name":"Online Information Review","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139379921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fight against hair loss together: exploring self-disclosure and social support in an online hair loss support community","authors":"Zizhong Zhang","doi":"10.1108/oir-07-2023-0346","DOIUrl":"https://doi.org/10.1108/oir-07-2023-0346","url":null,"abstract":"Purpose Hair loss is often overlooked but psychologically challenging. However, the emergence of online health communities provides opportunities for hair loss patients to seek social support through self-disclosure. Nevertheless, not all disclosures receive the desired support. This research explores what patients disclose within the community and how their health narrative (content, form and linguistic style) regarding self-disclosure influences the social support they receive.Design/methodology/approachThis study investigated a 13-year-old online support group for Chinese hair loss patients with nearly 240,000 members. Using structural topic modeling, Linguistic Inquiry and Word Count, and a negative binomial model, the research analyzed the content of self-disclosure and the interrelationships between social support and three narrative dimensions of self-disclosure.FindingsSelf-disclosures are classified into 14 topics, grouped under analytical, informative and emotional categories. Emotion-related self-disclosures, whether in content or effective word use, receive deeper social support. Longer and image-rich posts attract more support in quantity, but not necessarily in quality, while cognitive words have a limited impact.Originality/valueThis study addresses the previously overlooked population of hair loss patients within online health communities. It employs a more comprehensive health narrative framework to explore the relationship between self-disclosure and social support, utilizing unsupervised structural topic modeling methods to mine text. The research offers practical implications for how patients seek support and for healthcare professionals in developing doctor-patient communication strategies.","PeriodicalId":503252,"journal":{"name":"Online Information Review","volume":"13 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geographical and gender inequalities in health sciences studies: testing differences in research productivity, impact and visibility","authors":"M. Goyanes, Márton Demeter, Gergő Háló, Carlos Arcila-Calderón, Homero Gil de Zúñiga","doi":"10.1108/oir-10-2022-0541","DOIUrl":"https://doi.org/10.1108/oir-10-2022-0541","url":null,"abstract":"PurposeGender and geographical imbalance in production and impact levels is a pressing issue in global knowledge production. Within Health Sciences, while some studies found stark gender and geographical biases and inequalities, others found little empirical evidence of this marginalization. The purpose of the study is to clear the ambiguity concerning the topic.Design/methodology/approachBased on a comprehensive and systematic analysis of Health Sciences research data downloaded from the Scival (Scopus/Scimago) database from 2017 to 2020 (n = 7,990), this study first compares gender representation in research productivity, as well as differences in terms of citation per document, citations per document view and view per document scores according to geographical location. Additionally, the study clarifies whether there is a geographic bias in productivity and impact measures (i.e. citation per document, citations per document view and view per document) moderated by gender.FindingsResults indicate that gender inequalities in productivity are systematic at the overall disciplinary, as well as the subfield levels. Findings also suggest statistically significant geographical differences in citation per document, citations per document view, and view per document scores, and interaction effect of gender over the relation between geography and (1) the number of citations per view and (2) the number of views per document.Originality/valueThis study contributes to scientometric studies in health sciences by providing insightful findings about the geographical and gender bias in productivity and impact across world regions.","PeriodicalId":503252,"journal":{"name":"Online Information Review","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}